Transcriptomic analysis of caecal tissue in inbred chicken lines that exhibit heritable differences in resistance to Campylobacter jejuni

doi:10.21203/rs.3.rs-198715/v1

Download PDF

Research Article

Transcriptomic analysis of caecal tissue in inbred chicken lines that exhibit heritable differences in resistance to Campylobacter jejuni

https://doi.org/10.21203/rs.3.rs-198715/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Campylobacter jejuni is the leading cause of bacterial gastroenteritis in humans and the handling or consumption of contaminated poultry meat is a key source of infection. Selective breeding of poultry that exhibit elevated resistance to Campylobacter is an attractive control strategy. Here we studied the global transcriptional response of inbred chicken lines that differ in resistance to C. jejuni colonisation at a key site of bacterial persistence.

Results

Three-week-old chickens of line 6₁ and N were inoculated orally with C. jejuni strain M1 and caecal contents and tonsils were sampled at 1 and 5 days post-infection. Caecal colonisation was significantly lower in line 6₁ compared to line N at 1 day post-infection, but not 5 days post-infection. RNA-Seq analysis of caecal tonsils of both lines revealed a limited response to C. jejuni infection compared to age-matched uninfected controls. In line N at days 1 and 5 post-infection, just 8 and 3 differentially expressed genes (DEGs) were detected (fold-change > 2 and false-discovery rate of < 0.05) relative to uninfected controls, respectively. In the relatively resistant line 6₁, a broader response to C. jejuni was observed, with 69 DEGs relating to immune regulation, cell signalling and metabolism at 1 day post-infection. However, by day 5 post-infection, no DEGs were detected. By far, the greatest number of DEGs were between uninfected birds of the two lines implying that differential resistance to C. jejuni is intrinsic. Of these genes, several Major Histocompatibility Complex class I-related genes (MHCIA1, MHCBL2 and MHCIY) and antimicrobial peptides (MUC2, AvBD10 and GZMA) were expressed to a greater extent in line N. Two genes within quantitative trait loci associated with C. jejuni colonisation were also more highly expressed in line N (ASIC4 and BZFP2). Quantitative reverse-transcriptase PCR analysis of a subset of transcripts confirmed the RNA-Seq results.

Conclusions

Our data indicate a limited transcriptional response in the caecal tonsils of inbred chickens to intestinal colonisation by Campylobacter but identify a large number of differentially transcribed genes between lines 6₁ and N that may underlie variation in heritable resistance to C. jejuni.

Epigenetics & Genomics

Campylobacter jejuni

Chicken

Resistance

Susceptibility

Transcriptome

Campylobacter is estimated to have caused 95 million cases of acute gastroenteritis in humans in 2010, with the loss of 21,000 lives and 2.1 million disability-adjusted life years [1]. In the United Kingdom alone, 63,946 laboratory-confirmed cases of human campylobacteriosis were recorded in 2017 [2] and a further 9.3 cases were predicted to be unreported for every one captured by national surveillance [3]. Such infections have been estimated to cost the UK economy approximately £50 million per annum through lost productivity and healthcare costs [4]. Campylobacter infections in humans often involve watery diarrhoea, abdominal cramps and nausea but generally resolve within a week [5]. However, infections can relapse and severe sequelae exist, including inflammatory neuropathies such as the Guillain-Barré syndrome [5].

Source attribution studies unequivocally implicate the handling or consumption of contaminated poultry meat as a key risk factor for human campylobacteriosis, with up to 80% of cases thought to be attributable to the avian reservoir [6, 7]. The caeca are a key site of persistence of Campylobacter in poultry, where numbers of C. jejuni can reach as high as 10¹⁰ colony forming units (CFU)/g of contents in the absence of overt pathology. Given such levels, contamination of carcasses with numbers of C. jejuni predicted to be adequate for human infection is challenging to prevent during the slaughter process [8]. A recent survey in the United Kingdom found that 54% of fresh retail chicken was contaminated with Campylobacter [9]. Birds generally become colonized with C. jejuni from their environment, and across Europe the prevalence of C. jejuni positive flocks ranges from 18-90%, with seasonal variation and the highest levels occurring in the summer months [10]. Control of Campylobacter in poultry primarily relies on stringent on-farm biosecurity measures and carcass treatments. No commercial vaccines exist and it is likely that a multifactorial approach will be required.

It has been estimated that a 2 log ₁₀ reduction in the level of poultry carcass contamination by C. jejuni could lower the incidence of human cases due to this source by 12 to 30-fold [11]. However, more modest estimates suggest a 3 log₁₀ reduction in caecal colonisation would reduce human cases by 58%, although with a high degree of uncertainty [12]. One option to achieve this is to improve the intrinsic resistance of chickens to Campylobacter colonisation. Differences exist in the levels of colonisation across and within commercial broiler lines [13, 14] and these have been associated with variation in the transcriptome of the caeca [14-16] and spleen [17]. Genome-wide association studies in a commercial broiler population have indicated that resistance to caecal C. jejuni colonisation is under moderate genetic control [14]. However, heritable differences in resistance have been associated with quantitative trait loci (QTL) [14, 18], and the transcription of genes related to immunity [14, 19].

White Leghorn-derived inbred chicken lines 6₁ and N have been reported to be relatively resistant and susceptible to colonisation by several C. jejuni strains, respectively [20, 21], with F1 progeny of a cross exhibiting intermediate phenotypes [20]. Genome-wide association studies using backcross [(6₁ x N) x N] and ninth generation advanced intercross (6₁ x N) populations have identified QTLs associated with resistance to caecal colonisation by C. jejuni in these lines [21]. In this study, two candidate genes were identified in the QTL regions, ASIC4, located on chromosome 7, and BZFP2 located on chromosome 16, indicating a potential association with the Major Histocompatibility Complex (MHC) locus also present on chromosome 16. Immune-related genes such as IL6, CXCLi2 and CCLi2 [22] and immune-related pathways including lymphocyte activation, cytokine signalling and Ig production [15, 17, 19] have also been proposed to contribute to the differential resistance of chicken lines. Irrespective of the association of genes or expression patterns with heritable resistance, a need exists to better understand how birds respond to C. jejuni during infection, where previous studies have suggested a pro-inflammatory response that is limited in magnitude and timing [23-26], but which may also differ between commercial broiler lines [27].

Line 6₁ and N chickens not only differ in resistance to Campylobacter, but to gut colonisation by Salmonella enterica serovar Typhimurium and genetic associations have been mapped using a backcross [28]. We recently demonstrated that limited differences exist between lines 6₁ and N in their caecal microbiota and reciprocal transplants of caecal microbiota did not alter their resistance to C. jejuni colonisation, suggesting a role for host factors [29]. Here, we used RNA-Seq to investigate the caecal transcriptome of line 6₁ and N chickens, both in uninfected birds to identify differences between the lines that may underlie differential resistance to pathogens and following experimental challenge with C. jejuni.

Challenge of line 6₁ and line N birds with C. jejuni M1 confirms differential resistance early after inoculation

To examine the level of resistance and susceptibility of lines 6₁ and N to colonisation by C. jejuni M1, three-week-old birds from each line were challenged with 10⁸ CFU of C. jejuni M1 and the resulting numbers of C. jejuni in the caecal content determined at 1 and 5 days post-infection (dpi). At 1 dpi, line 6₁ birds exhibited a significantly (P<0.01) lower level of C. jejuni colonisation in the caeca compared to line N by approximately 3 log₁₀CFU/g (Figure 1). At 5 dpi, no significant difference in caecal colonisation by C. jejuni was observed between the two lines. These results indicate that line 6₁ is relatively resistant to C. jejuni M1 during early colonisation, as reported for other strains [20, 21, 29].

