Association Analysis of SNPs in the 3’-UTR of the MyD88 Gene with Resistance to Salmonella Pullorum Infection in Chickens

Peng Ren1, Xian-Qing Liu1, Chao-Wu Yang2,3, Hua-Rui Du2,3, Xiao-Song Jiang2,3 and Yi-Ping Liu1*

1Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu Campus, Chengdu 611130, China

2Sichuan Animal science academy, Chengdu 610066, China

3Animal Breeding and Genetics Key Laboratory of Sichuan Province, Chengdu 610066, China.

ABSTRACT

Pullorum disease is caused by Salmonella Pullorum and does a great loss to the poultry industry. As a universal innate immune gene, Myeloid differentiation primary response gene 88 (MyD88) can activate the nuclear factor-κB (NF-κB) pathway and regulate downstream gene expression. Single nucleotide polymorphisms (SNPs) in the coding regions (CDS) of the MyD88 gene have also been reported to be associated with inter-subject differences in responses to Salmonella Pullorum infection in chickens. However, whether the 3’-untranslated region (3’-UTR) of the MyD88 gene is associated with resistance to Salmonella Pullorum infection still remains unknown. In this study, a total of eight SNPs, including three novel mutations [SNP4 (A4812316G), SNP6 (C4813363A) and SNP7 (C4813618T)] and five known loci, were found within 3292 bp sequenced fragments. The allele frequency and genotype frequency of SNP4 (A4812316G) were found to be significantly different (P<0.05) between the case and control groups. However, no significant differences were found in the haplotypes of SNP1 and SNP2 (P>0.05). These results suggest that SNP4 (A4812316G) in the 3’-UTR of the MyD88 gene is linked to genetic resistance to Salmonella Pullorum infection and may provide an important reference for the marker-assisted selection of chickens during disease-resistance breeding.

Article Information

Received 04 June 2019

Revised 30 July 2019

Accepted 06 August 2019

Available online 24 February 2020

Authors’ Contribution

PR designed the study and wrote the article. XQL involved in the data analysis. CWY and HRD helped in sample collection. XSJ helped in preparation of the manuscript. YPL helped in conceiving and designing the study.

Key words

MyD88 gene, 3` Untranslated region, Salmonella Pullorum, SNPs, Polymorphisms

DOI: https://dx.doi.org/10.17582/journal.pjz/20190604100628

* Corresponding author: [email protected]

0030-9923/2020/0003-0849 $ 9.00/0

Copyright 2020 Zoological Society of Pakistan

INTRODUCTION

Salmonella Pullorum is one of the common bacterial diseases that limited the rapid development of the poultry industry (Barrow et al., 2003). Though many studies related to genetic resistance have been carried out during the past few decades (Severens, 1944; Li et al., 2010; Li et al., 2018), the identification of resistance genes and the determination of their underlying mechanisms against Salmonella Pullorum infection remain to be studied (Wigley, 2004). However, in recent years, accumulating evidence has shown that innate immune genes may play important roles in Salmonella Pullorum infection (Peng et al., 2010; Ramasamy et al., 2014; Qiu et al., 2017). Some evidence showed the importance of the MyD88 gene in Salmonella Pullorum infection (Arques et al., 2009; Li et al., 2010; Liu et al., 2015). Other research also showed that deletion of the MyD88 gene could substantially delay the innate immune response (Aderem and Ulevitch, 2000). The multiprotein complex of the MyD88-IRAK (Interleukin-1 receptor-associated kinase) family is used by receptors of Interleukin-1 (IL-1), Interleukin-18 (IL-18) and Interleukin-33 (IL-33), which are important for inflammation and host defenses (Netea et al., 2012).

