Multiple generation distinct toxicant exposures and epigenetic transgenerational inheritance of sex‑specific outcross impacts on sperm epigenetics and obesity pathology

ABSTRACT

Environmental factors can influence the epigenetic inheritance of pathology across generations. Prior studies found various toxicants increased pathology in their descendants through epigenetic transgenerational inheritance. Previously, three generations of rats were exposed separately to vinclozolin (F0 generation), a jet fuel hydrocarbon mixture (F1 generation), and dichlorodiphenyltrichloroethane (DDT) (F2 generation), producing a transgenerational F5 generation. The current study extends this study by breeding this transgenerational F5 generation with wild-type rats to create maternal and paternal outcross lineages, labeled MOC and POC, respectively. In the current study, we examined the differential DNA methylated regions (DMRs) in the outcross F6 generation offspring to detect potential parent-of-origin lineage‑specific inheritance impacts. When comparing both outcross MOC and POC lineages of the F6 generation to a control outcross F3 generation from the same lineage, with no toxicant exposure, a higher number of DMRs were observed in the F6 generation lineages. Additionally, while there was some common overlap between the two MOC and POC outcross generations, only the F6 POC generation had overlaps with the F5 multigenerational exposure lineage generation of the previous study, which was also a paternal outcross. Since previous pathology analysis has been extensive, limited pathology (e.g. obesity) was performed in the current study to primarily confirm the transgenerational pathology in the F6 generation outcross lineages. Comparisons between the control same lineage F3 generation and both of the outcross F6 generations identified outcross‑specific results, suggesting sex-specific epigenetic inheritance after ancestral toxicant exposure. Therefore, both the oocyte and sperm facilitate epigenetic transgenerational inheritance.

Introduction

Epigenetics is defined as the molecular factors and processes around the DNA that regulate genome activity, independent of the DNA sequence, and that are mitotically stable [1]. Exposure to environmental factors such as abnormal nutrition, stress, temperature, and toxicants can influence epigenetic inheritance in organisms ranging from plants to humans [2]. This epigenetic inheritance is distinct from genetic inheritance, as it involves the heritable epigenetic mechanisms of DNA methylation, histone modification, chromatin structure, and non-coding RNA (ncRNA) that impact gene regulation independently of an organism’s DNA sequence [3]. Despite the partial erasure of high-density CpG DNA methylation during the early stages of embryonic development, the transgenerational DNA methylation regions (DMRs) remain [3]. The epigenetic factors of sperm and eggs influence the epigenetic expression within zygotes. This epigenetic expression is then continued through embryonic pluripotent stem cells. The stem cells with altered epigenetics impact all subsequent somatic cell types, such that each somatic cell type has a unique epigenome and transcriptome that determines cell specificity. This epigenetic inheritance can influence later-life phenotypic variation and disease susceptibility.

The transmission of parent-of-origin allelic epigenetic information was initially found to occur within the imprinted genes [4,5]. This transmission enables sex-specific alleles to influence the promotion of imprinted gene expression and functions [6]. Researchers can determine specific parent-of-origin allelic transmission in a subsequent outcross generation by breeding the males of an impacted lineage with wild-type females, as well as females of an impacted lineage with wild-type males [7]. Previous studies have demonstrated that ancestral exposure to environmental toxicants influences the disease susceptibility and later-life phenotypic variation of subsequent generations [8]. These multigenerational impacts of ancestral exposure were further investigated by exposing gestating female rats to environmental toxicants that contemporary human populations were historically exposed to [9]. The first of these toxicants was the agricultural compound dichlorodiphenyltrichloroethane (DDT) which was developed in the late 1940s. DDT was utilized extensively in agricultural regions as well as throughout North America and Europe during the 1950s and 1960s to reduce malaria [10]. In the 1960s and 1970s, humans were exposed to plastic compounds, hydrocarbon oil spills, and jet fuel hydrocarbons. This was then followed by exposure to agricultural compounds such as the herbicide glyphosate and fungicide vinclozolin from the late 1970s and 1980s until the present day [11]. The linear pattern of exposure to distinct environmental toxicants has spanned about four to five generations in humans. This has the potential to result in the promotion of a non-genetic form of inheritance through each subsequent generation termed epigenetic transgenerational inheritance [12], but remains to be assessed in humans.

