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Background  Improving feed efficiency is one of the important breeding targets for poultry industry. The aim of current study was to investigate the breast muscle transcriptome data of native chickens divergent for feed efficiency. Residual feed intake (RFI) value was calculated for 1008 closely related chickens. The 5 most efficient (LRFI) and 5 least efficient (HRFI) birds were selected for further analysis. Transcriptomic data were generated from breast muscle collected post-slaughter.    Results  The differently expressed genes (DEGs) analysis showed that 24 and 325 known genes were significantly up- and down-regulated in LRFI birds. An enrichment analysis of DEGs showed that the genes and pathways related to inflammatory response and immune response were up-regulated in HRFI chickens. Moreover, Gene Set Enrichment Analysis (GSEA) was also employed, which indicated that LRFI chickens increased expression of genes related to mitochondrial function. Furthermore, protein network interaction and function analyses revealed  ND2 ,  ND4 ,  CYTB ,  RAC2 ,  VCAM1 ,  CTSS  and  TLR4  were key genes for feed efficiency. And the ‘phagosome’, ‘cell adhesion molecules (CAMs)’, ‘citrate cycle (TCA cycle)’ and ‘oxidative phosphorylation’ were key pathways contributing to the difference in feed efficiency.    Conclusions  In summary, a series of key genes and pathways were identified via bioinformatics analysis. These key genes may influence feed efficiency through deep involvement in ROS production and inflammatory response. Our results suggested that LRFI chickens may synthesize ATP more efficiently and control reactive oxygen species (ROS) production more strictly by enhancing the mitochondrial function in skeletal muscle compared with HRFI chickens. These findings provide some clues for understanding the molecular mechanism of feed efficiency in birds and will be a useful reference data for native chicken breeding.

Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis