Network and systems biology approaches help investigate gene regulatory interactions between Salmonella disease and host in chickens: Model-based in silico evidence combined with gene expression assays.

BACKGROUND

Salmonella enteritidis (SE), a previously widespread infectious disease, is still cited as a major factor in economic losses in commercial chicken production. The host's genetic immune system determines the pathogenicity of a particular bacterium. To shed light on this topic, it was necessary to understand the key candidate genes essential for regulating susceptibility and resistance to the target disease. The field of poultry farming in particular has benefited greatly from the connection between quantitative and molecular genetics.

OBJECTIVES

This study aims to identify the most important immune-related genes and their signalling pathways (gene ontology, co-expression and interactions) and to analyse their accumulation in host-resistant SE diseases by combining gene expression assays with model-based in silico evidence.

METHODS

A two-step experimental design is followed. To start, we used free computational tools and online bioinformatics resources, including predicting gene function using a multiple association network integration algorithm (geneMania), the Kyoto Encyclopedia of Genes and Genomes, the Annotation, Visualization and Integrated Discovery (DAVID) database and the stimulator of interferon genes. Natural resistance-associated macrophage protein 1 (NRAMP1), Toll-like receptor 4 (TLR4), interferon-γ (IFNγ), immunoglobulin Y (IgY) and interleukin 8 (IL8) were among the five genes whose expression levels in liver, spleen, and cecum were evaluated at 1107 SE after 48 h of inoculation. This molecular study was developed in the second phase of research to validate the in silico observations. Next, we use five promising biomarkers for relative real-time polymerase chain reaction (PCR) quantification: TLR4, IL8, NRAMP1, IFNγ and IgY genes in two case and control assays. The 2 Livak and Schmittgen method was used to compare the expression of genes in treated and untreated samples. This method normalizes the expression of the target gene to that of actin, an internal control and estimates the change in expression relative to the untreated control. Internal control was provided by the Beta actin gene. Next, statistically, the postdoc test was used for the evaluation of treatments using SAS version 9.4, and p values of 0.05 and 0.01 were chosen for significant level.

RESULTS

Interestingly, the results of our study suggest the involvement of various factors in the host immune response to Salmonella. These include inducible nitric oxide synthase, NRAMP1, immunoglobulin light chain (IgL), transforming growth factor B family (TGFb2, TGFb3 and TGFb4), interleukin 2 (IL2), apoptosis inhibitor protein 1 (IAP1), TLR4, myeloid differentiation protein 2 (MD2), IFNγ, caspase 1 (CASP1), lipopolysaccharide-induced tumour necrosis factor (LITAF), cluster of differentiation 28 (CD28) and prosaposin (PSAP). The summary of gene ontology and related genes found for SE resistance was surprisingly comprehensive and covered the following topics: positive regulation of endopeptidase activity, interleukin-8 production, chemokine production, interferon-gamma production, interleukin-6 production, positive regulation of mononuclear cell proliferation and response to interferon-gamma. The role of these promising biomarkers in our networks against SE susceptibility is essentially confirmed by these results. After 48 h, the spleen showed significant expression of the tissue-specific gene expression patterns for NRAMP1 and IL8 in the cecum, spleen and liver. Based on this information, this report searches for resistance and susceptibility lineages in most genomic regions for SE.

CONCLUSIONS

In conclusion, the development of an appropriate selection program to improve resistance to salmonellosis can be facilitated by a comprehensive understanding of the immune responses of the chicken immune system after SE exposure.