During infection, the host inflammatory response is initiated by pattern-recognition receptors (PRRs), which recognize pathogen-associated molecular patterns (PAMPs), conserved structures of the pathogenic microorganisms. The toll-like receptor (TLR) family is a major class of PRRs that leads to nuclear factor-κB (NF-κB) translocation and transactivation (Moynagh, 2005). Most TLR signaling involves the adaptor molecule MyD88 (Myeloid differentiation factor 88), and it plays a key role in maintaining the normal response of innate immunity that has been frequently reported during pathogen infections (Naiki et al., 2005). The function of MyD88 gene in the innate immune response against bacterial infection is largely known (Tauszigdelamasure et al., 2002; Serbina et al., 2003; Woods et al., 2008). Adenine- and uridine-rich elements (AREs) have been found in the 3’-UTR of certain messenger RNAs (mRNAs), and due to the function of AREs in the 3’-UTR (Kuersten and Goodwin, 2003; Barreau et al., 2006), it has been speculated that innate immune genes may be involved in regulating transcription and translation in the innate immune system (Sun and Ding, 2006; West et al., 2011). Despite being studied broadly among different species (Stockhammer et al., 2009; Issac et al., 2018), the role of the highly conserved MyD88 gene in the response to Salmonella Pullorum infection remains elusive. Identification of SNPs located outside the typical structure of the MyD88 gene, including the N-terminal death domain, C-terminal toll-interleukin 1 receptor (TIR) domain, and intermediate domain, and further studies focusing on the MyD88 untranslated region are necessary (Netea et al., 2012).

Innate immune-mediated disease resistance is closely related to genetic factors. As a key factor in innate immunity, the relevance of the MyD88 gene to Salmonella Pullorum infection remains unclear. To further explore the potential association between the MyD88 gene and genetic resistance to Salmonella Pullorum infection, 81 case and 90 control samples (infected or uninfected with Salmonella Pullorum) from local Qing Jiao Ma hens were collected to conduct an association study between the SNPs in the 3’-UTR of the MyD88 gene and the resistance to Salmonella Pullorum infection in chickens.

 

MATERIALS AND METHODS

Salmonella Pullorum detection and sample collection

Experimental hens at the age of 300 days are a pure line of local Qing Jiao Ma chickens from the Poultry Breeding Farm, Sichuan Agricultural University. Based on case-control design, the whole blood glass plate agglutination method (SN/T 1222-2003, AQSIQ) was used to test for Salmonella Pullorum infection (Liu et al., 2015), which is quick and less costly, exerting the greatest value on a flock basis. Following common laying hen immunization program, chickens were vaccinated timely of immunization. In this study, 2,200 Qing Jiao Ma hens have been tested at the time of 300 days of age, among which 81 infected subjects were collected as the cases and 90 uninfected subjects were collected as controls. The blood samples were collected from a vein under the wings of the chickens and stored at -20 °C for further use. All procedures carried out in this experiment were reviewed and approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University, China.

DNA extraction and pool construction

Genomic DNA from the blood samples was extracted by using the standard phenol/chloroform method. After the extracted DNA was tested by Nano Drop (ND-2000, Thermo Scientific) as previously described (Liu et al., 2019), the DNA samples were diluted to the level of (100 ± 3) ng/μL. A case DNA pool and a control one were composed of 30 samples that each contains 2μL DNA selected at random, respectively.

Primer design

A primer designing tool in NCBI (http://www.ncbi.nlm.nih.gov/ tools/primer-blast) was used for primer designing, and five primer pairs were designed to cover the 3’-UTR of the MyD88 gene according to the genomic sequence of Gallus gallus (GenBank accession number: NM_001030962). Primer pairs were designed after analysis by Oligo 7 (http://www.oligo.net/downloads.html). The primer sequences are shown in Table I.

PCR amplification and sequencing

Polymerase chain reaction (PCR) amplification was conducted using a 25 μL reaction mixture containing 50-100 ng of DNA, 0.3 μL of each forward and reverse primer, and 15 μL of 2× Taq PCR Master Mix (Tiangen Biotech Co., China). The procedure was carried out with 1 cycle of denaturalization at 96 °C for 4 minutes; 36 cycles of 98 °C for 40 seconds, 50-60 °C (optimal annealing temperature of each primer pair, listed in Table I) for 30 seconds, and 72 °C for 1 minute; and a final cycle of 72 °C for 8 minutes. All PCR products of the DNA pools and individuals were directly sequenced by the Shanghai Sangon Biotechnology Company (Shanghai, China).

Statistical analysis

Sequence variations, compositions and variable sites were identified using Chromas software and DNASTAR software (DNASTAR Inc., Madison, WI, USA). Hardy-Weinberg equilibrium, pairwise linkage disequilibrium (D’) and association analyses were conducted with Haploview software (version 3.32; http://www.broad.mit.edu/mpg/haploview/). The determination of allele frequency differences in the MyD88 gene between the case and control groups were performed by chi-squared test using R software (version 3.0.2, The R Foundation for Statistical Computing). In previous study, odds ratio (OR) and 95% confidence interval calculations were performed to determine the resistance or susceptibility to Salmonella Pullorum infection (Liu et al., 2015).