Previous studies have only focused on the first transgenerational generation (F3 generation) of a single F0 generation ancestrally exposed lineage. Researchers examined the potential parental origins of transgenerational disease within the outcross F4 generation [7]. Gestating females of the F0 generation were exposed to either DDT or vinclozolin. After their offspring reached puberty, randomly selected males and females from different litters within their specific lineages were bred without any inbreeding. The offspring of these breeding pairs were bred again with the same method to increase the F3 transgenerational generation [7]. The F3 generation males were then outcrossed with wild-type females, and the F3 generation females were outcrossed with the wild-type males. This resulted in an F4 generation paternal outcross lineage (POC) and an F4 generation maternal outcross lineage (MOC), respectively. Once the F4 outcross generation reached 1 y of age, researchers performed histopathology analysis on the reproductive organs of the POC and MOC lineages [7]. These results were then compared with the F3 transgenerational generation. Researchers also analyzed the differential DNA methylated regions (DMRs) of sperm DNA for both the F4 outcross generations and the F3 transgenerational generation for comparisons [7]. When compared with the F3 generation, both the MOC and POC lineages had a significant increase in DNA methylation. There was also an observable transmission of female multiple disease through the POC F4 generation lineage and a transmission of male multiple disease through the MOC F4 generation lineage [7]. The observed obesity results were consistent with a previous study that presented similar results through a transgenerational outcross of a methoxychlor lineage [13]. The results of the F4 generation outcross study indicate a parent-of-origin effect for transgenerational epigenetic inheritance in subsequent generations after ancestral exposure and warrants further investigation.

Researchers also investigated how successive ancestral exposures to environmental toxicants could potentially promote pathologies and diseases through epigenetic transgenerational inheritance [9]. They began by exposing the first generation (F0 generation) of gestating female rats during fetal gonadal sex determination to the fungicide vinclozolin. The resulting F1 generation was then bred, without any inbreeding, and the gestating females of this generation were exposed to jet fuel hydrocarbons (JP8). This pattern of breeding was repeated for the F2 generation with the gestating females being exposed to the agricultural compound dichlorodiphenyltrichloroethane (DDT). After the F2 generation, three more generations were bred without any additional exposures to create the F5 generation [9]. This F5 generation was then considered the transgenerational generation since none of its germ cells underwent direct exposure to the environmental toxicants vinclozolin, JP8, or DDT. The pathology and epigenetics of the F5 generation were compared to the previous generations as well as an F3 generation from a control lineage with no ancestral exposure to toxicants [9]. Initially, the F1 generation had the highest number of epigenetic changes in the differential DNA methylated regions (DMRs). This was followed by a decline in each generation until the F5 generation in which the amount of DMRs increased again [9]. Additionally, the F5 generation had an increase in obesity rates for both males and females. This generation also had a significant increase in kidney diseases for both sexes [9]. Compared to the F3 control generation, the F5 transgenerational generation had higher frequencies of pathologies for the testis, prostate, male kidney, female kidney, ovary, and multiple diseases [9]. The observed pathologies and DMRs indicate that ancestral exposure to environmental toxicants can influence the inheritance of epigenetic alterations in subsequent generations. In turn, these epigenetic changes result in higher disease frequencies several generations after the initial exposure to the specific toxicant. The question developed is if epigenetic transgenerational inheritance is observed in a parent-of-origin specific manner in subsequent generations after ancestral exposure to environmental toxicants?

The current study investigates parent-of‑origin-specific epigenetic inheritance by analyzing the maternal and paternal outcrosses of a transgenerational F5 generation after multiple generations were exposed to environmental toxicants. The transgenerational F5 generation is from the previously mentioned multigenerational study in which generations F0, F1, and F2 were exposed to the environmental toxicants vinclozolin, jet fuel hydrocarbons, and DDT, respectively [9]. The F5 generation was the first generation in which neither individuals nor their germ cells had direct exposure to the environmental toxicants used [9]. The F6 generation paternal outcross lineage (POC) and maternal outcross lineage (MOC) were then derived from the F5 generation through breeding with wild-type rats. Through these lineages, parent-of-origin transgenerational inheritance for specific disease (i.e., obesity) and sperm epigenetics was analyzed. Therefore, the current experiment expands upon previous studies to investigate whether epigenetic and disease expression in generations with multiple distinct toxicant exposures is transmitted in a sex-specific manner (e.g., POC or MOC).

Results

The experimental design of this study is a continuation of the previous study involving multiple generation exposure of environmental toxicants to promote epigenetic transgenerational inheritance of disease. However, unlike the previous experiment, the rat lineages were continued through the F6 outcross generations. These paternal and maternal outcrosses were created to better identify potential parent-of-origin inheritance of sperm epigenetics exhibited by the F6 outcross generations. In the first exposed generation, gestating rat females of the F0 generation received daily intraperitoneal (IP) injections of the anti-androgenic fungicide vinclozolin (100 mg/kg BW/day) during fetal days 8 through 14 of embryonic development [9]. This fungicide has a 25 mg/kg lowest observable adverse effect level (LOAEL) [14]. The rats of the F0 generation were then bred without any inbreeding to create the F1 generation. The gestating female rats of this F1 generation were then administered daily IP injections of a hydrocarbon jet fuel mixture (JP8) (500 mg/kg BW/day) and had a 1000 mg/kg/day LOAEL [15]. The offspring from the F1 generation were classified as the F2 generation, and gestating females of this F2 generation were given daily IP injections of the pesticide DDT (25 mg/kg/day) that has a 20 mg/kg BW/day LOAEL [16]. All of these IP injections were administered during the period of gonadal sex determination during development and aimed to influence the germ-cell epigenetics of the fetus.