Transcriptional responses to infection in chicken lines differing in C. jejuni resistance

To explore transcriptomic differences underlying the relative resistance of line 6₁and susceptibility of line N to C. jejuni M1 colonisation, RNA-Seq analysis was performed on caecal tonsil tissue from both infected and age-matched uninfected control birds of both lines. Birds were inoculated with C. jejuni at 3 weeks-of-age for parity with earlier reports on differential resistance to C. jejuni at this age [20, 21]. Differentially expressed genes (DEGs) were identified between experimental groups as follows: (1) line N infected vs control birds at 1 dpi, (2) line N infected vs control birds at 5 dpi, (3) line 6₁ infected vs control birds at 1 dpi, (4) line 6₁ infected vs control birds at 5 dpi and (5) line N vs line 6₁ control birds from both 1 and 5 dpi due to the high similarity between control samples across both time points, identified by sample clustering analysis. Gene Ontology (GO) analysis using GSEABase [30] and Ingenuity Pathway Analysis (IPA) [31] were used to identify enriched gene sets and their roles in biological systems.

Line N transcriptional responses

Despite the high levels of caecal C. jejuni M1 colonisation observed in susceptible line N, only 8 and 3 DEGs were identified between infected and control birds at 1 and 5 dpi respectively (Table 1). At 1 dpi, all 8 DEGs were upregulated in infected compared to control birds whereas at 5 dpi, 2 DEGs were upregulated and 1 downregulated. DEGs relating to immune function included Interleukin 1 Receptor Like 1 (IL1RL1) and the C-C motif chemokine 7 (CCL7), which were both upregulated in infected compared to control line N birds at 1 and 5 dpi, respectively. Other DEGs detected in line N were involved in cell growth and survival such as Sestrin 2 (SESN2) and GTPase, IMAP Family Member 8 (GIMAP8), which were both upregulated in infected birds. Overall, RNA-Seq analysis revealed that C. jejuni colonisation in line N birds produced very limited changes in gene expression.

Table 1: DEGs between control and infected susceptible line N birds at 1 and 5 dpi.

	Gene ID	Gene name	FC	P Value	FDR
DE at 1 dpi	ENSGALG00000005648	SESN2	2.06	6.86E-07	3.49E-03
ENSGALG00000041202	FBXO32	1.98	4.56E-07	3.49E-03
ENSGALG00000016785	IL1RL1	1.74	6.12E-06	1.27E-02
ENSGALG00000008885	PDE1A	1.52	8.40E-07	3.49E-03
ENSGALG00000004058	GPR146	1.51	2.07E-06	6.88E-03
ENSGALG00000008050	HBP1	1.41	5.72E-07	3.49E-03
ENSGALG00000008107	IRS4	1.39	3.62E-06	8.60E-03
	ENSGALG00000013489	CCDC82	1.37	3.33E-06	8.60E-03
DE at 5 dpi	ENSGALG00000041079	CCL7	9.58	3.19E-06	2.23E-02
ENSGALG00000044062	GIMAP8	3.9	1.30E-06	2.16E-02
ENSGALG00000031227	ELP6	0.53	4.02E-06	2.23E-02

Due to the limited number of DEGs identified between control and infected line N birds, functional annotation analysis was performed on all 8 DEGs combined from both time points. GO term enrichment analysis did not identify any enriched gene sets in the caecal tonsils of line N birds following C. jejuni colonisation probably due to the limited number of DEGs. IPA identified molecular functions associated with the DEGs between infected and uninfected line N birds, with pathways involved in cell death and survival, cell to cell signalling and interaction and cellular function and maintenance being the most significant (Additional File 1: Figure S1A). IPA also identified a significant network of genes involved in inflammatory responses (Additional File 1: Figure S1B), indicating that C. jejuni may elicit a limited inflammatory response in susceptible line N.

Line 6₁ transcriptional responses

At 1 dpi, 69 DEGs were identified between infected and control line 6₁ birds. Of these, 38 were upregulated and 31 were downregulated in C. jejuni-infected birds compared to controls (Additional file 2: Table S1). Genes involved in the activity of macrophages (including MIP1a and MPEG1), natural killer (NK) cells and CD8α⁺ T lymphocytes (including EOMES, PRF1, CCL1, CD8a chain-like 3 (ENSGALG00000032967), CD8a-like (ENSGALG00000044720)) were amongst those with the highest increase in expression in infected compared to control birds, indicating that early C. jejuni colonisation may stimulate inflammatory and/or antimicrobial responses in which these cell populations play a role.

Genes with the greatest reduction in expression following C. jejuni colonisation in line 6₁ included members of the solute carrier family (SLC4A9, SLC26A4, SLC51B), G protein coupled receptor 6 member A (GPRC6A), TBC1 Domain Family Member 24 (TBC1D24), H6 Family Homeobox 2 (HMX2) and fibroblast growth factors (FGF19 and FGFBP1). At 5 dpi, no DEGs were identified between infected and uninfected line 6₁ birds, despite the high levels of C. jejuni colonisation observed. None of the identified DEGs were shared between the two lines.

GO enrichment analysis of DEGs between infected and control line 6₁ birds at 1 dpi identified 10 associated GO terms, seven of which were upregulated in infected birds. Immune-related GO terms associated with DEGs identified included ‘Negative regulation of IL-17 Production’, ‘Chemokine Activity’ and ‘Interleukin 1 production’, all of which were upregulated in response to C. jejuni colonisation (Additional File 3:Table S2). Of the three GO terms downregulated in response to C. jejuni colonisation, all were involved in nucleotide transport and processing.

By IPA 18 canonical pathways associated with DEGs were identified, of which 11 were immune-related (Figure 2A). Some of the most significant of these included ‘Communication between Innate and Adaptive Immune Cells’, ‘Phagosome Maturation’, ‘Granulocyte Adhesion and Diapedesis’, ‘Agranulocyte Adhesion and Diapedesis’, ‘TREM1 signaling’ and ‘Crosstalk between Dendritic Cells (DC) and Natural Killer Cells’. Other canonical pathways linked to resistance in line 6₁ at 1 dpi included the FXR/RXR Activation and Iron Homeostasis signalling pathways. A number of molecular functions were identified as being significant to resistance in line 6₁ birds following C. jejuni colonisation, the most significant including pathways concerning ‘Molecular Transport’, ‘Lipid Metabolism’ and ‘Small Molecule Biochemistry’ (Figure 2B). Of the physiological functions found to be significantly related to C. jejuni resistance in line 6₁, the most significant were related to immune function and included ‘Hematological System Development and Function’, ‘Immune Cell Trafficking’, ‘Cell-Mediated Immune Responses’, ‘Lymphoid System Development and Function’ and ‘Hematopoesis’ (Figure 2C). IPA network analysis identified two significant networks of genes, involved in the antimicrobial response and cellular movement (Additional File 4:Figure S2A) and lipid metabolism and transport (Additional File 4: Figure S2B).