 

Table I. The primer sequences of the 3’-UTR used for amplification.

Name	Ampl-icon size (bp)	Sequence (5’-3’)	Target region	Production (bp)	Annealing temperature (°C)
P1	23	F: CTACTTAATTCAGCGAGCAATAG	Start - 1978	894	59.0
	20	R: ACAAACTGGACCCACTTAGG
P2	25	F: ACCCTTTAATAGAAACTCAGTCTTG	1869 - 2616	768	54.6
	21	R: CGAGTTTGTGAGCCTACCCTA
P3	20	F: GGTGCTGTTGCTGCTTCCTC	2531 - 3521	1012	54.0
	22	R: CACATCTCAAGTGCCAAACCAC
P4	19	F: CATACCCAACTTGTGCGTT	3479 - 3978	521	52.5
	22	R: ACTCCATTTTGTCATTCAGAGA
P5	20	F: GTAAAATCCAGCTTATGCAC	3907 - End	499	59.6
	20	R: ATTCCTCACTAACACTTCCT

 

Table II. Change in alleles in the 3’-UTR.

Markers	ID	Position	Obs HET	Expt HET	Allele change	HWE (P)	MAF
SNP1	rs317890917	4810191	0.457	0.412	A>G	0.4784	0.330
SNP2	rs14131328	4810253	0.258	0.349	A>G	0.5507	0.327
SNP3	rs14131329	4810257	0.405	0.419	C>T	0.4578	0.354
SNP4	A4812316G	4812316	0.481	0.481	A>G	0.8340	0.342
SNP5	rs14131331	4810276	0.436	0.483	C>T	0.8480	0.368
SNP6	C4813363A	4813363	0.471	0.477	C>A	0.8152	0.409
SNP7	C4813618T	4813618	0.474	0.483	C>T	0.6792	0.111
SNP8	rs312369633	4813635	0.072	0.082	C>T	0.9977	0.146

 

Obs HET, observed heterozygosity; Expt HET, expected heterozygosity; HWE (P), P value of the Hardy-Weinberg equilibrium test; MAF, minimum allele frequency.

 

RESULTS

Sequencing the 3’-UTR of the MyD88 gene

In total, eight SNPs were detected in the 3’-UTR of the MyD88 gene: three novel mutations located in the chicken genome, named A4812316G (SNP4), C4813363A (SNP6) and C4813618T (SNP7), and five known SNPs (http://www.ncbi.nlm.nih.gov/projects/SNP), named SNP1, SNP2, SNP3, SNP5, and SNP8.

The Hardy-Weinberg equilibrium

Eight SNPs were analyzed with the Hardy-Weinberg equilibrium (HWE) test, and the results were shown in Table II. The observed heterozygosity of all SNPs was at a general level as expected. All the eight SNPs fit the assumption of the Hardy-Weinberg equilibrium (P>0.05). The minor allele frequency (MAF) of the SNPs was greater than 0.01.

Allele and genotype frequency of the mutated loci

The results of the allele and genotype frequency analyses of the 8 SNPs in the case and control groups were shown in Tables III and IV. The allelic distributions did not significantly differ between cases and controls in the 3’-UTR except SNP4 (A4812316G, P=0.0315, χ2=4.625). Table III shows that all of the OR values were less than 1, including the data for SNP4. In addition, the genotype distribution of SNP4 was significantly different between the case and control groups in the analysis of resistance to Salmonella Pullorum infection (P<0.05).

Association between haplotypes and susceptibility to Salmonella Pullorum

There were no significant differences in the linkage disequilibrium (LD) structures of the 8 SNPs between the cases and controls (Fig. 1). There was only one block which was in strong linkage disequilibrium state (D>0.8). The analysis of the haplotypes showed that the haplotype

 

Table III. Allele frequency of mutations in the 3’-UTR.

