Figure 1.
Infection model optimization and distribution of colonization levels.
(A) To determine the optimum dose where differential colonization is observed, three groups of fifteen birds were initially inoculated with low (i, 3.5 ×10), medium (ii, 3.5×10
) and high (iii, 3.5×10
) doses of C. jejuni, and the colonization status of their caeca estimated after 48 hours. No colonization was detected in any bird after infection with 3.5×10
CFU. All birds were colonized after high dosage. Maximum differentiation is achieved after inoculation with the medium dose of 3.5×10
CFU C. jejuni. (B) 255 birds were challenged with 3.5×10
C. jejuni and their caecal colonization status determined after 48 hours. No C. jejuni colonization could be detected in 38 birds. Colonization levels of the remaining birds varied from 2×10
– 4 ×10
CFU/g with the majority of this group having very high caecal C. jejuni levels (
10
CFU/g C. jejuni).
Figure 2.
RNAseq read mapping percentage breakdown.
175.31 million (48.2%) of the reads generated from mRNA sequencing could be mapped uniquely to known gene models and therefore could be used in estimation of gene expression levels. 101.28 million reads (27.8%) were mapped successfully to the genome but could not be mapped to NCBI gene models (unknown transcripts, 3.65 Gbp) indicating a necessity for more comprehensive annotation of the chicken transcriptome. Relatively few repeat reads (20.49 million reads, 5.6%) were observed consistent with the low repeat density of the chicken genome [51].
Figure 3.
Detection of differentially expressed genes between high-colonized and nil-colonized birds.
Log2 fold change is plotted versus mean count numbers reflecting expression level. The R package DESeq was used to compare expression levels between the two groups of high-colonized and nil-colonized chickens and to identify genes displaying significant differential expression using a negative binomial model of the count data. 221 genes exhibiting significant differential expression with p-value 0.01 are highlighted red.
Figure 4.
Differential expression as a function of read count.
The proportion of genes differentially expressed is plotted against the total number of reads for each gene. This plot investigates whether differential expression is more likely to be detected in genes with a higher number of read counts. The green line is the probability weighting function fitted by Goseq. Flat = no bias present and no correction. This illustrates that the method to detect differential expression was robust against bias introduced by differing count numbers.
Table 1.
GO Terms and KEGG pathways significantly enriched in differentially expressed genes.
Table 2.
Immunoglobulins and non-categorized immune genes exhibiting differential expression between resistant and susceptible birds.
Table 3.
Genes involved in lymphocyte development and function exhibiting significant difference in expression between resistant and susceptible birds.
Table 4.
Genes involved in the renin-angiotensin system (RAS) exhibiting differential expression between resistant and susceptible birds.