The subsequent F3 generation, F4 generation, and F5 generation were generated (Figure 1) without any additional exposures to environmental toxicants. To avoid animal inbreeding, approximately 16 breeding pairs were maintained for each generation [9]. Each generation produced approximately 130 offspring through breeding within the previous generation, maintaining a balanced 50:50 male-to-female ratio [9]. The F5 generation was then considered the first transgenerational generation as neither the rats of this generation nor their germ cells had any direct exposure to the environmental toxicants used. From this F5 transgenerational generation, males were bred with wild-type females that had no history of exposure to create the F6 paternal outcross generation (POC). The F5 generation females were similarly bred with wild-type males resulting in the creation of the F6 maternal outcross generation (MOC) (Figure 1).

Figure 1.

F6 outcross exposure and breeding model. Graphic demonstrates the unique exposures for the F0–F5 generations as well as the outcross breeding of the F5 transgenerational generation to produce the F6 maternal outcross (MOC) generation and the F6 paternal outcross (POC) generation lineages.

At 1 y of age, sperm from the males of each generation were collected for epigenetic analysis. While the male sperm numbers can be in the millions, female germ cell oocyte collections at best generate dozens of oocytes, which is insufficient for epigenetic analysis. The collected sperm was used to assess potential sex-specific influences on the DNA methylation of the sperm. The sperm was isolated from the cauda epididymis and then fragmented to assess the sperm differential DNA methylation regions (DMRs) through methylated DNA immunoprecipitation (MeDIP) [17]. Although preliminary analysis of the F6 POC generation and F6 MOC generation DMRs contained background effects that skewed initial results, a new method using zero CpG (0 CpG) sites allowed for a normalization of the DMR data. This was achieved by locating 40 zero CpG areas to act as normalization sites, correcting the current analysis of the DMR data. These zero CpG sites are presented in Supplemental Table S1. This normalization protocol using 0 CpG sites was validated and described in the Methods.

Comparisons between the different DMRs of the control lineage and exposure outcross lineages were performed, as previously described [17]. The control lineage derives from the gestating females of the control F0 generation lineage that were exposed to the vehicle dimethyl sulfoxide (DMSO) as a comparison with the original toxicants of the previous multigenerational study that were dissolved in DMSO, as previously described [18]. The resulting control F1 generation offspring were then bred to create the F2 control generation. This was repeated once more to create the control F3 generation, which were then aged to 1  y before sperm collection and pathology analysis [9]. The sperm epigenetic analysis of the F3 control generation was then compared to the POC F6 generation and the MOC F6 generation, Figure 2. Although a control lineage F6 generation may have been a more appropriate control, those lineages were not generated and only the F3 generation control was obtained and used. We considered using the wild-type animals, but found the basal level of epigenetic variation higher, such that the F3 generation control was optimal for the analysis. Therefore, the F3 generation outcross control lineage was used as the control for the current study. The F6 outcross lineage male sperm were compared to the F3 generation control lineage sperm.

Figure 2.

DMR identification and overlaps. The number of DMRs found using different p-value cutoff thresholds and shows the number of DMRs containing at least two nearby significant window (1 kb each). The number of DMRs with the number of significant windows (1 kb per window) at a p-value threshold of p < 1e-04 for DMR is bolded. All DMR comparisons use the same control samples. (A) F3 control generation versus F6 POC generation; (B) F3 control generation versus F6 MOC generation. DMR chromosomal locations and DMR overlaps. (C) DMR chromosomal location F3 control generation versus F6 POC generation 1e-04; (D) DMR chromosomal location F3 control generation versus F6 MOC generation 1e-04.