Markers	Alleles		χ2, P value	OR	95% CI
SNP1 (rs317890917)	A	G	χ2=0.115 P=0.7344	0.9249	0.8392-1.1316
Cases	55 (0.340)	107 (0.660)
Controls	58 (0.322)	122 (0.678)
SNP2 (rs14131328)	A	G	χ2=0.048 P=0.8269	0.9508	0.8480-1.1409
Cases	54 (0.333)	108 (0.667)
Controls	58 (0.322)	122 (0.678)
SNP3 (rs14131329)	T	C	χ2=3.029 P=0.0818	0.6739	0.7408-1.0198
Cases	65 (0.401)	97 (0.599)
Controls	56 (0.311)	124 (0.689)
SNP4 (A4812316G)	G	A	χ2=4.625 P=0.0315	0.6088	0.531-0.9759
Cases	116 (0.716)	46 (0.284)
Controls	109 (0.606)	71 (0.394)
SNP5 (rs14131331)	C	T	χ2=1.424 P=0.2327	0.765	0.7689-1.0669
Cases	65 (0.401)	97 (0.599)
Controls	61 (0.339)	119 (0.661)
SNP6 (C4813363A)	C	A	χ2=0.074 P=0.786	0.925	0.5882-1.4942
Cases	135 (0.833)	27 (0.167)
Controls	148 (0.822)	32 (0.178)
SNP7 (C4813618T)	C	T	χ2=1.9 P=0.1681	0.6149	0.3473-1.2097
Cases	148 (0.914)	14 (0.086)
Controls	156 (0.867)	24 (0.133)
SNP8 (rs312369633)	C	T	χ2=2.655 P=0.1032	0.6057	0.8499-1.0163
Cases	29 (0.179)	133 (0.821)
Controls	21 (0.117)	159 (0.883)

 

OR, odds ratio; CI, confidence interval. When the p-value of the Chi-square test is less than 0.05, the maximum value of the 95% CI less than 1 means resistance effect.

groups GG and AA in block one of the 3’-UTR had no significant association with Salmonella Pullorum infection between the cases and controls (Table V).

 

DISCUSSION

Sequencing the 3’-UTR of the MyD88 gene

A previous study revealed that 2 SNPs in the coding region (CDS) of the MyD88 gene were significantly associated with susceptibility to Salmonella Pullorum infection (Liu et al., 2015). In addition, further evidence confirmed that higher expression of TLR signaling activation via MyD88-dependent pathway is more beneficial to chicken mononuclear cells mediated innate immunity (Karnati et al., 2015). Therefore, the 8 SNPs located in the 3’-UTR of the MyD88 gene were predicted to be potential alleles in the follow-up studies.

 

Table IV. Genotype frequency of mutations in the 3’-UTR.

Markers	Genotypes			χ2, P value
SNP1 (rs317890917)	AA	AG	GG	χ2=0.12 P=0.9398
Cases	8 (0.099)	39 (0.481)	34 (0.420)
Controls	8 (0.089)	42 (0.467)	40 (0.444)
SNP2 (rs14131328)	AA	AG	GG	χ2=0.06 P=0.9705
Cases	8 (0.099)	38 (0.469)	35 (0.432)
Controls	8 (0.089)	42 (0.467)	40 (0.444)
SNP3 (rs14131329)	TT	TC	CC	χ2=3.50 P=0.1734
Cases	13 (0.161)	39 (0.481)	29 (0.358)
Controls	11 (0.122)	34 (0.378)	45 (0.500)
SNP4 (A4812316G)	GG	GA	AA	χ2=6.31 P=0.0426
Cases	40 (0.494)	36 (0.444)	5 (0.062)
Controls	36 (0.400)	37 (0.411)	17 (0.189)
SNP5 (rs14131331)	TT	TC	CC	χ2=1.52 P=0.4681
Cases	28 (0.346)	41 (0.506)	12 (0.148)
Controls	39 (0.433)	41 (0.456)	10 (0.111)
SNP6 (C4813363A)	CC	CA	AA	χ2=2.98 P=0.3118
Cases	54 (0.667)	27 (0.333)	0 (0.000)
Controls	61 (0.678)	26 (0.289)	3 (0.033)
SNP7 (C4813618T)	TT	TC	CC	χ2=1.86 P=0.4302
Cases	1 (0.012)	12 (0.148)	68 (0.840)
Controls	2 (0.022)	20 (0.222)	68 (0.756)
SNP8 (rs312369633)	TT	TC	CC	χ2=4.76 P=0.0599
Cases	53 (0.654)	27 (0.333)	1 (0.013)
Controls	71 (0.789)	17 (0.189)	2 (0.022)

 

P, P value of the Chi-square test.

 

Table V. Haplotype analysis of the 8 SNPs.