Comparative analysis of responses between lines after C. jejuni infection

To compare differences in pathway activation in response to C. jejuni colonisation between the two lines, an IPA comparison was performed between activated pathways in infected birds of each line at 1 dpi (Figure 3). A number of immune-related pathways were found to be active in line 6₁ birds at 1 dpi, but not in line N birds, including pathways involved in macrophage activity such as ‘Phagosome Maturation’, ‘MIF-mediated Glucocorticoid’, ‘MIF Regulation of Innate Immune Responses’, and the ‘Inflammasome Pathway’. In contrast, pathways linked to Th2 (‘IL-10 Signalling’, the ‘Th2 pathway’) and IL-6 responses (‘STAT3 Pathway’ and ‘IL-6 Signalling’) were activated in line N but not line 6₁ at 1 dpi with C. jejuni. With few DEG identified in line N, the same genes may underlie the pathways related to these responses. Pathways mainly involved in regulating bile and cholesterol in the liver, but which are also relevant to intestinal inflammation, were also activated to different extents in the caecal tonsils of the two lines at 1 dpi. These included the ‘FXR/RXR Activation’, ‘Hepatic Cholestasis’ and the ‘Iron Homeostasis Signalling’ pathways which were more active in line 6₁ and the ‘LXR/RXR activation’, ‘VDR/RXR activation’ and ‘PPAR signalling’ pathways which were more active in line N. These results indicate inherent differences in the regulation of immune pathways during the early stages of C. jejuni infection, which may have implications for C. jejuni colonisation of the caeca. Significant molecular functions were also associated with the DEGs between infected birds of the two lines, including those involved in lipid and amino acid metabolic pathways (Additional File 5:Figure S3A). We also identified a significant network of genes, mainly expressed to a higher degree in line N, relating to endocrine pathways (Additional File 5: Figure S3B).

Transcriptome comparison of uninfected line 6₁ and line N birds

Gene expression

To investigate inherent differences between chicken lines 6₁ and N, caecal tonsil transcriptomes were compared between control birds from each line. In total, 948 DEGs were identified between control birds of the two lines, pooled from both time points, of which 528 were more highly expressed in line N compared to line 6₁ (Additional File 6: Table S3). Genes with the highest level of expression in line N compared to line 6₁ included Histone Cluster 1 H4 Family Member D (HIST1H4D), Ornithine Carbamoyltransferase (OTC), Choline O-Acetyltransferase (CHAT2), CD8 alpha chain-like (ENSGALG00000045876) and GTPase, IMAP Family Member 5-like (GIMAP5L). Several genes of the major histocompatibility complex I (MHCI) were also expressed to a greater extent in line N, including MHCIA1, MHCBL2 and MHCIY. Mucin 2 (MUC2), β-defensin 10 (AvBD10) and granzyme A (GZMA) were also expressed at a significantly higher level in susceptible line N. Interestingly, two genes identified in the QTL regions associated with C. jejuni colonisation in these lines were expressed at higher levels in line N. Acid Sensing Ion Channel Subunit Family Member 4 (ASIC4) was present in the QTL region on Chromosome 7 whereas ENSGALG00000028367, a zinc finger protein, was in the QTL identified on Chromosome 16 [21].

Of the DEGs identified between line 6₁ and N birds, 420 genes were expressed at higher levels in line 6₁ compared to line N. Of these, those with the greatest fold-change in expression included Class I histocompatibility antigen, F10 alpha chain-like (LOC107050538), Forkhead Box M1 (FOXM1), adenylate cyclase 5 (ADCY5), Deleted In Malignant Brain Tumors 1 (DMTB1), BPI Fold Containing Family B Member 3 (BPIFB3). Several other genes more highly expressed in line 6₁ included the macrophage marker CD163-like protein (DMBT1L), glutathione peroxidase 2 (GPX2; involved in protection against oxidative stress), and trefoil factor 2 (TFF2; involved in stabilisation of the mucosal layer and healing of the epithelial layer).

Functional analysis

GO enrichment analysis performed on DEGs between the control birds of each line identified 10 associated GO terms, five of which were enriched in each line and some of which had immune function (Additional File 7: Table S4). Immune-related GO terms enriched in line 6₁ compared to line N included the ‘Detection of Molecules of Bacterial Origin’, ‘Negative Regulation of IL-1β Production’ and ‘Negative Regulation of Hematopoietic Progenitor Cell Differentiation’ whereas GO terms enriched in line N compared to line 6₁ included ‘Negative Regulation of Viral Release from Host Cell’ and ‘Negative Regulation of Leukocyte Chemotaxis’, indicating that these chicken lines may be in different states of immune readiness prior to their interactions with pathogens.

IPA further identified inherent differences in the level of activity of canonical pathways between the two lines (Figure 4A). Blood coagulation pathways were more activated in line N, and included the ‘Coagulation System’ and ‘Intrinsic Prothrombin Pathway’. The ‘eNOS signalling’ pathway was also more activated in line N. Pathways more active in resistant line 6₁ included ‘Estrogen Biosynthesis’ and ‘Nicotine Degradation II and III’. Differences were also observed in immune functional and regulatory pathways, including ‘Differential Regulation of Cytokine Production in Macrophages and T Helper Cells’, ‘Phagosome Formation’ and ‘Acute Phase Response Signalling’, however no activity patterns were available to stipulate in which line activity was higher. IPA also identified significant differences in molecular functions, with the most significant being ‘Cell-to-Cell Signalling and Interaction’, ‘Molecular Transport’ and ‘Protein Synthesis’ (Figure 4B).

Significant networks of genes associated with cell-to-cell signalling (Additional File 8:Figure S4A), gastrointestinal pathways (Additional File 8: Figure S4B) and amino acid (Additional File 8: Figure S4C) and lipid metabolism (Additional File 8: Figure S4D) were identified with higher activity in line N compared to line 6₁, highlighting that these two lines may be in different metabolic states prior to C. jejuni challenge and susceptibility to C. jejuni in line N may be due in part to distinct metabolism. Furthermore, some genes potentially acting as upstream regulators of DEGs were found to be significantly upregulated in line N, including the B-cell receptor (BCR) (Additional File 9:Figure S5A), microRNA mir155 (Additional File 9:Figure S5B) and the nuclear factor of activated T-cells (NFAT) (Additional File 9: Figure S5C).

Gene cluster analysis

Graphia software [32] analysis revealed the most prominent clustering was by bird line, suggesting that basal gene expression differences between lines 6₁ and N may explain intrinsic resistance as opposed to differences in their response to C. jejuni infection. Two components containing the majority of DEGs were identified. These were Component 1 comprising of 2,822 genes expressed to a greater extent in line N and Component 2 comprising of 2,285 genes expressed to a greater extent in line 6₁ (Figure 5A and B respectively). Mean histogram plots of all genes present within these two components indicated that genes were generally expressed at higher levels in one line compared to the other indicating major differences in the regulation of groups of genes are key to the resistance and susceptible phenotypes in these lines (Figure 5C and D).

Validation of DEGs by qRT-PCR

RNA-Seq data was validated by qRT-PCR analysis of a subset of genes. These were chosen for validation based on their possible biological significance during C. jejuni colonisation and the degree to which they were DE. Genes were mainly selected from the pairwise comparison between control birds of each line, owing to the high number of DEGs identified in this group. Correlation of the qRT-PCR results with the RNA-Seq results produced a correlation co-efficient of R² = 0.86 (p < 0.001) therefore the qRT-PCR results are comparable to the RNA-Seq data (Figure 6).

With the handling or consumption of contaminated chicken accounting for a high proportion of human campylobacteriosis [6, 7], a pressing need exists to reduce the prevalence of C. jejuni in commercial broiler flocks. White Leghorn inbred lines 6₁ and N have an established difference in heritable resistance to C. jejuni colonisation, with genetic variation associated with C. jejuni resistance identified by genome-wide association studies using backcross and advanced 6₁ x N intercross populations [21]. Furthermore, key QTL associated with resistance to C. jejuni are shared between these inbred lines and commercial broilers [14] indicating that findings in inbred chickens can be highly relevant to C. jejuni control in commercial flocks. Using RNA-Seq, we evaluated transcriptomic differences in the caecal tonsils of these inbred lines, both inherently and following C. jejuni colonisation, to investigate the basis of their differential resistance to C. jejuni and potentially obtain biomarkers that could be selected in commercial populations.

We showed that compared to line N, line 6₁ was relatively resistant to early C. jejuni M1 colonisation in the caeca at 1 dpi, however by 5 dpi both lines were similarly susceptible to colonisation. Previously, Boyd et al reported resistance to cloacal and caecal colonisation was apparent in line 6₁ compared to line N from 4 to 20 dpi [20], a discrepancy with this study likely due, in part, to the different C. jejuni strains used by Boyd (C. jejuni 14N and 81-176) and in this report (C. jejuni M1). Chintoan-Uta et al recently reported line N birds to be colonised by C. jejuni 11168H at c. 10⁴ CFU/g caecal contents 9 dpi at 3 weeks-of-age whereas the challenge strain was absent at the limit of detection by direct plating at this time in line 6₁ birds [29]. M1 rapidly colonises the chicken caeca from doses as low as 100 CFU [33, 34] and both the rate of colonisation and caecal burden have proven greater than for other C. jejuni strains tested in the same model, including 11168H (Stevens et al, unpublished data). For many C. jejuni strains, there is a minimum infective dose which can cause high levels of colonisation in the chicken caeca [35-37]. It is plausible that both chicken lines may have been overwhelmed by the dose of C. jejuni M1 administered, now known to be considerably higher than the minimum dose required for reliable colonisation (10²CFU, [33]).

Susceptible line N displayed a very limited caecal transcriptional response to C. jejuni colonisation compared to resistant line 6₁ where a wider, albeit brief, response at 1 dpi was observed. Although functional annotation analysis identified significant pathways related to immune function in both lines, the difference in magnitude between the responses of the two lines may partly explain the relative resistance and susceptibility displayed at 1 dpi. DEGs related to macrophage, NK cell and CD8α⁺ T cell activity were upregulated in resistant line 6₁ at 1 dpi, with functional annotation analysis identifying pathways involved in communication between the innate and adaptive arms of immunity, phagosome maturation and crosstalk between NK cells and DC as involved. It follows that the lower level of colonisation in line 6₁ at 1 dpi may, in part, be attributable to this innate response whereas at 5 dpi, this response is absent and coincides with line 6₁ caecal colonisation levels matching those of line N. It is possible that NK cells are involved in the initial response to C. jejuni observed in line 6₁ as the NK complex is located in close proximity to the chicken MHC B complex, which influences the responsiveness of chicken NK cells [38], and several DE MHCI genes were identified between lines N and 6₁, including MHCIA1, MHCBL2 and MHCIY.

Innate inflammatory responses have been linked to reduced C. jejuni colonisation elsewhere. Although not detected in our study, Psifidi et al found CXCLi1 and CXCLi2 (proinflammatory chemokines involved in heterophil chemotaxis) expression was reduced in the caecal tonsils of both line 6₁and N following C. jejuni infection [21], but more so in line N, further implying innate responses may be involved in controlling C. jejuni colonisation at this site. Moreover, broilers selected for an inflammatory phenotype (high levels of IL-6, CXCLi2 and CCLi2) are less likely to become colonised by C. jejuni compared to those selected for lower inflammatory phenotype [22], indicating that an inherent proinflammatory status reduces the ability of C. jejuni to colonise the chicken caeca. However, in contrast, Humphrey et al found that levels of CXCLi1 and CXCLi2 expression were not related to differences in caecal C. jejuni load [27].

Iron homeostasis signalling pathways were upregulated in resistant line 6₁ at 1 dpi. Iron is essential for bacterial replication and mutants with defects in iron acquisition are frequently attenuated [39], therefore differences in iron availability in the gastrointestinal tract between chicken lines may factor in their relative resistance and merits further investigation. Additionally, higher activity related to MIF-mediated glucocorticoid, the MIF innate immune response and inflammasome pathways occurred during initial C. jejuni colonisation in line 6₁, indicating that MIF and the inflammasome may be mediators in the initial inflammatory response in line 6_1.

Major transcriptional differences were apparent between control birds of both lines, and were far greater than those observed in response to C. jejuni infection. Our study is not the first to identify such a degree of inherent transcriptional variation between White Leghorn inbred lines. For example, gene expression in the spleen and thymus differs by several hundred genes between lines 6₁ and 7₂, which are relatively resistant and susceptible to Marek’s Disease Virus respectively [40]. Significant networks of genes associated with amino acid and lipid metabolism were upregulated in line N control birds, consistent with other studies linking higher inherent metabolic states with susceptibility to C. jejuni. Li et al (2010) found amino acid, lipid and glucose metabolic pathways to be upregulated in a C. jejuni-susceptible line compared to a resistant line following colonisation, with amino acid processes also upregulated in control birds of the susceptible line [15]. Additionally, Li et al (2011) found increased expression of genes involved in fatty acid and protein metabolic processes between C. jejuni-colonised compared to non-colonised birds of the same line [16]. Our data identified differences in transcription of genes associated with metabolic activity between the lines coinciding with differential colonisation levels at 1 dpi. For example, higher activation in the farnesoid x receptor (FXR)/retinoid x receptor activation was present in line 6₁ but liver x receptor (LXR)/RXR, vitamin D receptor (VDR)/RXR and peroxisome proliferator-activated receptor (PPARα)/RXR activation was higher in line N. FXR, LXR and PPAR-α are mainly associated with lipid metabolism and in the regulation of triglyceride levels in mammals [41-43]. However, between them, FXR, LXR and PPAR-α also have various roles in regulating intestinal inflammation and immunity including effects on macrophage inflammatory activity, reducing the presence of reactive oxygen species (ROS) and maintaining the intestinal barrier [44-49], and are considered potential therapeutic targets in the case of mammalian IBD [45, 50, 51]. Expression of FXR, LXR and PPAR-α in these chicken lines may therefore modulate intestinal inflammatory responses, influencing C. jejuni colonisation. Furthermore, their roles in triglyceride metabolism may also influence colonisation as triglyceride-rich lipoprotein can bind LPS, reducing LPS toxicity during bacterial infection and reducing macrophage activation [52]. Vitamin D and its receptor are important in maintaining the mucosal barrier of the intestine [53] and modulate proinflammatory cytokine production [54]. Abiotic IL10^-/- mice treated with artificial vitamin D suffered less diarrhoea and had lower levels of intestinal IL-6, IFN-γ and CCL2 during campylobacteriosis than mice that did not receive vitamin D [55], therefore the contribution of vitamin D and the vitamin D receptor to C. jejuni susceptibility in line N chickens warrants further study. Upstream gene regulators involved in several immune-related networks were associated with the DEGs identified between control birds of both lines (BCR, NFAT and miR-155). Previously, increased caecal miR-155, a regulator of inflammatory processes in mammals [56], was observed following C. jejuni colonisation in chickens [57]. These differences in inflammatory states prior to C. jejuni inoculation likely influence the speed and resulting level of colonisation in different chicken lines.

Lines 6₁ is also relatively resistant to S. Typhimurium colonisation compared to line N [28, 58]. QTLs associated with resistance to both S. Typhimurium and C. jejuni have been identified on chromosome 14 [14, 21, 59] and chromosome 16 [14, 21, 60, 61] implying that similar mechanisms of genetic control may confer resistance to both C. jejuni and Salmonella. Furthermore, greater inflammatory responses to Salmonella have been observed in resistant chicken lines compared to susceptible birds [62] highlighting the importance of innate responses in resistance to enteric bacterial pathogens.

As C. jejuni mainly resides in the chicken lumen, it is plausible that differences at the mucosal surface or extracellular milieu may account for resistance. Genes encoding MUC2 and AvBD10 were expressed to a higher level in line N compared to line 6₁ control birds. Previously, it has been shown that chicken mucus inhibits C. jejuni invasion of both chicken and human primary intestinal cells compared with human mucus [63]. It is therefore possible that inherent differences in the composition of chicken mucus between lines accounts for the differences in the colonisation levels observed. GZMA was also more highly expressed in line N, corresponding to a microarray study where GZMA expression was higher in the caeca of C. jejuni-colonised compared to non-colonised birds, indicating GZMA is linked with elevated C. jejuni levels in the caeca [16]. Recent studies have identified mostly limited differences in the microbial communities present in the caeca of lines 6₁and N at the age they were inoculated herein, but the extent to which these differences drive distinct patterns of gene expression in the gut, or vice versa, is unknown [29].

Other studies have examined the impact of gene expression and host genetics on C. jejuni colonisation of the caeca [16, 17, 19, 20, 23, 24]. However, these varied in the strain of C. jejuni used, bird line and challenge age of the birds (between 2 and 4 weeks-of-age), and sampling intervals (from 6 hours to 14 dpi) resulting in variation of the level of colonisation observed and challenges in extrapolating from one study to the next. Despite these differences, a similar narrative can be seen throughout these studies whereby a moderate number of significant DEGs was identified upon Campylobacter challenge, but often at a low fold-change indicating that even high levels of C. jejuni colonisation does not elicit a dramatic response in the chicken [16, 17, 21, 64].

A GWAS on an advanced intercross of line 6₁ and N previously identified 6 SNPs on chromosomes 4, 14 and 16 associated with resistance to C. jejuni colonisation [21]. We identified two genes located within the QTL regions associated with C. jejuni colonisation in these lines, ASIC4 (Acid Sensing Ion Channel Subunit Family Member 4) and ENSGALG00000028367, both of which were inherently expressed to a greater extent in line N. ASIC4 is broadly expressed in the mammalian nervous system but its role in birds is unknown. In mammals, ASICs are known for their role in neuroinflammation [65] and promote exocytosis and maturation in bone marrow-derived macrophages stimulated by extracellular acidosis [66]. ENSGALG00000028367, or BZFP2, is a zinc finger-like protein which likely binds nucleic acids, proteins and other small molecules [67], and bares similarity to the CKR1-like gene and is present in a region of the MHC-B locus [68].

To conclude, RNA-Seq analysis demonstrated an initial inflammatory response to C. jejuni infection of a greater magnitude in resistant line 6₁ compared to line N, which may be associated with caecal colonisation. This response was short lived and absent at later intervals when differential resistance was not detected. By far, the most striking differences in gene expression were detected between uninfected control birds of the two lines. The identification of candidate genes involved in early innate responses and metabolic pathways provides a foundation for future studies on avian heritable resistance to Campylobacter.

Animals: Two inbred White Leghorn chicken lines were used in this study. Lines 6₁ and N, reported to be relatively resistant and susceptible to C. jejuni colonisation respectively, originate from the USDA ARS Avian Disease and Oncology Laboratory (formerly the Regional Poultry Laboratory) in East Lansing, MI, USA. [12]. Flocks of both lines were maintained under specified pathogen-free (SPF) conditions at the National Avian Research Facility (NARF) at the Roslin Institute, UK.

Bacterial culture, experimental design and sample collection: C. jejuni strain M1 [69] was used to inoculate animals in this study. C. jejuni M1 was cultured on modified charcoal-cephoperazone-microaerophillic agar (mCCDA; Oxoid) or in Mueller-Hinton (MH) broth (Oxoid) at 37°C with 5% O₂, 5% CO₂ and 90% N₂ in a microaerophilic cabinet. Liquid cultures in broth were with shaking at 400 rpm under the same atmospheric conditions.

Twelve birds each of White Leghorn lines 6₁ and N were obtained on the day of hatch and housed under SPF conditions with access to feed and water ad libitum. For each line there were 6 control and 6 infected birds. At 3 weeks-of-age, birds of the infected groups were challenged by oral gavage with 10⁸C. jejuni M1 in 0.1 ml of MH broth and birds of the control groups given an equivalent volume of MH broth only. At 1 and 5 dpi, 3 chickens of each group were culled by cervical dislocation and death confirmed by permanent cessation of blood circulation. At post mortem examination, caeca were collected for the enumeration of C. jejuni by plating 100 µl of triplicate 10-fold serial dilutions of caecal contents in phosphate-buffered saline (PBS) to mCCDA plates. Caecal tonsils were collected promptly into RNAlater® (Life Technologies) and stored at -80°C until processing.

RNA-Seq library preparation, sequencing and data analysis: RNA was extracted from both caecal tonsils using the Qiagen RNeasy mini kit according to the manufacturer’s instructions. RNA preparations were assessed for quantity and quality using a Qubit™ Fluorimeter (Invitrogen) and an Agilent 2200 TapeStation (Agilent Technologies) respectively. The resulting RNA integrity number (RIN) values for all RNA samples was greater than 9 therefore all 24 samples (n=3 per group) were subsequently submitted to the Edinburgh Genomics sequencing facility (Edinburgh, UK) for RNA-Seq. RNA libraries where generated using the TruSeq stranded mRNA-Seq Library Prep kit (Illumina) and transcriptomes sequenced using the Illumina HiSeq 4000 system to generate 75 bp paired end reads at a depth of 50 million reads per sample.

The quality of the raw sequence reads was assessed using FASTQC (v0.11.5) [70]. Cutadapt (v1.14) [71] was used to trim adapter sequences, remove bases with a Phred score of less than 30 and ensure resulting sequences were at least 50 bp in length. Trimmed reads were then mapped to the Gallus gallus reference genome (GalGal5, Ensembl release 89) using the STAR aligner software package (v2.5.1b) [72] with a minimum alignment of 52.5 million input reads for each sample (Additional File 10: Table S5). In all samples, over 90% of reads were uniquely mapped with fewer than 10% either unmapped or mapped to multiple loci. The number of reads aligning to genomic features were determined using FeatureCounts (v1.5.3) [73] using default parameters. Analysis of DEGs was performed in R using EdgeR package with DEGs exhibiting a fold change (FC) > 2 and a false discovery rate (FDR) < 0.05 considered significant.

Gene ontology term enrichment analysis was performed in R using the GSEABase package (downloaded from the Gene Set Enrichment Analysis (GSEA) website, http://software.broadinstitute.org/gsea/index.jsp, file: C5.all.v6.0.symbols). Significant gene ontology was determined using the mRoast function of the Limma package in R for each pairwise comparison. Ingenuity Pathway Analysis (IPA) was performed on significant DEGs using IPA software (Qiagen Bioinformatics, www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) to classify their associated biological functions, canonical pathways and biological networks. Network analysis was performed using the normalised raw counts in Graphia Pro [32] with a Pearson correlation threshold of r=0.93. The number of nodes (genes) in the analysis was 6066 linked by 181.1k edges. Markov Clustering (MCL) was performed with a granularity of 1.5 on these networks to identify components containing genes of similar expression patterns. Components of less than 10 nodes were removed from the analysis. Gene lists derived from Graphia were submitted to the functional annotation tool DAVID [74] to further investigate the roles of genes identified in the components, with Gallus gallus selected as the background for these lists.

cDNA preparation and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR): Genes related to immune function that were observed to be DE by RNA-Seq were validated by qRT-PCR. The Verso cDNA Synthesis kit (Thermo Scientific) was used to prepare cDNA from 1 μg of total RNA according to the manufacturer’s instructions. Quantitative PCR reactions were performed using the Forget-Me-Not™ qPCR Master Mix (Biotium) in 20 μL volumes containing 1 X Forget-Me- Not™ qPCR Master Mix, 0.5 μM each of the forward and reverse primers, 50 nM of ROX reference dye and 2 μL of cDNA at a 1:10 dilution in template buffer. Gene-specific primers were purchased from Sigma and are detailed in Additional File 11: Table S6. The amplification and detection of specific DNA was achieved using the AB 7500 FAST Real-Time PCR System (Applied Biosystems) to the following program: 95°C for 2 min followed by 40 cycles of 95°C for 5 s then 60°C for 30 s. To confirm the presence of a single PCR product, melting curves were generated by one cycle of 60°C for 1 min, increasing to 95°C in 1% increments every 15s. Samples were run in triplicate and each qPCR experiment contained 3 no-template controls and a 5-fold dilution series in duplicate of pooled caecal tonsil-derived cDNA from several birds from which standard curves were generated. The relative expression of genes were calculated as described by Pfaffl [75] and normalised to the geometric mean of three reference genes; r28S, TBP and GAPDH. A Spearman’s correlation was calculated between the log₂ fold-change of the RNA-Seq results to the log₂ fold-change detected by qPCR in R.

Ethics approval

All procedures were conducted under Home Office project licence PCD70CB48 in full compliance with the requirements of the Animal (Scientific Procedures) Act 1986, with the approval of the Moredun Institute Animal Welfare and Ethical Review Board. Housing and husbandry complied with the Code of Practice for Housing and Care of Animals Bred, Supplied or Used for Scientific Purposes and were also overseen by the Moredun Institute Animal Welfare and Ethical Review Board. Animals were culled by a schedule one method authorized by the Animals (Scientific Procedures) Act 1986. This study was performed in compliance with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines as defined by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs).

Availability of data and materials

Sequencing data have been submitted to the European Nucleotide Archive (https://www.ebi.ac.uk/ena) under accession number PRJEB24399.

Competing interests

The authors declare no conflicts of interest and no competing financial interests.

Funding

We gratefully acknowledge the support of the Scottish Government funding via the Rural & Environmental Science and Analytical Services programme of research for 2016-2021 and the Biotechnology & Biological Sciences Research Council (Institute Strategic Programme BBS/E/D/20002172 and Core Capability Grant in support of the National Avian Research Facility).

Authors' contributions

KMR: designed and carried out the study, performed transcriptomic analysis and co-wrote the manuscript. JS: assisted with downstream analysis and editing the manuscript. AB: qRT-PCR validation and co-wrote the manuscript. CCU: assisted with sample collection. LV and AP were co-investigators of one of the grants that funded the work, provided critical analysis of findings and edited the manuscript. MS: conceived the study, secured funding and edited the manuscript. All authors approved the final version of the manuscript.

Acknowledgements

We thank animal attendants at the Moredun Research Institute and technicians at Edinburgh Genomics for their assistance.

