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. 2023 Aug 26;102(12):103036. doi: 10.1016/j.psj.2023.103036

Transgenerational epigenetic inheritance and immunity in chickens that vary in Marek's disease resistance

Yanghua He *,†, Robert L Taylor Jr ‡, Hao Bai §, Christopher M Ashwell ‡, Keji Zhao #, Yaokun Li ǁ, Guirong Sun , Huanmin Zhang ⁎⁎, Jiuzhou Song †,1
PMCID: PMC10568563  PMID: 37832188

Abstract

Marek's disease virus (MDV), a naturally oncogenic, highly contagious alpha herpesvirus, induces a T cell lymphoma in chickens that causes severe economic loss. Marek's disease (MD) outcome in an individual is attributed to genetic and environmental factors. Further investigation of the host-virus interaction mechanisms that impact MD resistance is needed to achieve greater MD control. This study analyzed genome-wide DNA methylation patterns in 2 highly inbred parental lines 63 and 72 and 5 recombinant congenic strains (RCS) C, L, M, N, and X strains from those parents. Lines 63 and 72, are MD resistant and susceptible, respectively, whereas the RCS have different combinations of 87.5% Line 63 and 12.5% Line 72. Our DNA methylation cluster showed a strong association with MD incidence. Differentially methylated regions (DMRs) between the parental lines and the 5 RCS were captured. MD-resistant and MD-susceptible markers of DNA methylation were identified as transgenerational epigenetic inheritable. In addition, the growth of v-src DNA tumors and antibody response against sheep red blood cells differed among the 2 parental lines and the RCS. Overall, our results provide very solid evidence that DNA methylation patterns are transgenerational epigenetic inheritance (TEI) in chickens and also play a vital role in MD tumorigenesis and other immune responses; the specific methylated regions may be important modulators of general immunity.

Key words: epigenetics, transgenerational epigenetic inheritance, immunity, Marek's disease, disease resistance

INTRODUCTION

Marek's Disease (MD), caused by Marek's disease virus (MDV), is a T cell lymphomatous in many birds. Upon infection via inhalation, MDV is taken to various lymphoid organs, such as the spleen, thymus, and bursa of Fabricius. Infected cells carry the virus through the bloodstream to the visceral organs, peripheral nerves, and the feather follicle epithelium. The latently-infected lymphocytes become transformed and proliferate (Baigent et al., 2006; McPherson and Delany, 2016). Viral infection is one of the biggest threats to human and animal health. Since viruses are obligate intracellular pathogens that are highly efficient at infecting host cells, it is crucial to understand genes, genomics segments, and disease resistance and infection processes, which will help us develop new prevention strategies.

Viral disease outcomes have been recognized to have a complex determination, shaped by immunity, and polygenetic effects including epigenetics, and environmental factors (Tian et al., 2013; Yu et al., 2008a,b). Immunological memory is directly related to organisms’ protection mechanisms and improves the host's survival (Netea et al., 2016; Netea et al., 2019; Divangahi et al., 2021); immunity priming is an intriguing phenomenon in invertebrates and has been sporadically reported in vertebrates, usually agricultural animals, for example, fish and mollusks. As we know, furthermore, epigenetics influences host innate and adaptive immune responses through multiple generations (Yuet al., 2008a,b; Lau et al., 2018). Thus, 2 questions arise related to the Lamarckian inheritance of environmentally-induced traits. Does transgenerational epigenetic inheritance phenomena occur over multiple generations? Can epigenetic alterations be transferred to germinal cells which can perpetuate new phenotypes in birds?

In these experiments, we used 2 well-defined chicken inbred parental lines having known responses to MD as well as recombinant congenic strains (RCS) with different combinations of genes from the 2 parental lines. The studies included analyses of DNA methylation in response to MDV exposure. The lines were also compared for v-src tumor growth and antibody response against sheep red blood cells (SRBC) to provide other examples of DNA methylation effects. Integrating multiple outcomes from various lines will advance our knowledge of genomic and epigenomic effects in poultry immune responses.

