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. 2011 Jun 3;5(Suppl 4):S36. doi: 10.1186/1753-6561-5-S4-S36

In silico analysis of candidate genes associated with humoral innate immune response in chicken

Anna Slawinska 1,✉, Andrzej Witkowski 2, Marek Bednarczyk 1, Maria Siwek 1
PMCID: PMC3108232  PMID: 21645317

Abstract

Background

Production and function of natural antibodies (NAbs) constitutes an important mechanism of the humoral innate immunity in vertebrates. The level of NAbs in chicken is heritable and the genetic background has been partly investigated. However, to date the genetic determination of humoral innate immune response in avian species has not been fully described. The goal of this study was to propose a new set of candidate genes with a potential effect on the NAb phenotype for further SNP association study.

Methods

In silico analysis of positional and functional candidate genes covered 14 QTL regions associated with LPS, LTA & KLH NAbs and located on six chromosomes: GGA5, GGA6, GGA9, GGA14, GGA18 and GGAZ. The function of the genes was subsequently determined based on the NCBI, KEGG, Gene Ontology and InnateDB databases.

Results

As a result, the core panel of 38 genes participating in metabolic pathways of innate immune response was proposed. Most of them were assigned to chromosomes: GGA14, GGA5, GGA6 and GGAZ (13, 9, 8 and 5 genes, respectively). These candidate genes encode proteins predicted to play a role in (i) proliferation, differentiation and function of B lymphocytes; (ii) TLR signalling pathway, and (iii) MAP signalling cascade.

Conclusions

Proposed set of candidate genes is recommended to be included in the follow-up studies to model genetic networks of innate humoral immune response in chicken.

Background

Humoral innate immunity in vertebrates that establishes the first barrier against pathogens consists of two basic mechanisms – natural antibodies (NAbs) and complement system. Expanding the knowledge on this field of avian immunology might be of help to overcome the difficulties in poultry industry, struggling constantly with diseases outbreaks eg. Avian Influenza [1]. In chicken, the level of NAbs proved to be heritable [2]. However, the genetic determination of NAbs is not fully described as it lacks information on which genes can be considered as the regulators in the complicated network of NAbs creation and function. This study contributes to the discovery of genetic determination of humoral innate immunity as it lists the proposed positional and functional candidate genes that have the putative impact on the NAb phenotype.

Methods

Chromosomal regions for in silico candidate gene analysis were initially selected based on the location of the QTL associated with the NAb titres directed against LPS (lipopolysaccharide), LTA (lipoteichoic acid) and KLH (keyhole limpet hemocyanine) antigens in chicken. This step was performed based on results from two independent studies, i.e.

• Study 1 – LPS and LTA NAb QTL detection study [3];

• Study 2 – LPS and LTA NAb QTL validation study; KLH NAb detection study (data not published).

Study 2 was carried out within a new chicken reference population, set-up as a F2 cross between commercially selected breed (WL, White Leghorn) and a Polish, unselected native chicken breed (GP, Green-legged Partridgelike). For a candidate gene analysis reported here, the chromosomal regions of interest included QTL associated with LPS and LTA NAb titres that had been detected in study 1 and consecutively validated in study 2 as well as QTL associated with KLH NAb titres that had been detected in study 2. These QTL were located in the following chicken chromosomes: GGA5, GGA6, GGA9, GGA14, GGA18 and GGAZ. The regions of interest were designated based on the physical location of the microsatellite markers flanking the QTLs. The list of candidate genes within the QTL regions was prepared based on NCBI database [4], and gene function was assessed with KEGG [5], InnateDB [6] and Gene Ontology [7]. The genes meeting both the criteria, i.e. location within the QTL regions & function in innate immunity (including signalling pathways and B cell function) were listed in a panel of the candidate genes associated with humoral innate immune response.

Results

The results of the candidate gene analysis are presented in Table 1. Briefly, based on previously described criteria, the total number of 38 candidate genes located on six chromosomes was selected. The highest number of the candidate genes (13 genes) was located on GGA14; 9 genes were found on GGA5 and 8 – on GGA6. Lower number of candidate genes were found on GGAZ (5 genes), on GGA18 (2 genes) and on the GGA9 (1 gene).

Table 1.

