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BMC Veterinary Research

Open Access

The early immune response to infection of chickens with Infectious Bronchitis Virus (IBV) in susceptible and resistant birds

  • Jacqueline Smith1Email author,
  • Jean-Remy Sadeyen2,
  • David Cavanagh2,
  • Pete Kaiser1 and
  • David W. Burt1
Contributed equally
BMC Veterinary Research201511:256

https://doi.org/10.1186/s12917-015-0575-6

Received: 1 July 2015

Accepted: 5 October 2015

Published: 9 October 2015

Abstract

Background

Infectious Bronchitis is a highly contagious respiratory disease which causes tracheal lesions and also affects the reproductive tract and is responsible for large economic losses to the poultry industry every year. This is due to both mortality (either directly provoked by IBV itself or due to subsequent bacterial infection) and lost egg production. The virus is difficult to control by vaccination, so new methods to curb the impact of the disease need to be sought. Here, we seek to identify genes conferring resistance to this coronavirus, which could help in selective breeding programs to rear chickens which do not succumb to the effects of this disease.

Methods

Whole genome gene expression microarrays were used to analyse the gene expression differences, which occur upon infection of birds with Infectious Bronchitis Virus (IBV). Tracheal tissue was examined from control and infected birds at 2, 3 and 4 days post-infection in birds known to be either susceptible or resistant to the virus. The host innate immune response was evaluated over these 3 days and differences between the susceptible and resistant lines examined.

Results

Genes and biological pathways involved in the early host response to IBV infection were determined andgene expression differences between susceptible and resistant birds were identified. Potential candidate genes for resistance to IBV are highlighted.

Conclusions

The early host response to IBV is analysed and potential candidate genes for disease resistance are identified. These putative resistance genes can be used as targets for future genetic and functional studies to prove a causative link with resistance to IBV.

Keywords

ChickenInfectious bronchitis virusMicroarrayDisease resistanceCandidate gene

Background

Infectious bronchitis (IB) is a highly contagious respiratory disease of chickens first described in the USA in the 1930’s [13]. Clinical signs include: coughing, sneezing, rales and nasal discharge. The disease can also affect the reproductive organs, which leads to a decrease in egg quality and production, thus making it a major cause of economic losses within the poultry industry [4]. The causative virus, Infectious Bronchitis Virus (IBV) is a coronavirus, which is an enveloped virus with a single positive-stranded RNA genome, which replicates in the host cell cytoplasm [5]. Proteins encoded by IBV include the viral RNA polymerase, structural spike proteins, membrane and nucleocapsid and various other regulatory proteins. The spike glycoprotein mediates cell attachment and plays a significant role in host cell specificity [6].

The existence of many different IBV serotypes, which are not cross-protective means that control of IB, is very difficult. Mortality is usually fairly low (~5 %), however some strains of the virus can also cause nephritis meaning that, depending on strain, mortality can be greater than 50 % [7, 8] or even up to 80 % with some Australian isolates [9]. IBV infection leaves birds more susceptible to colibacillosis [10] and subsequent bacterial infections can also lead to a high level of mortality [11]. Currently, attenuated live vaccines are used in broilers and pullets, and killed vaccines are used in layers and breeders [12]. However, virus control is very difficult, as there are only a few vaccine types and many different strains of IBV. The virus also continues to mutate rapidly, generating more virulent strains of the disease [1315]. Coronaviruses have now also been detected in other avian species such as turkey, duck, goose, pheasant, guinea fowl, teal, pigeon, peafowl and partridge [4].

The extent to which the virus affects the host is highly dependent on the chicken breed [4] and the MHC B locus is known to play a role in susceptibility to the virus [16]. In this study we attempt to identify non-MHC genes, which may be involved in resistance to IBV. No genetic analyses have thus far been undertaken in order to try and do this and no quantitative trait loci or genes associated with resistance have been determined, so far. Based on differential gene expression in susceptible and resistant lines of chickens, we identify potential candidate genes for disease resistance towards IBV (virulent M41 strain). Building on the previous work by Dar et al. [17] and Wang et al. [18] we used Affymetrix whole-genome chicken microarrays to examine the tracheal gene expression profiles of a line of birds known to be susceptible to IBV infection (line 15I) and a line known to show resistance (line N). We determined the early host response to infection and propose possible candidate genes for involvement in disease resistance towards IBV. Understanding how coronaviruses infect the host and identifying genes involved in resistance is important not only for the poultry industry but also has important implications for human health, as diseases such as SARS are also caused by coronaviruses [19, 20].

Methods

Ethics statement

All animal work was conducted according to UK Home Office guidelines and approved by the Roslin Institute Animal Welfare and Ethical Review Body.

Experimental animals

The lines used in these experiments are an IBV susceptible line – line 15I (inbred White Leghorn strain) [21] and an IBV resistant line – line N (non-inbred Cornell strain). Line 15I was developed at East Lansing in the USA in the 1940s [22] and Line N at Cornell, USA in the 1960s [23]. The lines have since been maintained at the Institute for Animal Health in Compton, UK. Two-week-old chicks from each line (15I and N) were separated into two experimental rooms, with ad libitum access to food and water. In one room, 54 birds (27 from each line) were infected with 4 log10 CID50 (104 CID50) of virulent IBV-M41 strain in a total of 100 μl of 0.2 % BSA in PBS equally by intra nasal and ocular routes. In the other room, 54 control birds (27 from each line) received 100ul PBS via the same route. Trachea samples (upper half) were collected at 2, 3 and 4 days post-infection (9 individual birds from each line at each time point). The trachea of infected and control birds from each line were analysed for viral load using Taqman real-time quantitative RT-PCR assays.

RNA preparation

Tissue samples (~30 mg) were stabilized in RNAlater (Ambion, Life Technologies, Paisley, UK) and disrupted using a bead mill (Retsch MM 300, Retsch, Haan, Germany) at 20 Hz for 4 min. Total RNA was prepared using an RNeasy kit (Qiagen, Crawley, UK) extraction method as per the manufacturer’s protocol. Samples were resuspended in a final volume of 50 μl of RNAse-free water. Concentrations of the samples were calculated by measuring OD260 and OD280 on a spectrophotometer (Nanodrop, Thermo Scientific, Paisley, UK). Quality of the RNA was checked on a bioanalyser (Agilent Technologies, South Queensferry, UK). An RNA integrity number (RIN) > 8 proved the integrity of the RNA.