MATERIALS AND METHODS

Experimental Animals

A total of 28 individuals were used in the study, including 4 chickens with 2 males and 2 females from each of Line 63, Line 72, and 5 RCS (C, L, M, N, and X). All lines were developed and have been maintained at the USDA, Agriculture Research Service, Avian Disease and Oncology Laboratory (ADOL), East Lansing, Michigan, USA. All animals were handled according to a protocol approved by the ADOL Animal Care and Use Committee. Peripheral blood from MDV-free chickens was collected at the 15th month after which genomic DNA was extracted for further studies.

Phenotypic and MD Incidence

Chickens’ genetic resistance to MD is commonly evaluated with MD incidence post-MDV challenge, that is, a percentage of birds developed MD within each line following MDV exposure. Therefore, it is an average measurement for each line, not for each individual animal (Figure S1). Based on these average MD incidences within a group, Line 63 is relatively resistant to MD, whereas Line 72 is highly susceptible. The RCSs vary in MD resistance (Vallejo et al., 1997; Bacon et al., 2000,2001; Chang et al., 2010). Disease incidence data were analyzed by SAS 9.4 ANOVA (P < 0.05) using Bonferroni's method.

DNA Extraction and MBD-Seq Library Preparation

Genomic DNA from 4 individuals with 2 males and 2 females for each of the chicken lines was extracted using the Wizard Genomic DNA purification kit (Promega, A1120). DNA concentration was measured by the Qubit dsDNA Broad-Range Assay (Invitrogen, Q32850). DNA samples of a male and a female chicken from each of the line were randomly selected equally pooled for MBD-seq library constructions with biological duplicates.

MBD-seq method was used to identify methylated DNA regions. Methylated fragments were enriched with MethylCap kit (Diagenode, C02020010), and sequencing library constructions were conducted as previously described (Carrillo et al., 2015). Finally, cluster generation and sequencing analysis were performed on an Illumina Hiseq 2000 following the manufacturer's protocol, and single end reads with 50bp length were generated.

DNA Methylation Analysis

Sequence files were examined for quality assurance. The first 4 bps and the last 6bps from the original 50bp of reads were trimmed to control the mapping quality. After quality confirmation, trimmed reads were aligned to the galGal4 reference genome obtained from the UCSC browser (http://genome.ucsc.edu) with Bowtie 1.2.0. For data manipulation, filtration, and format conversion, a combination of procedures available in SAMtools and BEDtools were applied. Datasets of 2 biological replicates were merged and sorted by SAMtools, and duplicated reads were removed by rmdup in SAMtools. Bam files were converted to bedgraph files using the genomeCoverageBed package with -scale parameter to normalize after which the normalized bedgraph files were visualized on the Galaxy visualization platform.

The peak-calling step was applied individually for each sample using MACS1.4 (Zhang et al., 2008) based on sam files. Identification of the differentially methylated regions (DMRs) was accomplished by implementing the DiffBind R package (Ross-Innes et al., 2012). For normalization, the default method TMM (Trimmed Mean of M-values) that subtracts the controls reads and considers the effective library size (reads in peaks), was applied. The threshold false discovery rate (FDR) was 0.1.

The MEDIPS package that was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) followed by sequencing (MeDIP-seq), and afterwards for the analysis of other kinds of quantitative sequencing data (e.g., ChIP-seq, MBD-seq, and CMS-seq) was applied (Lienhard et al., 2014). The chicken genome (galGal4) was divided into adjacent bins of 1000nt length. All further calculations, such as short read coverage, differential coverage between conditions, genome-wide CpG densities and their normalizations, were applied to these bins. The Pearson pairwise correlation matrix was obtained by comparing genome-wide coverage profiles of each chicken line with the MEDIPS.correlation function. The chicken lines with similar Pearson correlations were considered as a group for tracking methylated markers among all the chicken lines. Testing differential coverage at genome-wide bins for 2 replicates per the chicken line was done with edgeR. The genome bins of differential methylation that is represented as relative methylation scores (rms) between groups were calculated with FDR less than 0.1, extracted and exported as tables. For annotating genomic windows with known genome characteristics, MEDIPS also generates an annotation object by accessing BioMart. Dataset “ggallus_gene_ensembl” which corresponds to galGal4, the genome used for alignment.

An interactive pathway analysis was performed using the Ingenuity software (http://www.ingenuity.com). The analysis generated an extensive report, but the most valuable pathways are: networks, diseases and disorders, molecular and cellular functions, physiological system development and function, and canonical pathways.

Validation of DNA Methylation Markers

Six significant DMRs were selected and validated by bisulfite cloning sequencing as previously described (He et al., 2018). The primers for the validation are shown in Table S1. To validate those DNA methylation markers, OneStep qMethyl Kit (Zymo Research, Cat. D5310) was utilized to assess DNA methylation levels of the 11 methylation markers across 2 parental lines and 5 RCS strains. Primers were designed using Primer3.0 online (http://frodo.wi.mit.edu/), and they span a DNA region with the length from 120 bp to 350 bp, containing at least 2 methylation sensitive restriction enzymes (MSREs) sites. Real-time PCR reaction was run with triplicate using the program as follows: MSRE Digestion (37°C for 2 h), Initial Denaturation (95°C for 10 min), 40 cycles of amplification (95°C for 30 s, 54°C for 60 s, and 72°C for 60 s), and Final Extension (72°C for 7 min). Cycle threshold values (Ct values) were obtained from iCycler iQ PCR software. The methylation level for all amplified regions can be determined using the following equation: Percent Methylation = 100×2−ΔCt, where ΔCt = the average Ct value from the Test Reaction minus the average Ct values from the Reference Reaction.

Other Immune Responses

Two other immune responses were evaluated to assess the broader effects of the DNA methylation regions. First, a second tumor model examined v-src DNA tumor growth. Second, antibody against SRBC was measured. Both tests used Line 63, Line 72 and RCS C, J, L. M, N, and X. The RCS J was added to these studies as that strain had the lowest MD incidence.

v-src DNA Tumor Growth

Two replicates of chicks were injected with 40 ug v-src DNA at 6 wk of age. Two weeks later, tumor size was measured for 7 consecutive weeks as described (Taylor et al., 1992). Birds that were terminated or died during the study were assigned subsequent tumor sizes of 100. A tumor profile index (TPI) was assigned to each bird based on the tumor scores as described (Taylor et al., 1992). A lower TPI indicated a more favorable tumor outcome. Mean tumor size and TPI were calculated for inbred line or RCS.

Antibody Response Against Sheep red Blood Cells

Antibody response against SRBC were assessed in 5 wk old chicks. A preinjection blood sample was collected to identify any preexisting antibody against SRBC. Two replicates were injected with 0.1 mL 0.25% SRBC. Additional 0.5 mL blood samples were collected 5 and 12 d post injection. Antibody levels were measured using a microtiter method for agglutination with readings taken at 24 h (Siegel and Gross, 1980).

Statistical Analysis

The TPI and antibody titer at 5 or 12 d postinjection were evaluated by analysis of variance with replicate and line as main effects. When significance was indicated, mean values were separated by Bonferonni's method.

RESULTS

Genetic Similarity and Phenotypic Analysis

In this study, we used inbred lines 63 and 72, MD-resistant and -susceptible lines, respectively (Figure 1) and RCS derived from the inbred parental lines. Inbred lines 63 and 72 possess the B2B2 major histocompatibility complex genotype but differ in multiple immune characteristics (Bacon et al., 2000) [16].

Figure 1.

Figure 1

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The chicken population used in this study. DNA methylation was measured in the chicken lines labeled in red.

Reciprocal crosses of lines 63 and 72 generated 2 F1 groups followed by 2 successive backcrosses to line 63. Subsequently, pairs of BC2 birds were mated with siblings. The resultant animals underwent brother-sister mating over more than 25 generations. Currently, 19 RCS were derived each RCS having a calculated genomic composition of 87.25% line 63 and 12.75% lines 72. Therefore, a whole RCS panel can be used as ideal models for genetic mapping of genes other than the MHC that impact immunity, or the individual lines within a panel can be used to study epigenetic aspects of immune responses.

MD resistance in chickens is generally evaluated with MD incidence (gross tumors induced by MDV) and survival days post MDV challenge. The contributions from the background line 63 and the donor line 72 to the 19 RCSs are different. the MD incidence based on the numbers of chickens that developed tumors post MDV challenge in the RCS strains varied (Xie et al., 2017) (Figure S1A). For example, RCS M is supposed to be genetically close to the background line 63, but the high tumor incidence phenotype approaches line 72. Intermediate MD incidence was found in RCS N and C. The RCS strains J, L, and X have phenotypes similar to the MD resistant line 63. Variations of MD incidences among the RCS strains suggested an assessment of each RCS genetic structure. Based on the MD incidence, lines 63 and 72, their reciprocal F1 crosses, and RCS strains C, L, M, N, and X were chosen for further examination. The Affymetrix Chicken Genotyping Array (600K) was used for multidimensional scaling analysis based on genome-wide identical by state pairwise distances in a total of 30 chickens from F0, F1, and 6 RCS strains. Three groups were identified with chicken SNPs markers (Figure S1). Line 72 is more extreme than the other 2 groups. The 6 RCS strains are grouped more closely to the MD resistance line 63. The reciprocal crosses produced by 63 females with 72 males and 63 males with 72 females are classified together. The cluster analysis results suggested additional investigation of epigenetics.

DNA Methylation Analysis

Epigenetic alterations are characterized by chemical changes, such as DNA methylation, histones modifications, and RNAs. In our previous studies, we have reported the transgenerational phenomena of DNA methylation patterns of DNMT3b in chicken spleen and liver(Yu et al., 2008a,b). Maternal effects in offspring have been reported, focusing on gene expression instead of epigenetics(Reed et al., 2014; Gauvin et al., 2020). Few studies have focused on genome-wide DNA methylation. In this study, we used Methyl-CpG-binding domain (MBD) method following high throughput sequencing to enrich and sequence the DNA segments including methylated CpGs. Then, DNA methylation levels for paternal lines 63 and 72 as well as 5 RCS strains C, L, M, N, and X were measured. The similarity of DNA methylation spectrums was evaluated by the Pearson pairwise correlation matrix calculated by the MEDIPS package (Lienhard et al., 2014).

As shown in Figure 2, lines with similar DNA methylation patterns emerged. The transgenerational inheritance of DNA methylation was similar within each group, as was the MD incidence phenotype. Based on the results of DNA methylation similarities, these 7 chicken lines were divided into 3 groups to identify DMRs. Specifically, MD-resistant chickens 63 and RCS L are in group 1 representing MD resistant, whereas MD-susceptible chickens 72 and RCS M are in group 2 representing MD susceptible, and RCS C, N, and X birds are in group 3 representing the RCS generation (Figure 3).

Figure 2.

Figure 2

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Similarity of DNA methylation in the 2 parental lines and 5 RCS lines. Abbreviation: RCS, recombinant congenic strains.

Figure 3.

Figure 3

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Methylated marker tracking. Chickens from the F0 generation and RCS were assigned to 3 groups based on their similarities of DNA methylation. The green color represents resistant methylated regions. The red color represents susceptible methylated regions. The purple color represents heritable resistant and susceptible methylated regions/markers. Abbreviation: RCS, recombinant congenic strains.

DNA methylation level in each bin of the genome was analyzed and normalized for all the chickens. The genome bins with FDR less than 0.1 remained for the comparisons among the groups, and the bins with significant DNA methylation levels were united within a group. Therefore, the resistant methylated regions were identified in the group of line 63 and RCS L, which excluded the overlapped bins with the group of line 72 and RCS M. Likewise, the susceptible methylated regions were found in the unique part of group 72 and M. In addition, methylated regions that can be inherited were identified in the RCS generation overlapped with both the resistant and susceptible groups. Six MD-resistant methylation makers and 5 MD-susceptible methylation markers (FDR ≤ 0.1) were found, which can be inherited from the parental lines to the RCS strains. Furthermore, their overlapped genes were annotated (Table 1). The Galaxy visualization tool visualized some methylation markers (Figures 4 & S2). Figure 4 shows an inheritable methylation marker for MD resistance is located in the IRF2 gene that encodes interferon regulatory factor 2, a member of the IRF family. In addition, the IRF2 product competitively inhibits the IRF1-mediated transcriptional activation of interferons alpha and beta and presumably other genes that employ IRF1 for transcription activation. In contrast, a methylated MD susceptible marker is located on the RNF215 gene encoding a novel E3 ubiquitin-protein ligase that strengthens p53 degradation and suppresses p53 function. These methylation markers are likely to be related to the activation, transcription, and ubiquitination of some tumor-related genes. Their relatively stable methylation status allows individuals to remain resistant or susceptible to MDV through generational transmission.

Table 1.

The coordinates of methylated markers and their overlapped genes.

Number Name Locus Overlapped genes
1 Resistant Marker #1 Chr3: 94349001-94350000 Not available
2 Resistant Marker #2 Chr4: 38999001-39000000 IRF2
3 Resistant Marker #3 Chr4: 17645001-17646000 Not available
4 Resistant Marker #4 Chr4: 20054001-20055000 Not available
5 Resistant Marker #5 Chr17: 695001-696000 EXD3
6 Resistant Marker #6 Chr23: 5716001-5717000 Not available
7 Susceptible Marker #1 Chr1: 194064001-194065000 Not available
8 Susceptible Marker #2 Chr3: 14744001-14745000 DZANK1
9 Susceptible Marker #3 Chr12: 2283001-2284000 Not available
10 Susceptible Marker #4 Chr12: 2285001-2286000 Not available
11 Susceptible Marker #5 Chr15: 10814001-10815000 RNF215
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Figure 4.

Figure 4

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A resistant methylated marker and a susceptible methylated marker through F0 and RCS chickens (FDR ≤ 0.1). DNA methylation profiles were visualized on the Galaxy visualization with normalized bedgraph files. Marker regions are highlighted in the red boxes.

To ensure the accuracy of our data analysis, 6 DMR (Table S1) were randomly selected for validation using bisulfite cloning sequencing. As shown in Figure 5A, 6 regions other than region 1 demonstrated a consistent trend between the MBD-seq results and bisulfite cloning sequencing results. For example, the susceptible line 72 shows the highest methylation levels in all validated regions except for region 1 in MBD-seq analysis as well as the bisulfite method. To confirm the 11 inheritable methylation markers shown in Table 1, MSRE-qPCR using the OneStep qMethyl Kit was applied to validate the methylation levels of these markers in all the chicken lines. Some marker regions are difficult to design primers due to the low CpG coverage and were excluded from the validation. The results (Figure 5B) indicated that DNA methylation levels in lines 63 and RCS L were higher for the resistant markers which tend to pass to their RCS generation whereas the lines 72 and RCS M showed higher methylation levels for the susceptible markers, which is consistent with our MBD-seq results. Therefore, the methylation analysis results from 2 different methods were validated.

Figure 5.

Figure 5

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The validation of significant DNA methylation regions. A) Six DMRs were validated by Bisulfite Cloning Sequencing method. Left: Relative DNA methylation level in each DMR per chicken line was measured by MBD-seq; Right: Relative DNA methylation level was validated in corresponding DMR with Bisulfite Cloning Sequencing. B) The validation of resistant and susceptible methylated markers by One-Step qMethyl PCR. Abbreviation: DMB, differentially methylated regions.

Antibody Response Against Sheep red Blood Cells

Table 2 shows the 5-d postinjection titers against SRBC were highest in RCS N, X and Line 72. Those titers were significantly higher than the lowest titer, found in RCS J. Antibody titers in Line 63 and RCS C, L, and M were intermediate with values that were statistically similar to both the high titer group and the low titer group. Antibody response differed among chickens with different DNA methylation patterns without a consistent relationship to the MD response.

Table 2.

Mean antibody titer (log2) ± SEM for inbred lines 63 and 72 plus selected recombinant congenic strains (RCS) at 5 d postinjection of 0.1 mL 0.25% sheep red blood cells (SRBC).

Line or strain n Mean anti-SRBC
log2 titer (± SEM)
Inbred 63 22 1.82a,b ± 0.28
Inbred 72 21 2.43a ± 0.31
RCS C 15 1.53a,b ± 0.29
RCS J 19 0.95b ± 0.21
RCS L 24 1.92a,b ± 0.32
RCS M 20 1.70a,b ± 0.37
RCS N 19 3.00a ± 0.45
RCS X 20 2.90a ± 0.35
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a,b

Values with no letter in common differ significantly (P < 0.05).

v-src DNA Tumor Growth

The TPI of Line 72 was significantly higher than all other strains (Table 3). The next TPI group included RCS C and L which were significantly different from RCS J. Line 63 plus RCS M, N, X, and J were statistically similar. Some of the lines with divergent DNA methylation also had differences in v-src tumor growth but these differences did not align completely with the MD response.

Table 3.

Mean tumor profile index (TPI) ± SEM for 2 inbred lines and 6 recombinant congenic strains (RCS) following injection of 40 ug v-src DNA at 6 wk of age.

Line or strain n Mean TPI (± SEM)
Inbred 63 33 3.06b,c ± 0.12
Inbred 72 28 4.89a ± 0.08
RCS C 15 3.67b ± 0.13
RCS J 18 2.72c ± 0.25
RCS L 24 3.63b ± 0.18
RCS M 18 3.17b,c ± 0.17
RCS N 22 3.09b,c ± 0.06
RCS X 27 3.22b,c ± 0.08
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abc

Values with no letter in common differ significantly (P < 0.05).

DISCUSSION

Genetic resistance and susceptibility to MD are complex characteristics, impacted by multiple genes and nongenetic factors. Genome-wide associated studies have been used to identify candidate genes for MD resistance that can be incorporated into selection. However, linkage disequilibrium has complicated efforts to pinpoint the genes underlying disease resistance. Although integrated studies have successfully identified a few of these genes, including MHC family (Briles et al., 1977), Rfp-Y (Wakenell et al., 1996), vitamin D receptor (Praslickova et al., 2008), stem cell antigen 2 (Luo et al., 2013) 46], growth hormone (GH1) (He et al., 2019) 45] and quantitative trait loci (Vallejo et al., 1998; Yonash et al., 1999; Heifetz et al., 2009), the genetic resistance to MD is much more complex than previously envisioned. In previous research, we confirmed the involvement of epigenetic components in MD. In this study, integrated epigenetics and genomic methods were used to identify important epigenetic modifications, regulatory elements, and genetic markers underpinning MD (Yu et al., 2008a; Luo et al., 2012a, b; Mitra et al., 2012). This effort revealed the similarity of methylation profiles after 23 generations between parents and RCS. This transgenerational methylation phenomenon provides new evidence that epigenetic patterns can be transferred to multiple generations.

Notably, the DNA methylation levels in epigenetics correspond to the trend of MD incidence. So far, most of the epigenetic studies only focused on epigenetic modifications within a generation, describing the transgenerational phenomenon (Goerlich et al., 2012; Liu et al., 2018) rather than interrelating with any phenotypes (Goerlich et al., 2012; Liu et al., 2018; Berghof et al., 2013). Our data provided the very first evidence of a direct link between DNA methylation profiles and the phenotype of MD incidence over multiple generations, indicating that the epigenetics in complex traits, like disease resistance, cannot be neglected. At present, how those epigenetic-related genes quantitatively affect the complex traits and why their phenotypes vary differently from individual to individual are both unknown. The complex traits may be explained by the variance of epigenetic components. Therefore, a systems-level analysis of the epigenetic regulatory mechanisms will serve as a blueprint for disease resistance research as well as a reference for other animals of agricultural importance.