cDNA – complementary DNA

DC – dendritic cell

DE - differentially expressed

DEGs - differentially expressed genes

dpi – days post-infection

FC – fold change

FDR – false discovery rate

GO – gene ontology

IPA – Ingenuity Pathway Analysis

mCCDA - Modified charcoal-cefoperazone-deoxycholate agar

NK – natural killer

qRT-PCR - quantitative reverse-transcriptase polymerase chain reaction

SPF – specified pathogen-free

Havelaar AH, Kirk MD, Torgerson PR, Gibb HJ, Hald T, Lake RJ, Praet N, Bellinger DC, de Silva NR, Gargouri N et al: World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Medicine 2015, 12(12):e1001923.
PHE: Anon. Zoonoses report UK. In. UK: Public Health England; 2017.
Tam CC, Rodrigues LC, Viviani L, Dodds JP, Evans MR, Hunter PR, Gray JJ, Letley LH, Rait G, Tompkins DS et al: Longitudinal study of infectious intestinal disease in the UK (IID2 study): incidence in the community and presenting to general practice. Gut 2012, 61(1):69-77.
Tam CC, O'Brien SJ: Economic cost of Campylobacter, norovirus and rotavirus disease in the United Kingdom. PLoS One 2016, 11(2):e0138526.
Janssen R, Krogfelt KA, Cawthraw SA, van Pelt W, Wagenaar JA, Owen RJ: Host-pathogen interactions in Campylobacter infections: the host perspective. Clin Microbiol Rev 2008, 21(3):505-518.
EFSA: EFSA Panel on Biological Hazards (BIOHAZ). Scientific Opinion on Campylobacter in broiler meat production: control options and performance objectives and/or targets at different stages of the food chain. EFSA Journal 2011, 9(4):2105.
Skarp CPA, Hänninen ML, Rautelin HIK: Campylobacteriosis: the role of poultry meat. Clin Microbiol Infect 2016, 22(2):103-109.
Wagenaar JA, French NP, Havelaar AH: Preventing Campylobacter at the source: why is it so difficult?Clin Infect Dis 2013, 57(11):1600-1606.
Jorgenson F, Charlett, A., Arnold, E., Swift, C., Elviss, N.C.: A microbiological survey of Campylobacter contamination in fresh whole UK-produced chilled chickens at retail sale. In. Edited by England PH; 2018.
Newell DG, Fearnley C: Sources of Campylobacter colonization in broiler chickens. Appl Environ Microbiol 2003, 69(8):4343-4351.
Rosenquist H, Nielsen NL, Sommer HM, Nørrung B, Christensen BB: Quantitative risk assessment of human campylobacteriosis associated with thermophilic Campylobacter species in chickens. Int J Food Microbiol 2003, 83(1):87-103.
Hazards EPanel oB, Koutsoumanis K, Allende A, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Davies R, De Cesare A, Herman L, Hilbert F et al: Update and review of control options for Campylobacter in broilers at primary production. EFSA Journal 2020, 18(4):e06090.
Li X, Swaggerty C, Kogut M, Chiang H-I, Wang Y, He H, Stern N, Pevzner I, Zhou H: The Paternal effect of Campylobacter jejuni colonization in ceca in broilers. Poultry science 2008, 87:1742-1747.
Psifidi A, Kranis A, Rothwell LM, Bremner A, Russell K, Robledo D, Bush SJ, Fife M, Hocking PM, Banos G et al: Genetic control of Campylobacter gut colonisation in broiler chickens: genomic and transcriptomic characterisation.Scientific Reports 2021, 11:1623.
Li X, Swaggerty CL, Kogut MH, Chiang HI, Wang Y, Genovese KJ, He H, Zhou H: Gene expression profiling of the local cecal response of genetic chicken lines that differ in their susceptibility to Campylobacter jejuni colonization. PLoS One 2010, 5(7):e11827.
Li XY, Swaggerty CL, Kogut MH, Chiang HI, Wang Y, Genovese KJ, He H, Pevzner IY, Zhou HJ: Caecal transcriptome analysis of colonized and non-colonized chickens within two genetic lines that differ in caecal colonization by Campylobacter jejuni. Anim Genet 2011, 42(5):491-500.
Li X, Swaggerty C, Kogut M, Chiang H-I, Wang Y, He H, McCarthy F, Burgess S, Pevzner I, Zhou H: Systemic response to Campylobacter jejuni infection by profiling gene transcription in the spleens of two genetic lines of chickens. Immunogenetics 2011, 64:59-69.
Connell S, Meade KG, Allan B, Lloyd AT, Downing T, O'Farrelly C, Bradley DG: Genome-wide association analysis of avian resistance to Campylobacter jejuni colonization identifies risk locus spanning the CDH13 gene. G3 (Bethesda) 2013, 3(5):881-890.
Connell S, Meade KG, Allan B, Lloyd AT, Kenny E, Cormican P, Morris DW, Bradley DG, O'Farrelly C: Avian resistance to Campylobacter jejuni colonization is associated with an intestinal immunogene expression signature identified by mRNA sequencing. PLoS One 2012, 7(8):e40409.
Boyd Y, Herbert EG, Marston KL, Jones MA, Barrow PA: Host genes affect intestinal colonisation of newly hatched chickens by Campylobacter jejuni. Immunogenetics 2005, 57(3-4):248-253.
Psifidi A, Fife M, Howell J, Matika O, van Diemen PM, Kuo R, Smith J, Hocking PM, Salmon N, Jones MA et al: The genomic architecture of resistance to Campylobacter jejuni intestinal colonisation in chickens. BMC Genomics 2016, 17:293.
Swaggerty CL, Pevzner IY, He H, Genovese KJ, Kogut MH: Selection for pro-inflammatory mediators produces chickens more resistant to Campylobacter jejuni. Poult Sci 2017, 96(6):1623-1627.
Shaughnessy RG, Meade KG, Cahalane S, Allan B, Reiman C, Callanan JJ, O'Farrelly C: Innate immune gene expression differentiates the early avian intestinal response between Salmonella and Campylobacter. Vet Immunol Immunopathol 2009, 132(2-4):191-198.
Shaughnessy RG, Meade KG, McGivney BA, Allan B, O'Farrelly C: Global gene expression analysis of chicken caecal response to Campylobacter jejuni. Vet Immunol Immunopathol 2011, 142(1-2):64-71.
Smith CK, Abuoun M, Cawthraw SA, Humphrey TJ, Rothwell L, Kaiser P, Barrow PA, Jones MA: Campylobacter colonization of the chicken induces a proinflammatory response in mucosal tissues. FEMS Immunol Med Microbiol 2008, 54(1):114-121.
Reid WD, Close AJ, Humphrey S, Chaloner G, Lacharme-Lora L, Rothwell L, Kaiser P, Williams NJ, Humphrey TJ, Wigley P et al: Cytokine responses in birds challenged with the human food-borne pathogen Campylobacter jejuni implies a Th17 response. R Soc Open Sci 2016, 3(3):150541.
Humphrey S, Chaloner G, Kemmett K, Davidson N, Williams N, Kipar A, Humphrey T, Wigley P: Campylobacter jejuni is not merely a commensal in commercial broiler chickens and affects bird welfare. mBio 2014, 5(4):e01364-01314.
Fife MS, Howell JS, Salmon N, Hocking PM, van Diemen PM, Jones MA, Stevens MP, Kaiser P: Genome-wide SNP analysis identifies major QTL for Salmonella colonization in the chicken. Anim Genet 2011, 42(2):134-140.
Chintoan-Uta C, Wisedchanwet T, Glendinning L, Bremner A, Psifidi A, Vervelde L, Watson K, Watson M, Stevens MP: Role of cecal microbiota in the differential resistance of inbred chicken lines to colonization by Campylobacter jejuni. Appl Environ Microbiol 2020, 86(7).
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005, 102(43):15545-15550.
Krämer A, Green J, Pollard J, Jr., Tugendreich S: Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 2014, 30(4):523-530.
Livigni A, O'Hara L, Polak ME, Angus T, Wright DW, Smith LB, Freeman TC: A graphical and computational modeling platform for biological pathways. Nat Protoc 2018, 13(4):705-722.
Vohra P, Chintoan-Uta C, Terra VS, Bremner A, Cuccui J, Wren BW, Vervelde L, Stevens MP: Evaluation of glycosylated FlpA and SodB as subunit vaccines against Campylobacter jejuni colonisation in chickens. Vaccines (Basel) 2020, 8(3).
Chaloner G, Wigley P, Humphrey S, Kemmett K, Lacharme-Lora L, Humphrey T, Williams N: Dynamics of dual infection with Campylobacter jejuni strains in chickens reveals distinct strain-to-strain variation in infection ecology. Appl Environ Microbiol 2014, 80(20):6366-6372.
Ringoir DD, Korolik V: Colonisation phenotype and colonisation potential differences in Campylobacter jejuni strains in chickens before and after passage in vivo. Vet Microbiol 2003, 92(3):225-235.