Positional and functional candidate genes associated with innate humoral immune response

Symbol ID Name Ch Metabolic Pathway Gene Function
BLNK 395733 B cell linker 6 BCR B-cell development
CARD11 416476 caspase recruitment domain family, member 11 14 BCR, TCR, NFκB NFκB activation
CASP7 423901 caspase 7, apoptosis-related cysteine peptidase 6 BCR, TNFα Apoptosis
CAT 423600 Catalase 5 NFκB Regulation of NFκB activity
CD59 423148 CD59 molecule, complement regulatory protein 5 T cells T cell activation, complement system inhibition
CD7 417346 T-cell antigen CD7 precursor 18 T cells T cell activation, T and B cell interaction, component of mature T cells
CD82 423172 CD82 molecule 5 NFκB, p53 Binding of proteins in cell membrane
CIITA 427676 class II, major histcompability complex, transactivator 14 TLR, MHC LRR binding, MHCII transcription activation
CXCL12 395180 chemokine (C-X-C motif) ligand 12 6 IL Leukocyte activation, T cell proliferation, chemotaxis
FADD 423146 FAS (TNFRSF6)-associated via death domain 5 NFκB Apoptosis, NFκB cascade activation, early development of T cells
FAS 395274 TNF receptor superfamily, member 6 6 TNFα, Fas, B and T cells Ig production, immune response with (B cells) Homeostasis between B I T cells
FGF10 395432 fibroblast growth factor 10 Z NFκB, MAPK TLR activation, inflammatory cytokine secretion (with APC)
FGF8 396313 fibroblast growth factor 8 6 MAPK MAPK cascade activation
FOS 396512 v-fos FBJ murine osteosarcoma viral oncogene homolog 5 TLR, BCR, TCR, MAPK, JNK, IL Synthesis of AP-1 transcription factor
IGSF6 771906 immunoglobulin superfamily, mem. 6 14 B and T cells Membrane receptor of T and B cells
IL20RB 768437 interleukin 20 receptor beta 14 Jak-STAT, IL T and B cells proliferation and differentiation
IL21R 416586 interleukin 21 receptor 14 Jak-STAT, IL T and B cells proliferation and differentiation
IL31RA 427140 interleukin 31 receptor A Z MAPK, Jak-STAT, IL MAPKKK cascade, cytokine and chemokine signal transduction, monocyte and macrophage differentiation
IL4R 416585 interleukin 4 receptor 14 T cells, IL Th2 lymhocyte differentation, cytokine receptor
IL6ST 395684 interleukin 6 signal transducer Z IL Fragment of cytokine receptor complex
IL9R 416587 interleukin 9 receptor 14 Jak-STAT, IL Jak and STAT activation, cytokine receptor
JAK2 374199 Janus kinase 2 Z Jak-STAT, IL Cytokine signalling
LITAF 374125 lipopolysaccharide induced TNF factor 14 TNFα TNFα expression
MAP2K3 416496 Mitogen activated protein kinase kinase 3 14 MAPK, TLR, JNK, Fc, p38, TNFα, Jak-STAT, TRAIL MAPKKK cascade
MAP2K4 417312 Mitogen activated protein kinase kinase 4 18 MAPK, TLR, Fas, JNK, Fc, TCR, Jak-STAT, TRAIL MAP kinase activation, in response to different stimuli, survival signal for T cells
MAP3K1 427144 mitogen activated protein kinase kinase kinase 1 Z MAPK, TLR, Fas, JNK, Fc, p38, NFκB, TCR, BCR, INFγ, TRAIL, TNFα Integration of enzyme fosforylation in response to different factors
MAP3K 13 424876 mitogen-activated protein kinase kinase kinase 13 9 MAPK, JNK Activation of different MAP kinases
MAPK8 IP3 426986 mitogen-activated protein kinase 8 interacting protein 3 14 MAPK, JNK MAPK and JNK integration
NFKBIA 396093 nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha 5 TLR, BCR, TCR, NFκB NFκB Inhibitor
PDCD4 374191 programmed cell death 4 (neoplastic transformation inhibitor) 6 JNK Negative JNK regulation, expression of the gene under control of T cells
RAG2 423165 recombination activating gene 2 5 B and T cells B and T cells differentiation, gene conversion in Ig
RBP4 396166 retinol binding protein 4, plasma 6 B cells Activation of Ig secretion
SOCS1 416630 supressor of cytokine sygnalling 1 14 Jak-STAT, IL Inhibition of cytokine secretion & Jak-STAT cascade
TCF7L2 395508 Transcription factor 7-like 2 6 WNT WNT signalling
TGFB3 396438 transforming growth factor, beta 3 5 MAPK, TGFβ, GPCR MAPK activation, growth factor activity
TNFRSF13B 770275 TNF receptor superfamily, member 13B 14 AP-1, NFκB, TNF Key role in humoral immune response
TRAF6 423163 TNF receptor-associated factor 6 5 TNF, TLR, IL, NFκB, TCR Signal transduction in many pathways, Th1 immune response, T cell activation
TRAF7 416555 TNF receptor-associated factor 7 14 TNF MAPKKK cascade activation
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Gene symbol, ID and name according to NCBI database; Ch - chromosome number, Metabolic Pathway and Gene Function based on GO and InnateDB.