Mechanisms through which epigenetic changes are transferred between generations include DNA methylation, histone modification, noncoding RNAs, or a combination of these act as information vectors. Epigenetic inheritances generated from DNA sequences are reset twice in the germline (Heifetz et al., 2009; Shea et al., 2015; Tang et al., 2015; Zimmermann and Macpherson, 2020). Systematic epigenetic patterns or called markers transferred over multiple generations may associate with individual phenotypic performance and disease outcomes. In the study, the interferon regulatory transcription factor (IRF) family was identified as MD resistance candidate marker. The gene product competitively inhibits the IRF1-mediated transcriptional activation of interferons alpha and beta, and presumably other genes that employ IRF1 for transcription activation. Interferons are a group of signaling proteins made and released by host cells in response to the presence of several viruses, which was confirmed by our previous studies (Tian et al., 2013; He et al., 2014; Mitra et al., 2015). Conversely, a susceptible methylation marker is located on RNF125 gene, which encodes a novel E3 ubiquitin-protein ligase that strengthens p53 degradation, and further suppressing p53 function. Previous studies have identified that MDV infection causes differential DNA methylation changes by decreasing H3K27 trimethylation in MX1 and CTLA4 promoters (Luo et al., 2012; Mitra et al., 2012; Luo et al., 2013; Tian et al., 2013; He et al., 2015; He et al., 2019). Investigations have identified a role of histone markers in transferring information across generations. Histone modifications can activate (H3K4me2 and H3K4me3) or repress (H3K27me3) gene transcription, thereby making it possible for intergenerational transmission of epigenetic information.

Host immune responses are actively barriers to prevent malfunction disease of organisms from pathogen invasion. They are classically divided into innate immune responses, reacting rapidly and nonspecifically; and adaptive immune responses, slower to develop are specific and build up immunological memory or priming. The immune priming is an intriguing form of parental care that contributes to the plasticity adjustment of the progeny's immune responses, which has been described in several vertebrate and invertebrate taxa (Grindstaff et al. 2006; Deleris et al., 2016; Espinas et al., 2016; Bozler et al., 2020; Fowler et al., 2020). Exposure to pathogens not resulting in the host becoming infectious may ‘prime’ the immune response (Tidbury et al., 2012). Antibody response against SRBC in RCS, the antibody titer at d 12 demonstrated the immune response priming compared to d 5 after injection. Interestingly, in v-src DNA injection in RCS, the tumor showed a similar response trend likewise the profile of antibody titers. We think such immune priming is a widespread and important feature of host–pathogen interactions. Especially parental exposure to pathogens alters offspring immune responses across generations (Roth et al., 2018), leading to increased susceptibility/resistance to infection or disease in offspring (Roth et al., 2012). The trained immunity will reveal an important and previously unrecognized features of chicken immune responses. Further examination of the lines with significant differences in TPI will help to identify genes that impact v-src DNA tumor growth.

In summary, most previous research that studied the host-virus interaction on genetics and epigenetics focused on a single generation (Luo et al., 2012, 2013; Tian et al., 2013; Bai et al., 2019; He et al., 2019). The current study demonstrates that DNA methylation patterns can be inherited over more than 23 generations. The transgenerational epigenetic inheritance, and immunity are associated with disease outcomes, they are not independent and cannot be completely isolated. Therefore, the role of epigenetics in immune responses merits additional investigation to identify specific pathways affected. Such work will advance our understanding of host-virus interaction, aid new prevention strategies, and enhance vaccine development.

Acknowledgments

This work was supported by the National Research Initiative Competitive Grant (No. USDA-NRI/NIFA 2010-65205-20588) from the USDA National Institute of Food and Agriculture and MAES (MD-ANSC-232659).

Data Availability

Accession to cite for these SRA data is PRJNA956938. Code is available

Disclosures

The authors declared no conflict of interest.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2023.103036.

Appendix. Supplementary materials

mmc1.docx (187.1KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx (187.1KB, docx)

Data Availability Statement

Accession to cite for these SRA data is PRJNA956938. Code is available

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