	Haplotype groups	Frequency (cases)	Frequency (control)	χ2	P value
Block 1	GG	0.660	0.678	0.115	0.7344
	AA	0.333	0.322	0.048	0.8269

 

P, P value of the Chi-square test.

 

Highly conserved in its CDS, the MyD88 gene plays a key role in the innate immune response. Though multiple previous studies have reported that the MyD88 gene may be involved in the response to pathogenic bacteria infection, most of these studies were performed at the gene expression level (Peng et al., 2010; Li et al., 2017). To reveal the role of the MyD88 gene in resistance to Salmonella Pullorum infection, we analyzed the 3’-UTR of the MyD88 gene in local Qing Jiao Ma hens (infected or uninfected with Salmonella Pullorum). Meanwhile, a growing number of studies have shown that the 3’-UTR plays a considerable role in mRNA stability and the efficiency of translation (Cok and Morrison, 2001; Wang et al., 2006). The AREs of the 3’-UTR speed up the degradation of mRNA in Cyclooxygenase-2 (COX-2) (Sureban et al., 2007). Collectively, SNPs of the 3’-UTR in the MyD88 gene have been speculated to have an association with the regulation of the innate immune response to Salmonella Pullorum infection (Tsai et al., 2004).

The Hardy-Weinberg equilibrium

The Hardy–Weinberg principle, also known as the Hardy-Weinberg equilibrium, states that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences, such as mate choice, mutations, selection, genetic drift, gene flow and meiotic drive (Salanti and Ioannidisb, 2008). Recently, Hardy-Weinberg tests performed in marker-disease association studies have been used for genotyping. The results showed that all these 8 SNPs fit the assumption of the HWE, and they can be used for further analysis. When analyzing the MAF of the 8 SNPs, the effects of selection and foreign blood were found to be eliminated from the chicken population. In other words, these SNPs are sporadic mutations and are vital in genetic breeding.

Allele and genotype frequency of the mutated loci

The odds ratio (OR) value is an important index to quantify how strongly the presence or absence of one property is associated with the presence or absence of another property in a given population (Cornfield, 1951). When the p-value of the Chi-square test is less than 0.05 (the OR value is meaningful), the maximum value of the 95% CI less than 1 means resistance effect (Liu et al., 2015). This suggested that the polymorphisms of SNP4 might have a significant correlation with resistance to Salmonella Pullorum infection. Besides the SNPs of MyD88, multiple genotypes of the innate immunity genes, including the Avian β-defensins and Cytokines SNPs, were previously reported to be associated with resistance to Salmonella infection (Sadeyen et al., 2006). It would be quite interesting to unravel the associations between resistance to disease and SNPs in other genes, particular those with the ability to fight against infections (Yu et al., 2018; Zhang et al., 2019).

Association between haplotypes and susceptibility to Salmonella Pullorum

It is known that the association between haplotype and disease is more effective than a single mutation analysis (Durrant et al., 2004). In past decades, haplotypes have repeatedly been used for analyses of the disease associations found in wild and domesticated populations of chickens (Hosomichi et al., 2008). Furthermore, studies have indicated linkage of the ability to respond to Salmonella infection with particular MHC class I or class II haplotypes in inbred lines of chickens (Liu et al., 2002; Eimes et al., 2010). Another study revealed the regulating effect of the 3’-UTR of chMAVS on mRNA by choosing two representative 3’-UTR haplotypes (Yu et al., 2014). It is notable that none of the haplotype groups showed significant associations with Salmonella Pullorum infection in our study. A possible explanation is that the effects of the SNPs in the haploid type groups canceled each other out due to potential interactions between genes, leading to no significant link between haplotypes and disease resistance.

 

CONCLUSION

In this study, we detected a significant novel mutation (SNP4) of the 3’-UTR in the MyD88 gene between the cases and controls. This finding suggests that SNP4 (A4812316G) may have an effect on the individual immune. But further functional studies are necessary to evaluate the molecular mechanism caused by polymorphisms of the 3’-UTR of MyD88 gene. Our results provide some clues to help better understand the potential role of genetic resistance to Salmonella Pullorum infection in poultry breeding.

 

ACKNOWLEDGMENTS

This work was supported by the Open Fund of Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province (Grant No. 2016NYZ0043, 2017JZ0033).

 

Statement of conflict of interest

The authors declare no conflict of interest.

