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. 2021 Jan 12;11(1):1623.
doi: 10.1038/s41598-020-79005-7.

Quantitative trait loci and transcriptome signatures associated with avian heritable resistance to Campylobacter

Androniki Psifidi  1   2 , Andreas Kranis  3   4 , Lisa M Rothwell  3 , Abi Bremner  3 , Kay Russell  3 , Diego Robledo  3 , Stephen J Bush  3   5 , Mark Fife  4   6 , Paul M Hocking  3 , Georgios Banos  3   7 , David A Hume  3   8 , Jim Kaufman  9   10 , Richard A Bailey  4 , Santiago Avendano  4 , Kellie A Watson  3 , Pete Kaiser  3 , Mark P Stevens  3
Affiliations

Quantitative trait loci and transcriptome signatures associated with avian heritable resistance to Campylobacter

Androniki Psifidi et al. Sci Rep. .

Abstract

Campylobacter is the leading cause of bacterial foodborne gastroenteritis worldwide. Handling or consumption of contaminated poultry meat is a key risk factor for human campylobacteriosis. One potential control strategy is to select poultry with increased resistance to Campylobacter. We associated high-density genome-wide genotypes (600K single nucleotide polymorphisms) of 3000 commercial broilers with Campylobacter load in their caeca. Trait heritability was modest but significant (h2 = 0.11 ± 0.03). Results confirmed quantitative trait loci (QTL) on chromosomes 14 and 16 previously identified in inbred chicken lines, and detected two additional QTLs on chromosomes 19 and 26. RNA-Seq analysis of broilers at the extremes of colonisation phenotype identified differentially transcribed genes within the QTL on chromosome 16 and proximal to the major histocompatibility complex (MHC) locus. We identified strong cis-QTLs located within MHC suggesting the presence of cis-acting variation in MHC class I and II and BG genes. Pathway and network analyses implicated cooperative functional pathways and networks in colonisation, including those related to antigen presentation, innate and adaptive immune responses, calcium, and renin-angiotensin signalling. While co-selection for enhanced resistance and other breeding goals is feasible, the frequency of resistance-associated alleles was high in the population studied and non-genetic factors significantly influenced Campylobacter colonisation.

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Conflict of interest statement

AK, RB, MF and SA are employed by Aviagen Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Manhattan plots and Q–Q plots displaying the GWAS results for chicken Campylobacter colonisation resistance using the 50K (A) and the imputed 600K (B) HD arrays. (i) Genomic location is plotted against – log10(P) in the Manhattan plot. Genome-wide (P < 0.05) and suggestive genome-wide thresholds are shown as red and blue lines, respectively. (ii) Q–Q plot of observed P values against the expected P values for Campylobacter caecal load of (log-transformed CFU of Campylobacter per gram of caeca content).
Figure 2
Figure 2
Differential expression analysis results. Differential expression of genes in chickens with different (low, average and high) Campylobacter colonisation levels. Each column represents relative gene expression levels in the caecal tonsils of chickens. Expression level is shown as log2 fold change in gene expression in caecal tonsils from chickens colonised at low and average levels relative to expression in chickens colonised at a high level.
Figure 3
Figure 3
Expression QTL analysis results. Boxplots showing the expression of BG1 (4A), TMEM11 (4B) and COPS3 (4C) genes depending on the genotypes of SNPs acting as cis-elements. On the x-axis, "0", "1" and "2" represent the number of copies of the non-reference allele, and on the y-axis the expression of each gene (in Transcripts Per Million, TPM), is represented after mean-centering and scaling.
Figure 4
Figure 4
Allele specific expression (ASE) analysis results. Bar plots showing the allele specific expression results for BF2 (5A), BLB1 (5B) and BLB2 (5C) genes. Each column represents gene expression levels, measured as read counts, for each allele (reference (red) vs non-reference (green) allele). Gene expression levels have been plotted against each individual animal.
Figure 5
Figure 5
Pathway analysis using the IPA software (https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/). The most highly represented canonical pathways derived from genes located within the candidate regions for Campylobacter colonisation resistance in commercial chickens are shown. The solid yellow line represents the significance threshold. The line joining squares represents the ratio of the genes represented within each pathway to the total number of genes in the pathway.
Figure 6
Figure 6
Network analysis using the IPA software (https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/). Three networks, related to immunological disease (A), cell death and survival (B), and molecular transport and protein trafficking (C) are shown that illustrate the molecular interactions between the products of candidate genes selected from QTL regions for Campylobacter colonisation resistance in commercial chickens. Arrows with solid lines represent direct interactions and arrows with broken lines represent indirect interactions. Genes with white labels are those added to the IPA analysis because of their interaction with the target gene products.
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