Whole genome gene expression microarray hybridization

Biotinylated fragmented cRNA was hybridized to the Affymetrix Chicken Genome Array. This array contains comprehensive coverage of 32,773 transcripts corresponding to over 28,000 chicken genes. The Chicken Genome Array also contains 689 probe sets for detecting 684 transcripts from 17 avian viruses. For each experimental group (control and infected birds in each of the two lines at each of 2, 3 and 4 dpi), three biological replicates (3 RNA pools from 3 birds) were hybridized. Thus, 36 arrays were used in total. Hybridization was performed at 45 °C for 16 hours in a hybridization oven with constant rotation (60 rpm). The microarrays were then automatically washed and stained with streptavidin-phycoerythrin conjugate (SAPE; Invitrogen, Paisley, UK) in a Genechip Fluidics Station (Affymetrix, Santa Clara, CA). Fluorescence intensities were scanned with a GeneArray Scanner 3000 (Affymetrix, Santa Clara, CA). The scanned images were inspected and analyzed using established quality control measures. Array data have been submitted to Array Express (http://www.ebi.ac.uk/arrayexpress/) under the Accession Number E-TABM-1128.

Statistical analysis

Gene expression data generated from the GeneChip Operating Software (GCOS) was normalised using the PLIER (probe logarithmic intensity error) method [24] within the Affymetrix Expression Console software package. This normalised data was then analysed using the limma and FARMS [25] packages within R in Bioconductor [26]. Probes with a False Discovery Rate (FDR) value <0.05 and a fold change ≥1.5 were deemed to be biologically significant.

Analysis of differentially-expressed genes

In order to determine which biological pathways are involved in the responses to viral infection, we analysed our differentially-expressed (DE) genes using Pathway Express [27, 28] which uses KEGG pathways [29] to pictorially display up/down regulation of genes. (NB. These diagrams are based on the human pathways and so are not completely representative of the chicken pathways). Genes differentially expressed during the host response (FDR <0.05) were analysed against a reference background consisting of all genes expressed in the experiment. Factors considered by Pathway Express include the magnitude of a gene’s expression change and its position and interactions in any given pathway, thus including an ‘impact factor’ when calculating statistically significant pathways. Anything with a p-value <0.25 is deemed significant when using this software. Use of the Ingenuity Pathway Analysis (IPA) program [30] revealed which canonical pathways are being switched on by IBV infection in the host (with Benjamini-Hochberg multiple testing correction) and allowed us to analyze the gene interaction networks involved in the host response. Genes were clustered by similar expression pattern and analysed for enriched GO-terms and transcription factor binding sites (TFBS) using Expander (v5.2) [31]. Normalised expression data from control samples were compared with infected samples to examine the host response to IBV infection. Enrichment analysis of particular GO terms or TFBS within clusters was done using the TANGO and PRIMA functions, respectively, within the Expander package.

Viral quantification and specific gene expression analysis by quantitative real-time PCR

Taqman real-time quantitative RT-PCR (qRT-PCR) was used to quantify viral RNA levels and for confirmation of the microarray results for the mRNA levels of selected genes. This was performed on 3 replicate pools of 3 samples (9 birds). Primers (Sigma) and probe (PE Applied Biosystems, Warrington, UK) (Table 1) were designed using Primer Express (PE Applied Biosystems). Briefly, the assays were performed using 2 μL of total RNA and the Taqman FAST Universal PCR Master Mix and one-step RT-PCR mastermix reagents (PE Applied Biosystems) in a 10 μL reaction. Amplification and detection of specific products were performed using the Applied Biosystems 7500 Fast Real-Time PCR System with the following cycle profile: one cycle at 48 °C for 30 min and 95 °C for 20 sec, followed by 40 cycles at 95 °C for 3 sec and 60 °C for 30 sec. Data are expressed in terms of the cycle threshold (Ct) value, normalised for each sample using the Ct value of 28S rRNA product for the same sample, as well described previously [3234]. Final results are shown as 40-Ct using the normalised value, or as fold-change from uninfected controls.
Table 1

Primers used in QRT-PCR analysis

Gene

Forward primer

Reverse primer

Probe primer

Opt. primer conc.

Probe (μM)

GenBank

28S

GGCGAAGCCAGAGGAAACT

GAC GACCGATTTGCACGTC

AGGACCGCTACGGACCTCCACCA

0.6

5

FM165415

C1S

GCGCAAAGGCTGGAAAATAC

TCAAGAACAGAATTGGGAGTGACA

TACTATGCTGAACCCATAACCTGTCTCCCG

0.6

5

NM_001030777

CCL13

CAGAGCCTGGCCCAGAGA

TGTCCATTTTGATTCTTCTGGTATG

CTGTGCCTGACAAGTGCTGCTTCAACTT

0.2

5

XM_415779

CCLi7 (ah221)

CACAACCTGCTGCTTCTCCTATG

TGTAGGCGGAGGCAATGAG

TCAACGTCCCGTCCCACGCA

0.2

5

AY037860

CD38

GCTTGATGGGCTTTCATGGT

CACATTCACTCCATTTTGGACAA

ACCCCTCAGCTCCAGGAATCAACTATGAA

0.6

5

XM_420774

CLU

TGAGTCAGAATCCCGTAACTTCAG

GCAGTCCACAGCCAAGATCTC

AGATCCGGCGCAACTCGGCC

0.2

5

NM_204900

COX11

TGGGATCTCCACCTACAACGTA

CAGCCACTGTTCTTCAAAACAAA

TGCCCTTCGAAGCAGGACAGTACTTCA

0.4

5

XM_001233972

DDT

GGCCCCGAGCGGATT

CATGACTGTTCTGTTCTTGCCAAT

CATTCGCTTTTACCCGCTGGAGCC

0.8

5

NM_001030667

FK-506-BP51

CGGAGGATCAAGAGGAAAGGA

CAGAACCCCTCCAGGTGAATT

AAGGCTATTCCAACCCCAACGAAGGTG

0.1

5

NM_001005431

HSC20

GGAAATCATGGAAATCAATGAGAAA

CACCTCTTTGGTCAGTTCTTCTTG

CAGAGCCCGAGAACGACGAGATCC

0.8

5

XR_026662

IFNAR2

TGGTCACTGCATCTCTAAATAAACATT

CTGCAATTGTGATGCCATAATAATC

CATCCCATCAGCCTGGAAATGCATAACT

0.4

5

NM_204858

IGFBP5

GAAGAGCAGCCAGAGGATGGT

GCTTGCACTGCTTCCTCTTGT

CACCTCCCCAACTGCGACCGAAA

0.1

5

XM_422069

IRF-7

ACCCGGACCGCCGTAT

GCCCAGGCCTTGAAGATCTC

CATCCCTTGGAAGCACAACGCCA

0.6

5

NM_205372

MAFK

GCGATGATGAACTCGTGTCAA

TTCAGACGGATGACCTCCTCTT

TCCGTACGGGAGCTGAACCAGCAC

0.4

5

NM_204756

MAP4K4

TGCTATTGAAATGGCTGAAGGA

TCCGTGGGATGAGGAAGAGT

TCCTCCCCTGTGTGACATGCACCC

0.2

5

NM_001031126

MMD2

TGCCACGCACGCATTCT

GGTCATCGGAGAGGACGTAGAG

TCCTGCCCAGCATCCTCGGC

0.4

5

XM_414787

MX1

TGGACTTCTGCAACGAATTGTC

ATCCAGAAGAGTGCTGAAATGTTTG

TTCACCTCCGCAATCCAGCAAGAGA

0.6

5

NM_204609

SRI

TACTATCAGGGCGGGTATGGA

AGCAAAATAACCATACAGAGGATCCT

CAGCTCCAGGAGGCCCATCATTCC

0.1

5

NM_001080865

SUCLG2

AGCTTCCCGGCTGTTCAAT

CATGGTCTGCCATCAGCTTTT

AACCCCTAGACGATGGCTGAATCTGCA

0.1

5

NM_001006141

TLR3

ATCCATGGTGCAGGAAGTTTAAG

GATGGAGTCTCGACTTTGCTCAATA

TGCATCATGCTTTACAGC

1.0

5

JF273967.1

TNFAIP1

GTTGTGGGAAGCACTTTGGAA

TCAACTCCTTGATCTCCTGTCTGT

CCGAGATGACACAATTGCACTTCCAAAA

0.2

5

NM_001030726

TP-D53

TGGCCAAGCTAGAGGATGAAA

TCAGGCTCATGCCAAGTTTTT

ACTAGCAGCCAAAGAAAAGCACCTGATTGA

0.2

5

NM_204215

Results and discussion

Assessment of viral load

Taqman real-time quantitative RT-PCR analysis was used to measure viral load in trachea samples from both control and infected birds from both lines 15I and N. Tracheal tissue was chosen for examination in this study as the target of IBV is the epithelial surface of the respiratory tract. Viral RNA was detected in infected birds, but no significant difference in viral load was detected between lines at any of the days 2, 3 or 4 post infection (Fig. 1). This would indicate that the resistance to the virus seen in Line N is due to how the birds respond to the virus once it has entered the body and is not a measure of how the birds can prevent initial infection by the virus itself. When resistance to IBV infection was originally determined in these lines, it was noted that they were equally susceptible to infection, but a variation in outcome was seen. In line N, 33 % of birds showed air sac lesions whereas 73 % of 15I birds presented lesions. Mortality was 0 in line N, but 47 % within line 15I birds. It was hypothesized that the different lines were producing different immunological responses upon infection [21].
Fig. 1

QRT-PCR measurement of viral load in control and infected birds from lines 15I and N. Mean 40-Ct values are shown with the standard error mean indicated

Host response to IBV infection

Gene expression differences found in the susceptible 15I line between infected and control birds over days 2, 3 and 4 post infection were analysed, with a view to examining the innate host response to infection by IBV. Genes seen to be induced during the host response to infection include C1S, IRF1, STAT1, MX1, TLR3 and CTSS as previously recognised by Guo et al. [35]. We also identified IFIT5, OASL, SCA2, LYG2, ISG12-2, DDX60, IFIH1, IRF7, ZC3HAV1, DHX58, CCli7, IFITM1 and IFITM3 as being up-regulated in response to IBV infection. Few genes are seen to be down-regulated during the early stage of the host response, but those which are include CHAC1 (pro-apoptotic), HBB (implicated in inflammation regulation) and PDK4 (glucose regulation). For a full list of the genes involved in the tracheal immune response (133 DE probes), see Additional file 1: Table S1.

To elucidate which biological pathways are being perturbed during the host response to IBV infection, we analysed our data using Pathway Express [36]. The resulting pathway diagrams are extremely useful in establishing which gene networks are involved in a particular experimental response. As seen in Fig. 2, genes involved in antigen presentation and the Toll-like receptor (TLR) pathway are up-regulated. TLRs identify pathogen associated molecular patterns (PAMPs) and are crucial to the innate immune system. In this study TLR3 is shown to be induced at 3 dpi. TLR3 recognizes double-stranded RNA intermediates produced during viral replication and has previously been shown to be induced in the trachea at this time after IBV infection [37]. Another pathway involved is the phosphatidylinositol signalling pathway (Table 2). Phosphatidylinositol kinases are known to play an important role in the viral life cycle after infection of the host and PI4KB is known to be exploited by coronaviruses for viral entry. The product of PI4KB catalysis is phosphatidylinositol 4-phosphate (PI4P) and coronavirus entry into the host is mediated by the PI4P lipid microenvironment [38]. Genes involved in the complement system are also highlighted as being up-regulated in response to IBV infection. Complement-mediated lysis of viruses is an important facet of the host innate immune system and its role in defence against viral infection [39] – as reflected in the induction of these genes in this study.
Fig. 2

Pathway Express analysis of the host response to IBV infection in the trachea of susceptible birds (Line 15I). Many genes involved in antigen processing and presentation (a) and in the Toll-like receptor pathway (b) can be seen to be up-regulated (shown in red)

Table 2

Pathway Express analysis of the host response to IBV infection

Rank

Pathway name

Impact Factor

Input genes/Genes in Pathway

Corrected gamma p-value

1

Antigen processing and presentation

14.244

6/89

9.93E-06

2

Toll-like receptor signaling pathway

7.927

5/102

0.003221463

3

Notch signaling pathway

7.073

3/47

0.006843371

4

Pancreatic cancer

4.448

3/72

0.06375221

5

Maturity onset diabetes of the young

3.952

1/24

0.095158775

6

Phosphatidylinositol signaling system

3.884

2/76

0.100456001

7

Complement and coagulation cascades

3.841

3/69

0.103946526

8

mTOR signaling pathway

3.652

2/52

0.120669151

9

Acute myeloid leukemia

3.448

2/59

0.141487282

10

Systemic lupus erythematosus

3.332

2/144

0.15474593

11

VEGF signaling pathway

3.126

2/74

0.181102595

Use of Ingenuity Pathway Analysis (IPA) software also allowed us to determine which biological systems are active during the host response. Up-regulated genes are seen to be part of the canonical biological pathways shown in Fig. 3a. Biological processes involving pattern recognition receptors and interferon signalling feature heavily. The interferon response is a powerful antiviral mechanism, which has previously been shown to be involved in the host response after IBV infection. A very early induction of IFN-γ has been reported in splenocytes [40], and in peripheral blood mononuclear cells (PBMCs) and lung leukocytes [41]. IFNB expression has also been reported in trachea between 1 and 2 dpi [42]. We do not see this increase in expression of interferon genes (due to the absence of data earlier than 2 dpi), but we do see the downstream effects, with increased expression of many interferon-induced genes. Specific physiological processes activated upon IBV infection can also be seen in Fig. 3b. The stimulation of various different immune cells is seen along with the indication of reproductive abnormality, which would reflect the problems seen with egg-laying upon IBV infection.
Fig. 3

Ingenuity Pathway Analysis (IPA) of genes responding to IBV infection. a Canonical biological pathways which are activated in the host upon IBV infection (p < 0.05). The line represents the ratio of genes represented within each pathway. b The most highly represented (p < 0.05) physiological functions of genes differentially expressed during the host response to IBV (in the trachea in susceptible birds (Line 15I)). Specific functions within groups are highlighted

In order to cluster genes seen to be involved in the host response to infectious bronchitis into groups with similar expression profiles and probably sharing similar functions or gene regulatory pathways, we utilised the CLICK algorithm within the Expander program [43]. Figure 4a shows the expression profile of genes up-regulated during the response to virus. The Expander program was also used to analyse the Gene Ontology (GO) functional annotations of the genes being differentially expressed. Figure 4b shows the biological process terms, which are significantly enriched in the genes responding during the host response to infection. As would be expected, these include terms like ‘innate immune response’ and ‘antigen processing and presentation’. ‘NAD + ADP-ribosyltransferase activity’ and ‘phosphoinositide binding’ are also highlighted. Transcription factor binding sites present in DE genes which are significantly over-represented were also predicted. Figure 4c shows that genes up-regulated during the host response have a high proportion of IRF7 and ISRE binding sites. IRF7 is a transcriptional activator, which binds to the interferon-stimulated response element (ISRE) in IFN promoters and functions as a molecular switch for antiviral activity.
Fig. 4

Gene expression cluster analysis of the host response in susceptible birds (Line 15I) using the Expander program (http://acgt.cs.tau.ac.il/expander/expander.html). a The expression profile of genes up-regulated during the response to virus. b The GO biological process terms which are significantly enriched during the host response to infection. The frequency of genes of a functional class within the examined set is described as a percentage. c Binding sites for the transcription factors IRF7 and ISRE are seen to be significantly over-represented in genes up-regulated during the host response to IBV infection. The frequency ratio (frequency in set divided by frequency in background) is shown

Differences between susceptible and resistant lines

Analysis of the gene expression differences between infected and control birds across the two lines has provided us with information on how these lines differ in their response to infection. Examination of the gene expression profiles in the control birds of the two different lines also allowed us to identify genes, which are inherently different between the susceptible and resistant birds. It can be seen that there are numerous genes, which show large expression differences between the two lines, even before infection. Dramatic differences in gene expression of certain genes, including DDT, SRI, BLB1, HSCB, BF1, BF2, SUCLG2, MX1 and SRI, which are more highly expressed in the resistant N line compared to the susceptible 15I line are noted (Additional file 2: Table S2 shows all 1930 DE probes) So, it can be seen that these are genes which have inherently different expression levels between susceptible and resistant birds, even before infection occurs. We therefore postulate that some of these genes may play an important role in disease resistance. The potential interactome of IBV has recently been investigated by stable isotope labelling with amino acids in cell culture (SILAC) coupled to a green fluorescent protein-nanotrap pull-down methodology [44]. Host proteins, which bind to the IBV N protein were identified, some of the genes for which, we see as being inherently expressed at higher levels in susceptible birds in this study. These genes include MYH9, CAPRIN1, DHX57, HNRNPH3, RPL27A, FMR1, C22orf28, HNRPDL, SFRS3, RPL31, NPM1 and RPSA. This may therefore be one of the reasons why Line 15I is more susceptible to IBV infection – there are more host proteins to which the virus binds, compared with the resistant Line N.

Upon infection, differences in response are also seen between the two lines. Interestingly, apart from CD38 and CD4 at 3 dpi and FKBP5 at 4 dpi, all other differential gene expression between the lines is seen at 2 dpi in this study (Additional file 3: Table S3). CD38 is a glycoprotein found on the surface of many immune cells including CD4+, CD8+, B lymphocytes and natural killer cells and is a marker of cell activation. It functions in cell adhesion, signal transduction and calcium signalling. CD4 is found on the surface of immune cells such as T helper cells, monocytes, macrophages and dendritic cells. It is a membrane glycoprotein which interacts with MHCII antigens. The protein functions to initiate or augment the early phase of T-cell activation. The protein encoded by FKBP5 is a member of the immunophilin protein family, which play a role in immuno-regulation and basic cellular processes involving protein folding and trafficking. Early defence by the host is a key mechanism for combatting viral infection, and induction of IFNB and other innate genes in response to IBV infection has been shown to peak around 18–36 hr post infection [42].

In this study, genes more highly expressed (or less down-regulated) in the resistant N line at 2 dpi include a number of collagen genes (COL3A1, COL1A2, COL9A1, COL9A2, COL6A1 and COL4A1) and other genes such as ACAN, FSTL1, COMP, EIF3A, STAT3 and IGFBP5. Genes seen to be more highly expressed (or less down-regulated) in the susceptible 15I line include RBM39, MAFB, NNK2, CCN1, MGAT5 and THRAP3. One consequence of IBV infection is the production of poor quality, misshapen eggs by infected birds [45]. Some of the genes previously identified as being important for the creation of a healthy eggshell are seen to be more highly expressed by the resistant N line birds after infection in this study. These genes include COL1A2, CRELD2, HSP90B1, P4HB and ERP29 [46]. For a full list of genes differentially expressed between the two lines in trachea (409 DE probes) see Additional file 3: Table S3.

IPA analysis of genes showing different inherent expression between lines 15I and N shows that the molecular functions of these genes is primarily concerned with their involvement in cell death and cell adhesion (Fig. 5), two processes previously shown to be significant in infected kidneys [47]. When the differential host responses to infection are examined, it is seen that genes involved in proliferation of T-lymphocytes and genes concerned with cell attachment and cytoplasmic organization are more highly expressed in the resistant line N. Other processes significantly involved are apoptosis and necrosis (Fig. 6a), which have been previously documented in IBV-infected Vero cells by Liu et al. [48]. One of the most perturbed biological networks noted in this analysis is that involving genes related to connective tissue disorders and involve many collagen genes. These genes are more highly expressed in susceptible line 15I birds compared to resistant line N birds (Fig. 6b) suggesting that IBV infection might cause more disorder of eggshell formation in this line [49]. The production of poor quality eggs by IBV infected birds may, in part be a reflection of the expression of these kinds of gene networks compared to that seen in resistant birds.
Fig. 5

Ingenuity Pathway Analysis (IPA) of genes showing inherent differential expression between susceptible and resistant control birds. This graph shows the most highly represented (p < 0.05) molecular functions of DE genes

Fig. 6

Ingenuity Pathway Analysis (IPA) of genes showing differential expression between susceptible and resistant lines during the host response to IBV infection. a The most highly represented (p < 0.05) molecular functions of DE genes. b This biological network shows genes associated with connective tissue disorders. Genes shown in red are more highly expressed in the resistant line and those in green have higher expression in the susceptible line

Confirmation of differential gene expression by quantitative real-time PCR

Twenty-one genes were selected for qRT-PCR validation (Table 3). These genes were chosen based on their involvement in the host response and whether they were differentially expressed between the susceptible and resistant lines (either inherently or during the course of infection). Of the 21 genes tested, 19 showed comparable differential expression to that determined by the arrays. However, the results for IFNAR2 and IGFBP5 were not confirmed (Additional file 4: Figure S1).
Table 3

Genes analyzed by qRT-PCR

Gene

Description

Fold change

GenBank

Ensembl

MX1

Interferon-induced GTP-binding protein Mx

6–20

NM_204609

ENSGALG00000016142

C1S

complement component 1, s subcomponent

2–4

NM_001030777

ENSGALG00000014603

IRF7

Interferon regulatory factor 7 (IRF-7)

4–5

NM_205372

ENSGALG00000014297

TLR3

Toll-like receptor 3

4

NM_001011691

ENSGALG00000013468

CCLi7

Chemokine ah221

2–12

AY037860

ENSGALG00000002343

CD38

ADP-ribosyl cyclase 1

4–9

XM_420774

ENSGALG00000014508

FKBP5

FK-506 binding protein 51

5–6

NM_001005431

ENSGALG00000000947

IGFBP5

Insulin-like growth factor binding protein 5

1

XM_422069

ENSGALG00000011468

DDT

D-dopachrome tautomerase

2–3

NM_001030667

ENSGALG00000006350

SRI

sorcin

2

NM_001080865

ENSGALG00000008985

CLU

clusterin

2

NM_204900

ENSGALG00000016587

COX11

Cytochrome c oxidase assembly protein COX11, mitochondrial precursor

2

XM_001233972

ENSGALG00000003017

MMD2

Monocyte to macrophage differentiation factor 2

2

XM_414787

ENSGALG00000004530

IFNAR2

interferon alpha/beta receptor 2

2–3

NM_204858

ENSGALG00000015938

TNFAIP1

tumor necrosis factor, alpha-induced protein 1 (endothelial)

2

NM_001030726

ENSGALG00000005715

MAP4K4

mitogen-activated protein kinase kinase kinase kinase 4

2

NM_001031126

ENSGALG00000008970

MAFK

Transcription factor MafK.

2

NM_204756

ENSGALG00000004189

CCL13

similar to Small inducible cytokine A13 precursor (CCL13)

3

XM_415779

ENSGALG00000024470

TPD52L1

Tumor protein D53 homolog

13–23

NM_204215

ENSGALG00000014843

HSCB

Co-chaperone protein HscB, mitochondrial precursor (Hsc20)

2

XR_026662

ENSGALG00000005706

SUCLG2

succinate-CoA ligase, GDP-forming, beta subunit

2

NM_001006141

ENSGALG00000007652

Potential candidate genes for IBV resistance

Besides knowing that the MHC B locus has a bearing on disease resistance, the lack of any genetic information or identified QTL meant that we had to rely upon the gene expression differences we saw between susceptible and resistant lines to give us clues as to genes potentially involved in resistance to IBV infection. Identifying genes which were expressed at different levels in the two lines of birds highlighted B-locus genes (BLB1, BF1, BF2, B-G) as well as bringing to our attention various other non-MHC genes which, due to their known biology, could be candidates for being involved in resistance to IBV infection (Table 4).
Table 4

Potential candidate genes for involvement in resistance to IBV

Gene

Description

Fold change

GenBank

Ensembl

MX1

Interferon-induced GTP-binding protein Mx.

4–24a

NM_204609

ENSGALG00000016142

C1S

Complement component 1, s subcomponent

2a

NM_001030777

ENSGALG00000014603

IRF7

Interferon regulatory factor 7 (IRF-7).

6a

NM_205372

ENSGALG00000014297

TLR3

Toll-like receptor 3

4a

NM_001011691

ENSGALG00000013468

C1R

Complement component 1, r subcomponent

2a

XM_416518

ENSGALG00000014659

CCLi7

Chemokine ah221

8a

AY037860

ENSGALG00000002343

ISG12-2

Interferon stimulated gene 12-2

16–18a

NM_001001296

ENSGALG00000013575

IFITM3

Interferon induced transmembrane protein 3

4–5a

KC876032

ENSGALG00000004243

CD38

ADP-ribosyl cyclase 1

4b

XM_420774

ENSGALG00000014508

CD4

CD4 protein

4b

NM_204649

ENSGALG00000014477

FKBP5

FK-506 binding protein 51

2b

NM_001005431

ENSGALG00000000947

STAT3

Signal transducer and activator of transcription 3 (acute-phase response factor)

3b

NM_001030931

ENSGALG00000003267

OASL

2'-5'-oligoadenylate synthetase-like

4c

NM_205041

ENSGALG00000013723

DDT

D-dopachrome tautomerase

62–72c

NM_001030667

ENSGALG00000006350

SRI

Sorcin

65–89c

NM_001080865

ENSGALG00000008985

BLB1

MHC Class II beta 1 and 2 domains

10–22c

NM_001044694

ENSGALG00000000141

IFNAR2

Interferon alpha/beta receptor 2

2–3c

NM_204858

ENSGALG00000015938

TPD52L1

Tumor protein D53 homolog

12–14c

NM_204215

ENSGALG00000014843

BCL2L1

BCL2-like - apoptosis regulator

1.7c

NM_001025304

ENSGALG00000006211

FAIM2

Fas apoptotic inhibitory molecule 2

1.7c

XM_004950568

ENSGALG00000027555

CIAPIN1

Cytokine Induced Apoptosis Inhibitor 11

1.7c

NM_001005834

ENSGALG00000005706

HSCB

Co-chaperone protein HscB, mitochondrial precursor (Hsc20).

8–10c

XR_026662

ENSGALG00000005706

BF1

MHC class I antigen B-F minor heavy chain

13–27c

NM_001097530

ENSGALG00000000178

BF2

Major class I glycoprotein precursor

7–8c

NM_001031338

ENSGALG00000000178

SUCLG2

succinate-CoA ligase, GDP-forming, beta subunit

16–32c

NM_001006141

ENSGALG00000007652

aUpregulated in response to infection in the susceptible line

bHigher expression in response to infection in the resistant than in the susceptible line

cInherently higher expression in the resistant line

MX1, C1S, IRF7, TLR3, C1R, CCLi7, ISG12-2 and IFITM3 are all strongly induced during the host response to IBV infection. They are all innate immune genes which could potentially have a role in determining susceptibility to the virus. MX1 and IFITM3 are already established as anti-viral molecules [5052]. CD38, CD4, FKBP5 and STAT3 all show a higher level of expression during the host response in the resistant birds compared to that of the susceptible birds, indicating their involvement in the host defence mechanism. CD38 and CD4, with their role as receptors on immune cells, as described above, are obvious candidates, along with FKBP5 as an immune-regulator. STAT3 is activated by various cytokines and growth factors and functions in cellular processes such as cell growth and apoptosis.

Even before infection, many genes are seen to be highly differentially expressed between lines 15I and N. OASL is an interferon-induced molecule known to have anti-viral activity against certain viruses such as hepatitis C virus. DDT is highly homologous to the macrophage migration inhibition factor, MIF. We have also shown it to be highly differentially expressed in other chicken lines, which are susceptible or resistant to Marek’s Disease virus [53]. IFNAR2 is an obvious candidate prediction, as the interferon response is central to the host’s defence against IBV infection. TPD52L1, BCL2L1, FAIM2 and CIAPIN1 are all known to be involved in regulation of apoptosis, a process seen to be important during IBV infection. HSCB, SRI, and SUCLG2, although not having an obvious potential biological role in disease resistance, are highly differentially expressed between susceptible and resistant lines and should thus be considered as potential candidates.

Conclusions

Resistance to IBV infection is brought about by the immune response after the virus has entered the host and is not due to prevention of initial viral infection. There is a small initial innate response at 2 dpi, with much more gene expression seen at 3 and 4 dpi. Analysis of genes being activated or inhibited upon infection shows that the biological pathways primarily affected during IBV infection include MAPK signalling, those involved in the interferon response and those involving pattern recognition receptors.

Susceptible and resistant lines show a differential host response mostly at 2 dpi. There are also genes which are inherently different between the two lines studied, including many genes, which control the apoptotic potential of the host. These differences seen in gene expression levels, allow us to postulate on many candidate genes for disease resistance. Some potential candidates for involvement in disease resistance include genes already known to confer resistance to other viral infections (MHC-B locus genes, MX1, OASL and IFITM3), genes involved in apoptotic processes (TPD52L1, BCL2L1, FAIM2 and CIAPIN1) and others which could be potential candidates due to their known biology (e.g. DDT and CD4).

Availability of supporting data

Array data have been submitted to Array Express (http://www.ebi.ac.uk/arrayexpress/) under the accession number E-TABM-1128.

Notes

Abbreviations

BSA: 

Bovine serum albumin

CID50

mean chicken infectious dose

DE: 

Differentially expressed

FARMS: 

Factor Analysis for Robust Microarray Summarization

FDR: 

False discovery rate

GCOS: 

GeneChip Operating Software

GO: 

Gene ontology

IB: 

Infectious bronchitis

IBV: 

Infectious bronchitis virus

KEGG: 

Kyoto Encyclopaedia of Genes and Genomes

MHC: 

Major histocompatibility complex

OD: 

Optical density

PBS: 

Phosphate buffered saline

PLIER: 

Probe Logarithmic Error Intensity Estimate

qRT-PCR: 

Quantitative reverse transcription polymerase chain reaction

QTL: 

Quantitative trait loci

RIN: 

RNA integrity number

SARS: 

Severe acute respiratory syndrome

SILAC: 

Stable isotope labelling with amino acids in cell culture

TFBS: 

Transcription factor binding site

Declarations

Acknowledgements

This work was supported by the Biotechnology and Biological Science Research Council (BBSRC), as part of grant numbers BB/D013704/1, BB/D013704/2 and BB/D010705/1. The authors would like to thank Alison Downing (Edinburgh Genomics, Edinburgh, UK) for excellent technical assistance with the Affymetrix microarray experiments.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
The Roslin Institute & R(D)SVS, University of Edinburgh
(2)
The Pirbright Institute, Compton Laboratory