 

REFERENCES

Aderem, A., and Ulevitch, R.J., 2000. Toll-like receptors in the induction of the innate immune response. Nature, 406: 782-787. https://doi.org/10.1038/35021228

Arques, J.L., Hautefort, I., Ivory, K., Bertelli, E., Regoli, M., Clare, S., Hinton, J.C. and Nicoletti, C., 2009. Salmonella induces flagellin- and MyD88-dependent migration of bacteria-capturing dendritic cells into the gut lumen. Gastroenterology, 137: 579-587. https://doi.org/10.1053/j.gastro.2009.04.010

Barreau, C., Paillard, L. and Osborne, H.B., 2006. AU-rich elements and associated factors: are there unifying principles. Nucleic Acids Res., 33: 7138-7150. https://doi.org/10.1093/nar/gki1012

Barrow, P.A., Bumstead, N., Marston, K., Lovell, M.A. and Wigley, P., 2003. Faecal shedding and intestinal colonisation of Salmonella enterica in in-bred chickens; the effect of host genetic background. Epidemiol. Infect., 132: 117-126. https://doi.org/10.1017/S0950268803001274

Cok, S.J. and Morrison, A.R., 2001. The 3’-untranslated region of murine cyclooxygenase-2 contains multiple regulatory elements that alter message stability and translational efficiency. J. biol. Chem., 276: 23179-23185. https://doi.org/10.1074/jbc.M008461200

Cornfield, J., 1951. A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. J. Natl. Cancer I., 32:1269-1275. https://doi.org/10.1016/0021-9681(79)90028-6

Durrant, C., Zondervan, K.T., Cardon, L.R., Hunt, S., Deloukas, P. and Morris, A.P., 2004. Linkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes. Am. J. Hum. Genet., 75: 35-43. https://doi.org/10.1086/422174

Eimes, J.A., Bollmer, J.L., Dunn, P.O., Whittingham, L.A. and Wimpee, C., 2010. MHC class II diversity and balancing selection in greater prairie-chickens. Genetica, 138: 265–271. https://doi.org/10.1007/s10709-009-9417-4

Hosomichi, K., Miller, M.M., Goto, R.M., Wang, Y., Suzuki, S., Kulski, J.K., Nishibori, M., Inoko, H., Hanzawa, K. and Shiina, T., 2008. Contribution of mutation, recombination, and gene conversion to chicken Mhc-B haplotype diversity. Immunology, 181: 3393-3399. https://doi.org/10.4049/jimmunol.181.5.3393

Issac, J.M., Mohamed, Y.A., Bashir, G.H., Al-Sbiei, A., Conca, W., Khan, T.A., Iqbal, A., Riemekasten, G., Bieber, K., Ludwig, R.J., Cabral-Marques, O., Fernandez-Cabezudo, M.J. and Al-Ramadi, B.K., 2018. Induction of hypergammaglobulinemia and autoantibodies by salmonella infection in MyD88-deficient mice. Front. Immunol., 9: 1384. https://doi.org/10.3389/fimmu.2018.01384

Karnati, H.K., Pasupuleti, S.R., Kandi, R., Undi, R.B., Sahu, I., Kannaki, T.R., Subbiah, M. and Gutti, R.K., 2015. TLR-4 signalling pathway: MyD88 independent pathway up-regulation in chicken breeds upon LPS treatment. Vet. Res. Commun., 39: 73-78. https://doi.org/10.1007/s11259-014-9621-2

Kuersten, S. and Goodwin, E.B., 2003. The power of the 3’ UTR: translational control and development. Nat. Rev. Genet., 4: 626-637. https://doi.org/10.1038/nrg1125

Li, J.J., Yang, C.W., Ran, J.S., Jiang, X.S., Du, H.R., Liu, Y.P. and Zhang, L., 2018. Genotype frequency contributions of Mx1 gene in eight chicken breeds under different selection pressures. 3. Biotech, 8: 483. https://doi.org/10.1007/s13205-018-1504-8

Li, P., Xia, P., Wen, J., Zheng, M., Chen, J., Zhao, J., Jiang, R., Liu, R. and Zhao, G., 2010. Up-regulation of the MyD88-dependent pathway of TLR signaling in spleen and caecum of young chickens infected with Salmonella serovar Pulloru. Vet. Microbiol., 143: 346-351. https://doi.org/10.1016/j.vetmic.2009.12.008

