Literature DB >> 21645317

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

Anna Slawinska1, Andrzej Witkowski, Marek Bednarczyk, Maria Siwek.   

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.

Entities:  

Year:  2011        PMID: 21645317      PMCID: PMC3108232          DOI: 10.1186/1753-6561-5-S4-S36

Source DB:  PubMed          Journal:  BMC Proc        ISSN: 1753-6561


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

SymbolIDNameChMetabolic PathwayGene Function
BLNK395733B cell linker6BCRB-cell development
CARD11416476caspase recruitment domain family, member 1114BCR, TCR, NFκBNFκB activation
CASP7423901caspase 7, apoptosis-related cysteine peptidase6BCR, TNFαApoptosis
CAT423600Catalase5NFκBRegulation of NFκB activity
CD59423148CD59 molecule, complement regulatory protein5T cellsT cell activation, complement system inhibition
CD7417346T-cell antigen CD7 precursor18T cellsT cell activation, T and B cell interaction, component of mature T cells
CD82423172CD82 molecule5NFκB, p53Binding of proteins in cell membrane
CIITA427676class II, major histcompability complex, transactivator14TLR, MHCLRR binding, MHCII transcription activation
CXCL12395180chemokine (C-X-C motif) ligand 126ILLeukocyte activation, T cell proliferation, chemotaxis
FADD423146FAS (TNFRSF6)-associated via death domain5NFκBApoptosis, NFκB cascade activation, early development of T cells
FAS395274TNF receptor superfamily, member 66TNFα, Fas, B and T cellsIg production, immune response with (B cells) Homeostasis between B I T cells
FGF10395432fibroblast growth factor 10ZNFκB, MAPKTLR activation, inflammatory cytokine secretion (with APC)
FGF8396313fibroblast growth factor 86MAPKMAPK cascade activation
FOS396512v-fos FBJ murine osteosarcoma viral oncogene homolog5TLR, BCR, TCR, MAPK, JNK, ILSynthesis of AP-1 transcription factor
IGSF6771906immunoglobulin superfamily, mem. 614B and T cellsMembrane receptor of T and B cells
IL20RB768437interleukin 20 receptor beta14Jak-STAT, ILT and B cells proliferation and differentiation
IL21R416586interleukin 21 receptor14Jak-STAT, ILT and B cells proliferation and differentiation
IL31RA427140interleukin 31 receptor AZMAPK, Jak-STAT, ILMAPKKK cascade, cytokine and chemokine signal transduction, monocyte and macrophage differentiation
IL4R416585interleukin 4 receptor14T cells, ILTh2 lymhocyte differentation, cytokine receptor
IL6ST395684interleukin 6 signal transducerZILFragment of cytokine receptor complex
IL9R416587interleukin 9 receptor14Jak-STAT, ILJak and STAT activation, cytokine receptor
JAK2374199Janus kinase 2ZJak-STAT, ILCytokine signalling
LITAF374125lipopolysaccharide induced TNF factor14TNFαTNFα expression
MAP2K3416496Mitogen activated protein kinase kinase 314MAPK, TLR, JNK, Fc, p38, TNFα, Jak-STAT, TRAILMAPKKK cascade
MAP2K4417312Mitogen activated protein kinase kinase 418MAPK, TLR, Fas, JNK, Fc, TCR, Jak-STAT, TRAILMAP kinase activation, in response to different stimuli, survival signal for T cells
MAP3K1427144mitogen activated protein kinase kinase kinase 1ZMAPK, TLR, Fas, JNK, Fc, p38, NFκB, TCR, BCR, INFγ, TRAIL, TNFαIntegration of enzyme fosforylation in response to different factors
MAP3K 13424876mitogen-activated protein kinase kinase kinase 139MAPK, JNKActivation of different MAP kinases
MAPK8 IP3426986mitogen-activated protein kinase 8 interacting protein 314MAPK, JNKMAPK and JNK integration
NFKBIA396093nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha5TLR, BCR, TCR, NFκBNFκB Inhibitor
PDCD4374191programmed cell death 4 (neoplastic transformation inhibitor)6JNKNegative JNK regulation, expression of the gene under control of T cells
RAG2423165recombination activating gene 25B and T cellsB and T cells differentiation, gene conversion in Ig
RBP4396166retinol binding protein 4, plasma6B cellsActivation of Ig secretion
SOCS1416630supressor of cytokine sygnalling 114Jak-STAT, ILInhibition of cytokine secretion & Jak-STAT cascade
TCF7L2395508Transcription factor 7-like 26WNTWNT signalling
TGFB3396438transforming growth factor, beta 35MAPK, TGFβ, GPCRMAPK activation, growth factor activity
TNFRSF13B770275TNF receptor superfamily, member 13B14AP-1, NFκB, TNFKey role in humoral immune response
TRAF6423163TNF receptor-associated factor 65TNF, TLR, IL, NFκB, TCRSignal transduction in many pathways, Th1 immune response, T cell activation
TRAF7416555TNF receptor-associated factor 714TNFMAPKKK cascade activation