It can be summarized that these candidate genes encode proteins predicted to play a role in:

(i) Proliferation, differentiation and function of B lymphocytes, e.g. CXCL12, BLNK, IL21R, RBP4, CD59, TNFRSF13B;

(ii) TLR signalling pathway, e.g. TRAF6, FADD, NFκBIA, CARD11, FAS, FGF8, TGFB, IL31RA;

(iii) MAP signalling cascade, e.g. MAP2K3, MAP2K4, MAP3K1, MAP3K13, MAPK8IP3.

Discussion

Immune response is a complicated process; encoded by multiple genes organized within the frames of functional networks rather than pathways and regulated by many interactions. However, prior to modelling the most probable genetic network, the information is needed on the genes that can be taken into account and their physiological function.

As mentioned above, the function of the proposed set of candidate genes was associated with three groups of cellular and physiological processes that can hypothetically affect innate humoral immune response in chicken. Briefly, production of antibodies, including NAbs takes place in B cells, stimulated by Th2 cytokines. Therefore, both B and T cells function is a crucial element in antibody release. CXCL12 gene is responsible for B cells proliferation [8]. CXCL12-/- knockout mice produced drastically reduced number of B cells and died during the perinatal period [9]. In turn, BLNK gene affects B cell development, which was completely inhibited in BLNK-/- knockout mouse [10]. Finally, IL21R and RBP4 genes are responsible for maintenance of mature B cells function. Knocked out mice (both IL21R-/- and RBP4-/-) expressed impaired production of antibodies [11,12].

TLR signalling pathway is triggered when molecular patterns (such as LPS or LTA) are recognized. Some of the proposed candidate genes are involved in TLR pathway, just to mention TRAF6 and FADD, as well as genes affecting NFκB expression and function, such as NFκBIA, CARD11, TNFRSF13B and FAS[13-15]. Furthermore, the analysis in silico pointed out a number of genes that activate MAPK cascade, a key signalling pathway initiated by TLR, for example FGF8, TGFB3 and IL31RA[14]. Additionally, the candidate gene set includes such genes as MAP2K3, MAPK8IP3, MAP3K13, MAP2K4 and MAP3K1, which are the members of MAPK signal transduction pathway [15].

Conclusions

Chicken immune response is one of the major areas recently studied in life science research related to livestock. So far, different approaches have been applied to dissect the genetic bases of avian health traits. Rapid development of technology supporting high-throughput genomic studies provided an excellent tool for fast and efficient genotyping. Still, the accurate gene selection can pose a problem. Therefore, the additional criteria, like validated QTL regions may be of assistance to list the proper genes that can be further on evaluated and contribute to genetic network modelling of humoral immune response in chicken. For that reason we proposed a panel of candidate genes related to the level of LPS, LTA & KLH NAbs in chicken.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS performed the analysis and drafted the manuscript; AW made substantial contributions to acquisition of data; MB participated in the design of the study; MS conceived of the study, participated in its design and coordination and helped to draft the manuscript.

Contributor Information

Anna Slawinska, Email: anna.slawinska@utp.edu.pl.

Andrzej Witkowski, Email: andrzej.witkowski@up.lublin.pl.

Marek Bednarczyk, Email: marek.bednarczyk@utp.edu.pl.

Maria Siwek, Email: maria.siwek@utp.edu.pl.

Acknowledgements

The study supported by the State Committee for Scientific Research (grant no. P 06D 012 30) and by the Integrated Regional Development Programme (grant no. SPS.IV-3040-UE/S05/2009)

This article has been published as part of BMC Proceedings Volume 5 Supplement 4, 2011: Proceedings of the International Symposium on Animal Genomics for Animal Health (AGAH 2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/5?issue=S4.