References

  1. Schalk AF, Hawn MC. An apparently new respiratory disease in baby chicks. J Am Vet Med Assoc. 1931;78:413–22.Google Scholar
  2. Beach JR, Schalm OW. Studies of infectious coryza of chickens with special reference to its etiology. Poult Sci. 1936;15:199–206.View ArticleGoogle Scholar
  3. Beaudette FR, Hudson CB. Cultivation of the virus of infectious bronchitis. J Am Vet Med Assoc. 1937;90:51–8.Google Scholar
  4. Cavanagh D. Coronavirus avian infectious bronchitis virus. Vet Res. 2007;38:281–97.View ArticlePubMedGoogle Scholar
  5. Enjuanes L, Sola I, Zúñiga S, Almazán F. Coronavirus Replication and Interaction with Host. In: Mettenleiter TC, Sobrino F, editors. Animal Viruses: Molecular Biology. Norfolk: Caister Academic Press; 2008. p. 149–202.Google Scholar
  6. Casais R, Dove B, Cavanagh D, Britton P. Recombinant avian infectious bronchitis virus expressing a heterologous spike gene demonstrates that the spike protein is a determinant of cell tropism. J Virol. 2003;77:9084–9.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Asif M, Lowenthal JW, Ford ME, Schat KA, Kimpton WG, Bean AG. Interleukin-6 expression after infectious bronchitis virus infection in chickens. Viral Immunol. 2007;20:479–86.View ArticlePubMedGoogle Scholar
  8. Jackwood MW. Review of infectious bronchitis virus around the world. Avian Dis. 2012;56:634–41.View ArticlePubMedGoogle Scholar
  9. Meulemans G, Van den Berg TP. Nephropathogenic avian infectious bronchitis viruses. World Poult Sci. 1998;54:145–53.View ArticleGoogle Scholar
  10. Ariaans MP, Matthijs MG, van Haarlem D, van de Haar P, van Eck JH, Hensen EJ, et al. The role of phagocytic cells in enhanced susceptibility of broilers to colibacillosis after Infectious Bronchitis Virus infection. Vet Immunol Immunopathol. 2008;123:240–50.View ArticlePubMedGoogle Scholar
  11. Matthijs MG, van Eck JH, Landman WJ, Stegeman JA. Ability of Massachusetts-type infectious bronchitis virus to increase colibacillosis susceptibility in commercial broilers: a comparison between vaccine and virulent field virus. Avian Pathol. 2003;32:473–81.View ArticlePubMedGoogle Scholar
  12. Jackwood MW, de Wit S. Infectious Bronchitis. In: Swayne DE, Glisson JR, McDougald LR, Nolan LK, Suarez DL, Nair VL, editors. Diseases of Poultry. 13th ed. UK: Wiley-Blackwell; 2013. p. 139–60.Google Scholar
  13. Worthington KJ, Currie RJW, Jones RC. A reverse transcriptase–polymerase chain reaction survey of infectious IBV infection bronchitis virus genotypes in Western Europe from 2002 to 2006. Avian Pathol. 2008;37:247–57.View ArticlePubMedGoogle Scholar
  14. Ignjatovic J, Gould G, Sapats S. Isolation of a variant infectious bronchitis virus in Australia that further illustrates diversity among emerging strains. Arch Virol. 2006;151:1567–85.View ArticlePubMedGoogle Scholar
  15. Mase M, Kawanishi N, Ootani Y, Murayama K, Karino A, Inoue T. A novel genotype of avian infectious bronchitis virus isolated in Japan in 2009. J Vet Med Sci. 2010;72:1265–8.View ArticlePubMedGoogle Scholar
  16. Banat GR, Tkalcic S, Dzielawa JA, Jackwood MW, Saggese MD, Yates L, et al. Association of the chicken MHC B haplotypes with resistance to avian coronavirus. Dev Comp Immunol. 2013;39:430–7.View ArticlePubMedGoogle Scholar
  17. Dar A, Munir S, Vishwanathan S, Manuja A, Griebel P, Tikoo S, et al. Transcriptional analysis of avian embryonic tissues following infection with avian infectious bronchitis virus. Virus Res. 2005;110:41–55.View ArticlePubMedGoogle Scholar
  18. Wang X, Rosa AJM, Oliverira HN, Rosa GJM, Guo X, Travnicek M, et al. Transcriptome of Local Innate and Adaptive Immunity during Early Phase of Infectious Bronchitis Viral Infection. Viral Immunol. 2006;19:768–74.View ArticlePubMedGoogle Scholar
  19. Drosten C, Günther S, Preiser W, van der Werf S, Brodt HR, Becker S, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med. 2003;348:1967–76.View ArticlePubMedGoogle Scholar
  20. Ksiazek TG, Erdman D, Goldsmith CS, Zaki SR, Peret T, Emery S, et al. A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med. 2003;348:1953–66.View ArticlePubMedGoogle Scholar
  21. Cook J, Otsuki K, Huggins M, Bumstead N. Investigations into resistance of chicken lines to infection with infectious bronchitis virus. Adv Exp Med Biol. 1990;276:491–6.View ArticlePubMedGoogle Scholar
  22. Stone HA. Use of highly inbred chickens in research. USDA Agricultural Research Service Technical Bulletin. 1975, No.1514, Washington, DC.Google Scholar
  23. Cole RK. Studies on genetic resistance to Marek's disease. Avian Dis. 1968;12:9–28.View ArticlePubMedGoogle Scholar
  24. Affymetrix. Guide to Probe Logarithmic Intensity Error (PLIER) Estimation. Santa Clara: Affymetrix I; 2005.Google Scholar
  25. Talloen W, Clevert DA, Hochreiter S, Amaratunga D, Bijnens L, Kass S, et al. I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data. Bioinformatics. 2007;23:2897–902.View ArticlePubMedGoogle Scholar
  26. R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2007. http://www.R-project.org. ISBN 3-900051-07-0.Google Scholar
  27. Khatri P, Sellamuthu S, Malhotra P, Amin K, Done A, Draghici S. Recent additions and improvements to the Onto-Tools. Nucleic Acids Res. 2005;33(Web Server issue):W762–5.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Pathway Express. [http://vortex.cs.wayne.edu/projects.htm]
  29. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Ingenuity Pathway Analysis. [Ingenuity® Systems. www.ingenuity.com]
  31. Expander. [http://acgt.cs.tau.ac.il/expander/expander.html]
  32. Eldaghayes I, Rothwell L, Williams A, Withers D, Balu S, Davison F, et al. Infectious bursal disease virus: strains that differ in virulence differentially modulate the innate immune response to infection in the chicken bursa. Viral Immunol. 2006;19:83–91.View ArticlePubMedGoogle Scholar
  33. Poh TY, Pease J, Young JR, Bumstead N, Kaiser P. Re-evaluation of chicken CXCR1 determines the true gene structure: CXCLi1 (K60) and CXCLi2 (CAF/interleukin-8) are ligands for this receptor. J Biol Chem. 2008;283:16408–15.View ArticlePubMedGoogle Scholar
  34. Rothwell L, Hu T, Wu Z, Kaiser P. Chicken interleukin-21 is costimulatory for T cells and blocks maturation of dendritic cells. Dev Comp Immunol. 2012;36:475–82.View ArticlePubMedGoogle Scholar
  35. Guo X, Rosa AJM, Chen D-G, Wang X. Molecular mechanisms of primary and secondary mucosal immunity using avian infectious bronchitis virus as a model system. Vet Immunol Immunopathol. 2008;121:332–43.View ArticlePubMedGoogle Scholar
  36. Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, et al. A systems biology approach for pathway level analysis. Genome Res. 2007;17:1537–45.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Kameka AM, Haddadi S, Kim DS, Cork SC, Abdul-Careem MF. Induction of innate immune response following infectious bronchitis corona virus infection in the respiratory tract of chickens. Virology. 2014;450–451:114–21.View ArticlePubMedGoogle Scholar
  38. Yang N, Ma P, Lang J, Zhang Y, Deng J, JU X, et al. Phosphatidylinositol 4-Kinase IIIβ Is Required for Severe Acute Respiratory Syndrome Coronavirus Spike-mediated Cell Entry. J Biol Chem. 2012;287:8457–67.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Favoreel HW, Van de Walle GR, Nauwynck HJ, Pensaert MB. Virus complement evasion strategies. J Gen Virol. 2003;84:1–15.View ArticlePubMedGoogle Scholar
  40. Ariaans MP, van de Haar PM, Hensen EJ, Vervelde L. Infectious Bronchitis Virus induces acute interferon-gamma production through polyclonal stimulation of chicken leukocytes. Virology. 2009;385:68–73.View ArticlePubMedGoogle Scholar
  41. Vervelde L, Matthijs MG, van Haarlem DA, de Wit JJ, Jansen CA. Rapid NK-cell activation in chicken after infection with infectious bronchitis virus M41. Vet Immunol Immunopathol. 2013;151:337–41.View ArticlePubMedGoogle Scholar
  42. Kint J, Fernandez-Gutierrez M, Maier HJ, Britton P, Langereis MA, Koumans J, et al. Activation of the chicken type I IFN response by infectious bronchitis coronavirus. J Virol. 2015;89:1156–67.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Sharan R, Maron-Katz A, Shamir R. CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics. 2003;19:1787–99.View ArticlePubMedGoogle Scholar
  44. Emmott E, Munday D, Bickerton E, Britton P, Rodgers MA, Whitehouse A, et al. The cellular interactome of the coronavirus infectious bronchitis virus nucleocapsid protein and functional implications for virus biology. J Virol. 2013;87:9486–500.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Crinion RA. Egg quality and production following infectious bronchitis virus exposure at one day old. Poult Sci. 1972;51:582–5.View ArticlePubMedGoogle Scholar
  46. Brionne A, Nys Y, Hennequet-Antier C, Gautron J. Hen uterine gene expression profiling during eggshell formation reveals putative proteins involved in the supply of minerals or in the shell mineralization process. BMC Genomics. 2014;15:220.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Cong F, Liu X, Han Z, Shao Y, Kong X, Liu S. Transcriptome analysis of chicken kidney tissues following coronavirus avian infectious bronchitis virus infection. BMC Genomics. 2013;14:743.PubMed CentralView ArticlePubMedGoogle Scholar
  48. Liu C, Xu HY, Liu DX. Induction of caspase-dependent apoptosis in cultured cells by the avian coronavirus infectious bronchitis virus. J Virol. 2001;75:6402–9.PubMed CentralView ArticlePubMedGoogle Scholar
  49. Nii T, Isobe N, Yoshimura Y. Effects of avian infectious bronchitis virus antigen on eggshell formation and immunoreaction in hen oviduct. Theriogenology. 2014;81:1129–38.View ArticlePubMedGoogle Scholar
  50. Goodbourn S, Didcock L, Randall RE. Interferons: cell signalling, immune modulation, antiviral response and virus countermeasures. J Gen Virol. 2000;81:2341–64.View ArticlePubMedGoogle Scholar
  51. Haller O, Frese M, Kochs G. Mx proteins: Mediators of innate resistance to RNA viruses. Rev Sci Technol. 1998;17:220–30.Google Scholar
  52. Perreira JM, Chin CR, Feeley EM, Brass AL. IFITMs restrict the replication of multiple pathogenic viruses. J Mol Biol. 2013;425:4937–55.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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;85:11146–58.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Smith et al. 2015

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