Gene symbol, ID and name according to NCBI database; Ch - chromosome number, Metabolic Pathway and Gene Function based on GO and InnateDB.

Positional and functional candidate genes associated with innate humoral immune response 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.
  15 in total

1.  KEGG: kyoto encyclopedia of genes and genomes.

Authors:  M Kanehisa; S Goto
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  NFkappaB-dependent signaling pathways.

Authors:  Xiaoxia Li; George R Stark
Journal:  Exp Hematol       Date:  2002-04       Impact factor: 3.084

3.  Creating the gene ontology resource: design and implementation.

Authors: 
Journal:  Genome Res       Date:  2001-08       Impact factor: 9.043

4.  The avian Toll-Like receptor pathway--subtle differences amidst general conformity.

Authors:  Paul Cormican; Andrew T Lloyd; Tim Downing; Sarah J Connell; Dan Bradley; Cliona O'Farrelly
Journal:  Dev Comp Immunol       Date:  2009-04-24       Impact factor: 3.636

5.  Defects of B-cell lymphopoiesis and bone-marrow myelopoiesis in mice lacking the CXC chemokine PBSF/SDF-1.

Authors:  T Nagasawa; S Hirota; K Tachibana; N Takakura; S Nishikawa; Y Kitamura; N Yoshida; H Kikutani; T Kishimoto
Journal:  Nature       Date:  1996-08-15       Impact factor: 49.962

6.  Requirement for B cell linker protein (BLNK) in B cell development.

Authors:  R Pappu; A M Cheng; B Li; Q Gong; C Chiu; N Griffin; M White; B P Sleckman; A C Chan
Journal:  Science       Date:  1999-12-03       Impact factor: 47.728

7.  Retinol and retinol-binding protein: gut integrity and circulating immunoglobulins.

Authors:  L Quadro; M V Gamble; S Vogel; A A Lima; R Piantedosi; S R Moore; V Colantuoni; M E Gottesman; R L Guerrant; W S Blaner
Journal:  J Infect Dis       Date:  2000-09       Impact factor: 5.226

8.  A critical role for IL-21 in regulating immunoglobulin production.

Authors:  Katsutoshi Ozaki; Rosanne Spolski; Carl G Feng; Chen-Feng Qi; Jun Cheng; Alan Sher; Herbert C Morse; Chengyu Liu; Pamela L Schwartzberg; Warren J Leonard
Journal:  Science       Date:  2002-11-22       Impact factor: 47.728

9.  Different levels of natural antibodies in chickens divergently selected for specific antibody responses.

Authors:  Henk K Parmentier; Aart Lammers; Jan J Hoekman; Ger De Vries Reilingh; Ilja T A Zaanen; Huub F J Savelkoul
Journal:  Dev Comp Immunol       Date:  2004-01       Impact factor: 3.636

10.  In silico identification of components of the Toll-like receptor (TLR) signaling pathway in clustered chicken expressed sequence tags (ESTs).

Authors:  David J Lynn; Andrew T Lloyd; Cliona O'Farrelly
Journal:  Vet Immunol Immunopathol       Date:  2003-06-20       Impact factor: 2.046

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.