References

  1. Gauthier-Clerc M, Lebarbenchon C, Thomas F. Recent expansion of highly pathogenic avian influenza H5N1: a critical review. Int. J. Avian Sci. 2007;149:201–214. [Google Scholar]
  2. Parmentier HK, Lammers A, Hoekman JJ, de V Reilingh G, Zaanen ITA, Savelkoul HFJ. Different levels of natural antibodies in chickens divergently selected for specific antibody responses. Dev. Comp. Immunol. 2004;28:39–49. doi: 10.1016/S0145-305X(03)00087-9. [DOI] [PubMed] [Google Scholar]
  3. Siwek M, Buitenhuis B, Cornelissen S, Nieuwland M, Knol EF, Crooijmans R, Groenen M, Parmentier H, van der Poel J. Detection of QTL for innate: non-specific antibody levels binding LPS and LTA in two independent populations of laying hens. Dev. Comp. Immunol. 2006;30:659–66. doi: 10.1016/j.dci.2005.09.004. [DOI] [PubMed] [Google Scholar]
  4. National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov
  5. Kanehisa M, Goto S. KEGG: kyoto encyklopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, Gardy JL, Roche FM, Chan THW, Shah N, Lo R, Naseer M, Que J, Yau M, Acab M, Tulpan D, Whiteside MD, Chikatamarla A, Mah B, Munzner T, Hokamp K, Hancock REW, Brinkman FSL. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol. Syst. Biol. 2008;4:218. doi: 10.1038/msb.2008.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Gene Ontology Consortium. Creating the gene ontology resource: design and implementation. Genome Res. 2001;11:1425–33. doi: 10.1101/gr.180801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ma Q, Jones D, Borghesani PR, Segal RA, Nagasawa T, Kishimoto T, Bronson RT, Springer TA. Impaired B-lyphopoiesis, myelopoiesis, and derailed cerebellar neuron migration in CXCR4- and SDF-1-deficient mice. PNAS. 1998;95:9448–53. doi: 10.1073/pnas.95.16.9448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Nagasawa T, Hirota S, Tachibana K, Takakura N, Nishikawa S, Kitamura Y, Yoshida N, Kikutani H, Kishimoto T. Defects of B-cell lymphopoiesis and bone-marrow myelopoiesis in mice lacking the CXC chemokine PBSF/SDF-1. Nature. 1996;382:635–8. doi: 10.1038/382635a0. [DOI] [PubMed] [Google Scholar]
  10. Pappu R, Cheng AM, Li B, Gong Q, Chiu C, Griffin N, White M, Sleckman BP, Chan AC. Requirement for B Cell Linker Protein (BLNK) in B Cell Development. Science. 1999;286:1949–54. doi: 10.1126/science.286.5446.1949. [DOI] [PubMed] [Google Scholar]
  11. Quadro L, Gamble MV, Vogel S, Lima AAM, Piantedosi R, Moore SR, Colantuoni V, Gottesman ME, Guerrant RL, Blaner WS. Retinol and Retinol-Binding Protein. Gut Integrity and Circulating Immunoglobulins. J Infect Dis. 2000;182 Suppl 1:S97–S102. doi: 10.1086/315920. [DOI] [PubMed] [Google Scholar]
  12. Ozaki K, Spolski R, Feng CG, Qi CF, Cheng J, Sher A, Morse HC 3rd, Liu C, Schwartzberg PL, Leonard WJ. A critical role for IL-21 in regulating immunoglobulin production. Science. 2002;298:1630–4. doi: 10.1126/science.1077002. [DOI] [PubMed] [Google Scholar]
  13. Li X, Stark GR. NFκB-dependent signaling pathway. Exp. Hematol. 2002;30:285–96. doi: 10.1016/S0301-472X(02)00777-4. [DOI] [PubMed] [Google Scholar]
  14. Cormican P, Lloyd AT, Downing T, Connell SJ, Bradley D, O'Farrelly C. The avian Toll-Like receptor pathway--subtle differences amidst general conformity. Dev Comp Immunol. 2009;33:967–73. doi: 10.1016/j.dci.2009.04.001. [DOI] [PubMed] [Google Scholar]
  15. Lynn DJ, Lloyd AT, O'Farrelly C. In silico identification of components of the Toll-like receptor (TLR) signaling pathway in clustered chicken expressed sequence tags (ESTs) Vet Immunol Immunopathol. 2003;93:177–84. doi: 10.1016/S0165-2427(03)00058-8. [DOI] [PubMed] [Google Scholar]
  16. Massagué J. Integration of Smad and MAPK pathways: a link and a linker revisited. Genes Dev. 2003;17:2993–7. doi: 10.1101/gad.1167003. [DOI] [PubMed] [Google Scholar]
  17. Liu Y, Shepherd EG, Nelin LD. MAPK phosphatases-regulating the immune response. Nat. Rev. Immunol. 2007;7:202–12. doi: 10.1038/nri2035. [DOI] [PubMed] [Google Scholar]

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