Li, X.C., Zhang, P., Jiang, X.S., Du, H.R., Yang, C.W., Zhang, Z.R., Men, S., Zhang, Z.K., Jiang, W. and Wang, H.N., 2017. Differences in expression of genes in the, MyD88, and, TRIF, signalling pathways and methylation of, TLR4, and, TRIF, in Tibetan chickens and DaHeng S03 chickens infected with, Salmonella enterica, serovar Enteritidis. Vet. Immunol. Immunopathol., 189: 28-35. https://doi.org/10.1016/j.vetimm.2017.05.003

Liu, J.B., Zhang, Y., Li, Y., Yan, H.L., Zhang, H.F., 2019. L-Tryptophan enhances intestinal integrity in diquat-challenged piglets associated with improvement of Redox status and mitochondrial function. Animals, 9: 266. https://doi.org/10.3390/ani9050266

Liu, W., Miller, M.M. and Lamont, S.J., 2002. Association of MHC class I and class II gene polymorphisms with vaccine or challenge response to Salmonella enteritidis in young chicks. Immunogenetics, 54: 582-590. https://doi.org/10.1007/s00251-002-0495-z

Liu, X., Ma, T., Wang, H., Sheng, Z., Dou, X., Wang, K., Li, Z., Pan, Z., Chang, G. and Chen, G., 2015. Up-regulation of NLRC5 and NF-κB signaling pathway in carrier chickens challenged with Salmonella enterica Serovar Pullorum at different persistence periods. Indian J. Biochem. Biophys., 52:132-9.

Liu, X.Q., Wang, F., Jin, J., Zhou, Y.G., Ran, J.S., Feng, Z.Q., Wang, Y. and Liu, Y.P., 2015. MyD88 Polymorphisms and Association with Susceptibility to Salmonella Pullorum. Biomed Res. Int., 2015: 692973. https://doi.org/10.1155/2015/692973

Moynagh, P.N., 2005. TLR signalling and activation of IRFs: revisiting old friends from the NF-κB pathway. Trends Immunol., 26: 469-476. https://doi.org/10.1016/j.it.2005.06.009

Naiki, Y., Michelsen, K.S., Schröder, N.W., Alsabeh, R., Slepenkin, A., Zhang, W., Chen, S., Wei, B., Bulut, Y., Wong, M.H., Peterson, E.M., and Arditi, M., 2005. MyD88 is pivotal for the early inflammatory response and subsequent bacterial clearance and survival in a mouse model of Chlamydia pneumoniae pneumonia. J. biol. Chem., 280: 29242-29249. https://doi.org/10.1074/jbc.M503225200

Netea, M.G., Wijmenga, C. and O’Neill, L.A.J., 2012. Genetic variation in Toll-like receptors and disease susceptibility. Nat. Immunol., 13: 535-542. https://doi.org/10.1038/ni.2284

Peng, L., Xia, P.A., Jie, W., Zheng, M.Q., Chen, J.L., Zhao, J.P., Jiang, R.R., Liu, R.R., and Zhao, G.P., 2010. Up-regulation of the myd88-dependent pathway of tlr signaling in spleen and caecum of young chickens infected with Salmonella serovar pullorum. Vet. Microbiol., 143: 346-351. https://doi.org/10.1016/j.vetmic.2009.12.008

Qiu, L.L., Ma, T., Chang, G.B., Liu, X.P., Guo, X.M., Xu, L., Zhang, Y., Zhao, W.M., Xu, Q. and Chen, G.H., 2017. Expression patterns of nlrc5, and key genes in the stat1 pathway following infection with salmonella pullorum. Gene, 597: 23-29. https://doi.org/10.1016/j.gene.2016.10.026

Ramasamy, K.T., Verma, P. and Reddy, M.R., 2014. Toll-like receptors gene expression in the gastrointestinal tract of salmonella, serovar pullorum-infected broiler chicken. Appl. Biochem. Biotechnol., 173: 356-364. https://doi.org/10.1007/s12010-014-0864-8

Sadeyen, J.R., Trotereau, J., Protais, J., Beaumont, C., Sellier, N., Salvat, G., Velgea, P., Lalmanach, A.C., 2006. Salmonella carrier-state in hens: study of host resistance by a gene expression approach. Microbes Infect., 8: 1308-1314. https://doi.org/10.1016/j.micinf.2005.12.014

