Marek’s disease (MD) represents a significant global economic and animal welfare issue. This immunosuppressive disease is responsible for an estimated 2 billion USD annual economic loss to the global poultry industry [1
], through bird mortality, lost egg production, and vaccination costs. The virus responsible, Marek’s disease virus (MDV), is an α-herpes virus that initially infects B-cells, experiences a latency period, and can then proceed to develop as an oncogenic disease after the infection of T-cells [2
]. Furthermore, if birds do not succumb to MDV itself, they are often left severely compromised with secondary infections such as Escherichia coli
]. The commercially utilized vaccines are not sterilizing vaccines. They prevent the formation of tumours, but do not prevent infection by MDV or shedding of the pathogenic virus [4
]. Both vaccine and pathogenic MDVs are found in vaccinated flocks, resulting in the emergence of increasingly more virulent strains [5
]. As more virulent strains emerge, vaccine treatments are becoming less and less effective [6
In the race to keep ahead of this viral evolution, genetic selection is a possible tool to aid in breeding for viral resistance. Indeed, genetic selection following multiple generations of progeny challenge has been shown to improve survival in commercial chicken populations [7
]. Selection in the poultry industry works relatively well, but knowing the causative elements for resistance would provide a route for much more precise selection. However, the underlying genetic changes, and the genes and specific variants impacting this genetic resistance are still largely unknown. For decades, researchers have sought to identify the genes responsible for MDV resistance, with limited success. It has become clear that many quantitative trait locus (QTL)/genes are involved in the resistance phenotype, with resistance not being a simple trait with one or a few major genes but a classic quantitative trait with many genes of small effect, thus making it difficult to identify causal variants [8
MD is also of interest from a clinical perspective, as it can serve as a model for human lymphoma. MDV-induced lymphomas are found to over-express the Hodgkin’s disease antigen CD30, with expression correlating with the viral Meq oncogene [9
]. Indeed, after infection, the chicken CD30 promoter has also been shown to be hypo-methylated [10
]. Identifying the genetic mechanisms underlying MDV resistance is therefore not only of great importance to the poultry industry but will also have implications for increasing our understanding of human cancers.
Different Major Histocompatability Complex (MHC) haplotypes are known to confer different susceptibilities to the virus [11
]. Studies have also reported the influence of non-MHC regions on resistance to MDV [14
]. Several studies have implicated specific genes in MDV resistance/susceptibility. These include GH1
, and PTPN3
]. These studies are usually done, not with relevant commercial lines, but with experimental or inbred lines and examine whole tissues, although recent work has investigated the host response to MDV in specific cells such as macrophages, which are an early target for the virus [21
]. Recent studies on the role of long non-coding RNAs [23
] and microRNAs (in both the host and virus) have also been carried out [25
], including a study of serum exosomes from lymphoma-bearing birds [28
]. In addition, the role of epigenetics in resistance to MDV has been studied, with regions of differential methylation between susceptible and resistant lines of birds highlighted [29
Here, the availability of large-scale, phenotyped commercial populations, genome wide analysis technologies and an F6
advanced intercross line [31
], has given us the opportunity to carry out, for the first time, a high-resolution analysis of genes underlying MDV resistance in commercially relevant populations. We use multiple genetic resources at our disposal, including the F6
population of an advanced full-sib inter-cross line (FSIL) previously analysed in a low-resolution study for MD resistance using microsatellite markers to identify genomic regions associated with survival following MD challenge [31
]. Genomic DNA of the original 10 founder individuals and the subsequently produced F6
was available for fine mapping through genome sequencing, and/or genotyping using a genome-wide 600K SNP chip [32
]. Furthermore, an extensive multi-generation (15) and multi-line (8) collection of DNAs from progeny-challenged males was available to further examine candidate genes and related variants associated with survival in the face of MDV infection.
In this report, we reveal for the first time MD as a true complex trait, controlled by many QTL. Integration of multiple lines of evidence (F6, multi-generation/multi-line collection, host gene expression responses to viral infection, genome annotations, etc.) on a large scale enabled a high-resolution analysis that predicted mutations within genes, miRNAs, and lncRNAs highly associated with MDV response in commercial egg production lines. This analysis not only provides new markers for MD resistance but also reveals more about the biology behind the mechanism of MDV susceptibility, information that should lead to more precise selection strategies in the future.
QTL mapping in an FSIL F6 population phenotyped for survival in the face of MDV challenge, identified 38 QTLR distributed over 19 autosomes. Use of such a resource allowed for the identification of QTLR at a higher resolution than have been mapped previously, thus allowing for easier identification of potential candidate genes. The mapped QTLR, along with genomic sequences of the F0 founder individuals, and transcriptomic information from challenged and control birds, has allowed us to identify genes, miRNAs, lncRNAs, and potentially functional mutations located under these QTLR as candidates for association with progeny mortality from Marek’s disease. Genetic association studies in multiple elite lines have confirmed the significant effects of most of these candidates on MD. Here we will discuss the potential role of some of the most significant candidates.
Many of the genes we have associated with MD response in this study have biological roles clearly relevant to the pathogenesis of MDV infection. One of the primary targets of the virus are B-cells and genes known to be associated with B-cells, include two of our candidates, CD7 and TLR4. CD7 is a surface antigen found on naive and memory T-cells, as well as defined NK cell progenitors, and provides survival signaling through its tyrosine kinase activity in association with T-cell activation and is involved in T-cell/B-cell interactions. The Toll-like receptor, TLR4 is found on the surface of B-lymphocytes and macrophages and drives the induction of IL-1 β, TNFα, and other pro-inflammatory cytokines implicated as being important in this study.
After initial infection and a period of latency, T-cells become infected. Genes related to T-cell signalling pathways include our MD-associated ADAMTS5, CD7, HAVCR1, LAG3, RELT, TIMD4 and TREML2. ADAMTS5 (ADAM Metallopeptidase with Thrombospondin Type 1 Motif 5) encodes a metalloproteinase that plays an important role in inflammation and cell migration. It also has a critical role in T-lymphocyte migration from draining lymph nodes following viral infection. HAVCR1, Hepatitis A Virus Cellular Receptor 1 (T-Cell Immunoglobulin Mucin Receptor 1), is a receptor for TIMD4. HAVCR1 plays a critical role in regulating immune cell activity, particularly regarding the host response to viral infection, while TIMD4 is a T-cell immunoglobulin involved in regulating T-cell proliferation and lymphotoxin signalling. LAG3 (Lymphocyte-Activation Gene 3) belongs to the immunoglobulin superfamily and acts as an inhibitory receptor on activated T-cells. It negatively regulates the activation, proliferation, and effector function of both CD8+ and CD4+ T-cells as well as mediating immune tolerance. RELT is a member of the TNF-receptor superfamily. It can activate the NF-kappaB pathway and selectively bind TNF receptor-associated factor 1 (TRAF1). This receptor acts via CD3 signalling to stimulate T-cell proliferation, suggesting its regulatory role in the immune response. TREML2 (Triggering Receptor Expressed on Myeloid Cells Like 2) is a cell surface receptor that may play a role in both the innate and adaptive immune responses. It interacts with CD276 on T-cells, enhancing T-cell activation.
Once infection of T-cells has occurred, the disease can then proceed to become oncogenic. Once again, we see that many of our MD associated genes have functions that have been implicated in cancer, including BG1
which encodes an Ig-superfamily type I transmembrane receptor-like protein that contains an immuno-receptor tyrosine-based inhibition motif (ITIM). BG1 has previously been documented as conferring MHC-associated resistance to MDV-induced lymphoma [57
]. Other candidates include BRINP1
(silenced in some bladder cancers), CD7
(associated with leukaemia), CSTA
(encodes a stefin that functions as a cysteine protease inhibitor, suggested as a prognostic tool for cancer), and SUPT20H
(a known tumour rejection antigen). FLT3
is another significant candidate, with mutations in this gene being common in acute myeloid leukaemia. FLT3 is involved in activation of various pathways including apoptosis, and proliferation and differentiation of hematopoietic cells.
One of the main pathologies of MD is its effect on the nervous system, and so it is interesting to see that some of our MD associated genes are involved in the function/growth of neurons (SCN4A and CTNNA2). SCN4A (Sodium Voltage-Gated Channel α Subunit 4) encodes one member of the sodium channel α subunit gene family involved in generation and propagation of action potentials in neurons and muscle. CTNNA2 (Catenin α 2) is thought to be involved in the regulation of cell–cell adhesion and differentiation in the nervous system. It is required for proper regulation of cortical neuronal migration and neurite growth.
