Long noncoding RNAs and mRNAs profiling in ovary during laying and broodiness in Taihe Black-Bone Silky Fowls (Gallus gallus domesticus Brisson)

Abstract

AbstractBroodiness is one of the important factors affecting poultry egg production, with the black-bone Silky (Gallus gallus domesticus Brisson) exhibiting strong broodiness behavior. Changes in ovarian signaling can offer insights into the mechanisms influencing broodiness. However, research comparing the characteristics of long non- coding RNAs (lncRNAs) in the ovaries during the broody chickens (BC) and high egg- laying chickens (GC) groups of poultry is still limited. In this study, we employed RNA-seq to analyze the ovarian transcriptomes (lncRNAs and mRNAs) of eight brooding and high egg-laying Taihe Black-Bone Silky Fowls (TBsf). We identified a total of 16,444 mRNAs and 18,756 lncRNAs, among which 349 mRNAs and 651 lncRNAs (P < 0.05) showed significantly different expression (DE) between the BC and GC groups. Additionally, we identified the cis-regulated and trans-regulated target genes of differentially abundant lncRNA transcripts and constructed an lncRNA- mRNA trans-regulated interaction network associated with ovarian follicle development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analyses revealed that DE mRNAs and target genes of DE lncRNAs were associated with neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, Cytokine-cytokine receptor interaction, and ECM-receptor interaction pathways. Our study provides a catalogue of lncRNAs and mRNAs related to ovarian development and constructs a predicted interaction network of differentially abundant lncRNAs and Differentially Expressed Genes (DEGs) in TBsf, thus enhancing our understanding of the interactions between lncRNAs and genes regulating brooding behavior.Author summaryIn this study, we performed transcriptome sequencing analysis of the ovaries from BC and GC groups in TBsf. A total of 349 significantly differentially expressed mRNAs and 651 significantly differentially expressed lncRNAs were detected. Furthermore, we identified 10 key genes that may affect reproductive performance in chickens: STC1, MMP13, IL8, AT2, FSHB, RARB, THOC7, FGF12, EPHA1, and NPY5R. Based on GO and KEGG enrichment analyses, we preliminarily explored five pathways that may influence egg production performance: neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, cytokine-cytokine receptor interaction and ECM-receptor interaction. These genes and pathways may play crucial roles in improving broodiness behavior. This study establishes a foundation for subsequent screening of candidate functional genes and functional validation of important production traits in TBsf, providing a theoretical basis for further molecular breeding and enhancing egg-laying performance in TBsf.