Identification of key transcription factors and their functional role involved in Salmonella typhimurium infection in chicken using integrated transcriptome analysis and bioinformatics approach

Abstract

Salmonella enterica serovar typhimurium is the cause of significant morbidity and mortality worldwide that causes economic losses to poultry and is able to cause infection in humans. Indigenous chicken breeds are a potential source of animal protein and have the added advantage of being disease resistant. An indigenous chicken, Kashmir favorella and commercial broiler were selected for understanding the mechanism of disease resistance. Following infection in Kashmir favorella, three differentially expressed genes Nuclear Factor Kappa B (NF-κB1), Forkhead Box Protein O3 (FOXO3) and Paired box 5 (Pax5) were identified. FOXO3, a transcriptional activator, is the potential marker of host resistance in Salmonella infection. NF-κB1 is an inducible transcription factor which lays the foundation for studying gene network of the innate immune response of Salmonella infection in chicken. Pax5 is essential for differentiation of pre-B cells into mature B cell. The real time PCR analysis showed that in response to Salmonella Typhimurium infection a remarkable increase of NF-κB1 (P˂0.01)_, FOXO3 (P˂0.01) gene expression in liver and Pax5 (P˂0.01) gene expression in spleen of Kashmir favorella was observed. The protein-protein interaction (PPI) and protein-TF interaction network by STRINGDB analysis suggests that FOXO3 is a hub gene in the network and is closely related to Salmonella infection along with NF-κB1. All the three differentially expressed genes (NF-κB1, FOXO3 and PaX5) showed their influence on 12 interacting proteins and 16 TFs, where cyclic adenosine monophosphate Response Element Binding protein (CREBBP), erythroblast transformation-specific (ETSI), Tumour-protein 53(TP53I), IKKBK, lymphoid enhancer-binding factor-1 (LEF1), and interferon regulatory factor-4 (IRF4) play role in immune responses. This study shall pave the way for newer strategies for treatment and prevention of Salmonella infection and may help in increasing the innate disease resistance.

Keywords: Bioinformatics; Chicken; RNA seq; Salmonella typhimurium; Transcription factors.

Fig. 1

Clinical symptoms and gross pathology following Salmonella Typhimurium infection (a) Vent paste (b) Ruffled feathers (c)Intestinal haemorrhages (d) Bronze discoloration of liver (e) Elevated white nodular lesions on ventricles (f) Prominent necrotic foci on liver (g) Hepatomegaly

Fig. 2

Proportionate distribution of gross lesions in commercial broiler and Kashmir favorella observed in case of experimentally induced Salmonella Typhimurium infection

Fig. 3

Recovery of Salmonella Typhimurium from the caecum, liver and spleen at day 5 post inoculation. The bacterial load is shown as mean log10 CFU/gram

Fig. 4

2D-Hierarchical clustering of top DEGs in spleen samples

Fig. 5

Protein–Protein and Protein-TF interaction network. Pentagon represents TFs, green solid-circles represent interacting proteins and red solid-circles represent TFs of interest in this study. The nodes were selected with degree > 5 and betweenness > 5 threshold

Fig. 6

Real time PCR validation of differentially expressed genes. The y-axis represents the log2 fold change DEGs; the x-axis shows the gene names employed for validation. RNA seq:*P < 0.01; RT-qPCR: **P < 0.01