The remaining MD associated genes are seen to have general roles as immune system genes: C1S, TAP1 and SOCS1. C1S (Complement component 1S) encodes a serine protease component of the complement system which enhances the host antibody immune response. TAP1 (Transporter 1, ATP Binding Cassette Subfamily B Member) is involved in the transport of antigens from the cytoplasm to the endoplasmic reticulum for association with MHC class I molecules. SOCS1 (Suppressor of Cytokine Signalling 1) encodes a protein which functions downstream of cytokine receptors, and takes part in a negative feedback loop to attenuate cytokine signaling via the JAK/STAT3 pathway. All of these candidate genes had more than one test significant at p ≤ 0.05.
We would hypothesize that many of our candidate genes are working in conjunction with each other to confer the resistance phenotype, whether that be through their proximity in the genome (e.g., CSTA, LAG3
on Chr5 and HAVCR1
on Chr13) or their interaction in biological networks (e.g., SOCS1, TLR4
or RELT, LAG3, HAVCR1
). This would support the idea that some traits may be associated with pathway-level interactions as opposed to discrete gene functions [58
Examination of these genes and their significance of association with MDV resistance across the elite lines indicates a few top candidates, namely: the cluster of genes in QTLR5 (CSTA, C1S and LAG3), FLT3 in QTLR10, CTNNA2 in QTLR19 and TAP1 in QTLR32. It is also interesting to note the distribution of associated genes across the different lines of birds. Examination of the coding candidate genes shows that only two genes, FLT3 and CTNNA2, are significant in all three varieties of birds. The genes ADAMTS5 and TAP1 only showed significant association in Plymouth Rock birds, whereas CSTA, LAG3, C1S, SUPT20H and the functional variant in SCN4A identified as significant candidates in White Leghorn and Rhode Island Red breeds. Finally, several genes are only significant in White Leghorns, namely RELT, TRANK1, HAVCR1, TIMD4, SOCS1, TLR4, CD7, TREML2, along with the functional variant in ENSGALG00000003188.
Genes identified in this analysis include many novel candidates for resistance as well as highlighting genes proposed in previous studies. For example CD8B
(T-cell glycoprotein), CTLA4
(immunoglobulin) and CD72
(B-cell associated) are postulated as important lncRNA target genes by You et al. [24
] and are found under our QTLRs (CD8B
—QTLR19), and differentially expressed in our transcriptomic work (CTLA4
). Similarly, ATF2
(involved in carcinogenesis is found in QTLR25) was proposed as an important target for the miRNA gga-mir15b during MDV infection [25
]. Also in QTLR25 we find gga-mir-10b, previously seen to be upregulated in the spleen during MDV infection [27
]. Other potentially interesting miRNA targets include PBEF1
(pre-B-cell enhancing factor) and FCHSD2
(involved in endocytosis) [26
] that lie under QTLR2 and 11, respectively. Further genes previously linked with MDV resistance include GH1
(growth hormone) and CD79B
(B-cell antigen), both of which lie under our QTLR37.
One of the significant aspects of this research is that it utilized large, commercial production relevant lines, and the challenge virus is a very virulent ++ strain, frequently encountered by production birds in the field. In contrast, most previously published MDV resistance research utilizes specialized research lines, many of which are inbred, and selected for differential response to MDV. These studies utilized laboratory strains of the virus, for which commercial production birds now appear to be resistant. Furthermore, this study investigated MD resistance genes in three distinct breeds of chickens, namely White Leghorn, White Plymouth Rock, and Rhode Island Red, not just one laboratory line. Moreover, these MD association studies replicated the results from the FSIL study increasing our confidence in the causal nature of the QTLR, and possibly the genes and variants in MDV resistance. The response to the virus was measured as mortality in a large progeny group (approximately 30 daughters) for over 9000 sires, using pre-existing information that had been developed within a commercially relevant production trait breeding program. This unique approach increases the relevance of the results to application into a commercial breeding program, while simultaneously provides information on the underlying mechanism of general viral resistance applicable to not only birds, but also other species. This information can provide insights into mechanisms for improving resistance or lead to the development of improved commercial vaccines.