Young CR, Ziprin RL, Hume ME, Stanker LH: Dose response and organ invasion of day-of-hatch Leghorn chicks by different isolates of Campylobacter jejuni. Avian Dis 1999, 43(4):763-767.
Cawthraw SA, Wassenaar TM, Ayling R, Newell DG: Increased colonization potential of Campylobacter jejuni strain 81116 after passage through chickens and its implication on the rate of transmission within flocks. Epidemiol Infect 1996, 117(1):213-215.
Kaufman J, Milne S, Göbel TWF, Walker BA, Jacob JP, Auffray C, Zoorob R, Beck S: The chicken B locus is a minimal essential major histocompatibility complex. Nature 1999, 401(6756):923-925.
Palyada K, Threadgill D, Stintzi A: Iron acquisition and regulation in Campylobacter jejuni. J Bacteriol 2004, 186(14):4714-4729.
Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, Nair V, Burt DW, Kaiser P: Systems analysis of immune responses in Marek's disease virus-infected chickens identifies a gene involved in susceptibility and highlights a possible novel pathogenicity mechanism. J Virol 2011, 85(21):11146-11158.
Patel MB, Oza NA, Anand IS, Deshpande SS, Patel CN: Liver x receptor: a novel therapeutic target. Indian J Pharm Sci 2008, 70(2):135-144.
Schultz JR, Tu H, Luk A, Repa JJ, Medina JC, Li L, Schwendner S, Wang S, Thoolen M, Mangelsdorf DJ et al: Role of LXRs in control of lipogenesis. Genes Dev 2000, 14(22):2831-2838.
van Raalte DH, Li M, Pritchard PH, Wasan KM: Peroxisome proliferator-activated receptor (PPAR)-alpha: a pharmacological target with a promising future. Pharm Res 2004, 21(9):1531-1538.
N AG, Castrillo A: Liver X receptors as regulators of macrophage inflammatory and metabolic pathways. Biochim Biophys Acta 2011, 1812(8):982-994.
Wildenberg ME, van den Brink GR: FXR activation inhibits inflammation and preserves the intestinal barrier in IBD. Gut 2011, 60(4):432-433.
Armstrong LE, Guo GL: Role of FXR in liver inflammation during nonalcoholic steatohepatitis. Curr Pharmacol Rep 2017, 3(2):92-100.
Korbecki J, Bobiński R, Dutka M: Self-regulation of the inflammatory response by peroxisome proliferator-activated receptors. Inflammation Research 2019, 68(6):443-458.
Zandbergen F, Plutzky J: PPARalpha in atherosclerosis and inflammation. Biochim Biophys Acta 2007, 1771(8):972-982.
Le Menn G, Neels JG: Regulation of immune cell function by PPARs and the connection with metabolic and neurodegenerative diseases. Int J Mol Sci 2018, 19(6).
Azuma YT, Nishiyama K, Matsuo Y, Kuwamura M, Morioka A, Nakajima H, Takeuchi T: PPARα contributes to colonic protection in mice with DSS-induced colitis. Int Immunopharmacol 2010, 10(10):1261-1267.
Klepsch V, Moschen AR, Tilg H, Baier G, Hermann-Kleiter N: Nuclear receptors regulate intestinal inflammation in the context of IBD. Front Immunol 2019, 10:1070.
Barcia AM, Harris HW: Triglyceride-rich lipoproteins as agents of innate immunity. Clin Infect Dis 2005, 41 Suppl 7:S498-503.
Kong J, Zhang Z, Musch MW, Ning G, Sun J, Hart J, Bissonnette M, Li YC: Novel role of the vitamin D receptor in maintaining the integrity of the intestinal mucosal barrier. Am J Physiol Gastrointest Liver Physiol 2008, 294(1):G208-216.
Yin K, Agrawal DK: Vitamin D and inflammatory diseases. J Inflamm Res 2014, 7:69-87.
Mousavi S, Lobo de Sá FD, Schulzke JD, Bücker R, Bereswill S, Heimesaat MM: Vitamin D in acute campylobacteriosis-results from an intervention study applying a clinical Campylobacter jejuni induced enterocolitis model. Front Immunol 2019, 10:2094.
Mahesh G, Biswas R: MicroRNA-155: A master regulator of inflammation. J Interferon Cytokine Res 2019, 39(6):321-330.
Liu X, Liu L, Zhang M, Wang H, Yang N, Li X: Chicken cecal microRNAs in the response to Campylobacter jejuni inoculation by Solexa sequencing. Poult Sci 2016, 95(12):2819-2823.
Barrow PA, Bumstead N, Marston K, Lovell MA, Wigley P: Faecal shedding and intestinal colonization of Salmonella enterica in in-bred chickens: the effect of host-genetic background. Epidemiol Infect 2004, 132(1):117-126.
Calenge F, Vignal A, Demars J, Fève K, Menanteau P, Velge P, Beaumont C: New QTL for resistance to Salmonella carrier-state identified on fowl microchromosomes. Mol Genet Genomics 2011, 285(3):237-243.
Calenge F, Lecerf F, Demars J, Feve K, Vignoles F, Pitel F, Vignal A, Velge P, Sellier N, Beaumont C: QTL for resistance to Salmonella carrier state confirmed in both experimental and commercial chicken lines. Anim Genet 2009, 40(5):590-597.
Tilquin P, Barrow PA, Marly J, Pitel F, Plisson-Petit F, Velge P, Vignal A, Baret PV, Bumstead N, Beaumont C: A genome scan for quantitative trait loci affecting the Salmonella carrier-state in the chicken. Genet Sel Evol 2005, 37(5):539-561.
Swaggerty CL, Kogut MH, Ferro PJ, Rothwell L, Pevzner IY, Kaiser P: Differential cytokine mRNA expression in heterophils isolated from Salmonella-resistant and -susceptible chickens. Immunology 2004, 113(1):139-148.
Byrne CM, Clyne M, Bourke B: Campylobacter jejuni adhere to and invade chicken intestinal epithelial cells in vitro. Microbiology 2007, 153(2):561-569.
Bailey RA, Kranis A, Psifidi A, Watson KA, Rothwell L, Hocking PM, Kaiser P, Stevens MP, Avendano S: Colonization of a commercial broiler line by Campylobacter is under limited genetic control and does not significantly impair performance or intestinal health. Poultry Science 2018.
Ni L, Fang P, Hu Z-l, Zhou H-y, Chen J-g, Wang F, Jin Y: Identification and function of acid-sensing ion channels in RAW 264.7 macrophage cells. Current Medical Science 2018, 38(3):436-442.
Kong X, Tang X, Du W, Tong J, Yan Y, Zheng F, Fang M, Gong F, Tan Z: Extracellular acidosis modulates the endocytosis and maturation of macrophages. Cellular immunology 2013, 281:44-50.
Miller MM, Taylor RL, Jr.: Brief review of the chicken Major Histocompatibility Complex: the genes, their distribution on chromosome 16, and their contributions to disease resistance. Poult Sci 2016, 95(2):375-392.
Shiina T, Briles WE, Goto RM, Hosomichi K, Yanagiya K, Shimizu S, Inoko H, Miller MM: Extended gene map reveals tripartite motif, C-type lectin, and Ig superfamily type genes within a subregion of the chicken MHC-B affecting infectious disease. J Immunol 2007, 178(11):7162-7172.
Friis C, Wassenaar TM, Javed MA, Snipen L, Lagesen K, Hallin PF, Newell DG, Toszeghy M, Ridley A, Manning G et al: Genomic characterization of Campylobacter jejuni strain M1. PLoS One 2010, 5(8):e12253.
Andrews S: FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc. 2010.
Martin M: Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 2011, 17(1):3.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29(1):15-21.
Liao Y, Smyth GK, Shi W: featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30(7):923-930.
Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biology 2003, 4(9):R60.
Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 2001, 29(9):e45.

No competing interests reported.

Download PDF

Editorial decision: Major revision
05 Apr, 2021
Reviewers agreed at journal
18 Mar, 2021
Reviews received at journal
17 Mar, 2021
Reviews received at journal
12 Mar, 2021
Reviewers agreed at journal
05 Mar, 2021
Reviewers agreed at journal
03 Mar, 2021
Reviews received at journal
27 Feb, 2021
Reviewers agreed at journal
10 Feb, 2021
Reviewers invited by journal
09 Feb, 2021
Editor assigned by journal
09 Feb, 2021
Editor invited by journal
09 Feb, 2021
Submission checks completed at journal
09 Feb, 2021
First submitted to journal
01 Feb, 2021

You are reading this latest preprint version

Transcriptomic analysis of caecal tissue in inbred chicken lines that exhibit heritable differences in resistance to Campylobacter jejuni

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Results

Discussion

Methods

Declarations

Abbreviations

References

Additional Declarations

Supplementary Files

Status:

Version 1