Salanti, G. and Ioannidis, J.P.A., 2008. Hardy-Weinberg Equilibrium. In: Encyclopedia of parasitology (ed. H. Mehlhorn). 2nd edn. pp. 1844-1846. Springer, GER. https://doi.org/10.1016/B978-008045405-4.00862-4

Serbina, N.V., Kuziel, W., Flavell, R., Akira, S., Rollins, B. and Pamer, E.G., 2003. Sequential MyD88-independent and -dependent activation of innate immune responses to intracellular bacterial infection. Immunity, 19: 891-901. https://doi.org/10.1016/S1074-7613(03)00330-3

Severens, J.M., 1944. A study of the defense mechanism involved in hereditary resistance to pullorum disease of the domestic fowl. J. Infect. Dis., 75: 33-46. https://doi.org/10.1093/infdis/75.1.33

Stockhammer, O.W., Zakrzewska, A., Hegedûs, Z., Spaink, H.P. and Meijer, A.H., 2009. Transcriptome profiling and functional analyses of the zebrafish embryonic innate immune response to Salmonella infection. J. Immunol., 182: 5641-5653. https://doi.org/10.4049/jimmunol.0900082

Sun, D. and Ding, A., 2006. MyD88-mediated stabilization of interferon-γ-induced cytokine and chemokine mRNA. Nat. Immunol., 7: 375-381. https://doi.org/10.1038/ni1308

Sureban, S.M., Murmu, N., Rodriguez, P., May, R., Maheshwari, R., Dieckgraefe, B.K., Houchen, C.W. and Anant, S., 2007. Functional antagonism between RNA binding proteins HuR and CUGBP2 determines the fate of COX-2 mRNA translation. Gastroenterology, 132: 1055-1065. https://doi.org/10.1053/j.gastro.2006.12.031

Tauszigdelamasure, S., Bilak, H., Capovilla, M., Hoffmann, J.A. and Imler, J.L., 2002. Drosophila MyD88 is required for the response to fungal and Gram-positive bacterial infections. Nat. Immunol. 3: 91-97. https://doi.org/10.1038/ni747

Tsai, M., Chen, W. and Chen, H., 2004. Urokinase gene 3’-UTR T/C polymorphism is associated with oral cancer. J. clin. Lab. Anal., 18: 276-279. https://doi.org/10.1002/jcla.20037

Wang, J., Pitarque, M. and Ingelman-Sundberg, M., 2006. 3’-UTR polymorphism in the human CYP2A6 gene affects mRNA stability and enzyme expression. Biochem. biophys. Res. Commun., 340: 491-497. https://doi.org/10.1016/j.bbrc.2005.12.035

West, A.P., Shadel, G.S. and Ghosh, S., 2011. Mitochondria in innate immune responses. Nat. Rev. Immunol., 11: 389–402. https://doi.org/10.1038/nri2975

Wigley, P., 2004. Genetic resistance to Salmonella infection in domestic animals, Res. Vet. Sci., 76: 165-169. https://doi.org/10.1016/S0034-5288(03)00117-6

Woods, A., Soulassprauel, P., Jaulhac, B., Arditi, B., Knapp, A.M., Pasquali, J.L., Korganow, A.S., and Martin, T., 2008. MyD88 negatively controls hypergammaglobulinemia with autoantibody production during bacterial infection. Infect. Immun., 76: 1657-1667. https://doi.org/10.1128/IAI.00951-07

Yu, D.D., Xu, L., Peng, L., Chen, S.Y., Liu, Y.P., and Yao, Y.G., 2014. Genetic variations of mitochondrial antiviral signaling gene (MAVS) in domestic chickens. Gene, 545: 226-232. https://doi.org/10.1016/j.gene.2014.05.029

Yu, L.T., Xiao, Y.P., Li, J.J., Ran, J.S., Yin, L.Q., Liu, Y.P. and Zhang, L., 2018. Molecular characterization of a novel ovodefensin gene in chickens. Gene, 678: 233–240. https://doi.org/10.1016/j.gene.2018.08.029

Zhang, L., Chen, D., Yu, L., Wei, Y., Li, J. and Zhou, C., 2019. Genome-wide analysis of the ovodefensin gene family: Monophyletic origin, independent gene duplication and presence of different selection patterns. Infect. Genet. Evol., 68: 265–272. https://doi.org/10.1016/j.meegid.2019.01.001