The edgeR p-value as well as the number of DMRs associated with each outcross generation is presented for the F6 paternal outcross generation (Figure 2(A)) and the F6 maternal outcross generation (Figure 2(B)). A comparison of the DMRs between the F6 POC generation and the F6 MOC generation indicated a higher number of DMRs in the F6 POC lineage (Figure 2(A)). The number of single window 1 kb DMR is presented and the multiple-window adjacent 1 kb DMR is presented. The majority of DMRs identified were single-window DMRs (Figure 2) at a p-value threshold of p < 1e-04. The DMR names, molecular and gene associations characteristics for each outcross generation are presented in Supplemental Tables S2–S3. The genomic chromosomal locations of DMRs from comparisons of the control and outcross generations are presented in Figure 2(C,D). For each chromosome, the DMRs are presented with red arrowheads indicating the locations of the DMRs and black box clusters of DMRs (Figure 2(C,D)). The DMRs have a genome-wide distribution. The CpG density of the DMRs is presented for each outcross generation (Figure 3(A,C)). The CpG densities are 1–3 CpG/100 bp. As such, these are primarily lower density CpG desert densities, as previously described [19]. The DMR length was found to be primarily 1 kb with some at 3–4 kb (Figure 3(B,D)). All the specific DMR statistics, characteristics, and gene associations are presented in Supplemental Tables S2–S3 for each outcross generation.

Figure 3.

DMR CpG density and DMR CpG lengths. (A) DMR CpG density of F3 control generation versus F6 POC generation; (B) DMR CpG lengths of F3 control generation versus F6 POC generation; (C) DMR CpG density of F3 control generation versus F6 MOC generation; (D) DMR CpG lengths of F3 control generation versus F6 MOC generation.

An overlap of the DMRs at p < 1e-04 DMR sets is compared between the F6 POC generation and the F6 MOC generation (Figure 4(A,B)). The overlap for the p < 1e-04 DMRs of the two F6 outcross generations and the previous F5 multigenerational lineage is also compared (Figure 4(B)). Between the two F6 outcross lineages, there were 78 common DMRs, which was over 50% of the MOC DMRs (Figure 4(B)). The F6 POC outcross lineage had three common DMRs with the previous F5 multigenerational lineage (Figure 4(B)). There were no common DMRs between the F6 MOC outcross lineage and the F5 multigenerational lineage (Figure 4(B)). An extended overlap comparison of the POC and MOC DMR data is presented in Figure 4(C). The edgeR p < 1e-04 DMR data was compared to DMR analysis p < 0.05 statistic to determine if at a reduced statistical stringency overlap between the DMRs may be observed. The POC F6, MOC F6, and the original F5 DMRs were compared. The direct overlaps provided 100% overlap as expected for each comparison, Figure 4(C). At a reduced statistical stringency of p < 0.05 overlap between the POC and MOC DMRs of 97.4% and 89.1%, respectively. Therefore, greater than 89% of the DMRs were common between the outcross POC and MOC at the reduced p < 0.05, Figure 4(C). The overlap with the F5 generation DMR was lower at <15% for all comparisons, Figure 4(C). Therefore, strong overlap was observed between the MOC and POC DMRs, but limited overlap with the F5 generation DMRs. The reduced overlap between the F5 generation and outcrossed F6 generations suggests the outcross for both the MOC and POC are dramatically altered by the wild-type outcross. This is an interesting observation further discussed in the Discussion section.

Figure 4.

Comparative DMR analysis. (A) Venn diagram F6 POC generation versus F6 MOC generation DMR overlap 1e-04. (B) Venn diagram F6 outcross generations (POC and MOC) versus F5 multigen DMR overlap. (C) Extended overlap with p < 0.05 for DMR overlap comparisons between the F6 POC generation, F6 MOC generation, and previously demonstrated/published F5 generations all at p < 0.05 or p < 1e-04.

A principal component analysis of the DMR read depth demonstrated good separation between the F3 control generation and the F6 outcross generations when DMR sites were considered, Figure 5(A,B) Both the MOC and POC DMR data had good separation in the principal component analysis between the control and outcross DMR epigenetic molecular data. This indicates the epigenetic data in all the samples used have good separation in the DMR sites. An additional analysis examined within the DMR population’s maximum log-fold change of the DMRs. For both the POC and MOC F6 generation DMRs approximately 50% of the DMRs had an increase in DNA methylation (positive log) versus 50% with a reduced DNA methylation (negative log), Figure 5(C,D) The magnitude of the changes was higher in the POC DMRs compared to the MOC DMRs, Figure 5.

Figure 5.

DMR principal component analysis. (A) F3 control generation versus F6 POC generation p < 1e-04; (B) F3 control generation versus F6 MOC generation p < 1e-04. Maximum log-fold change. (C) F3 control generation versus F6 POC generation; (D) F3 control generation versus F6 MOC generation.

The DMR gene-associations were investigated and involved DMRs within 10 kb of a gene to include the proximal and distal promoter regions (Figure 6(A) and Supplemental Tables S1 & S2). The individual DMR gene associations and categories are presented in Supplemental Tables S1 for the POC DMRs and Supplemental Table S2 for the MOC DMRs. The gene categories for individual DMR-associated genes were obtained from Supplemental Tables S1 & S2. The DMR gene associations from the POC and MOC DMRs of both F6 outcross generations demonstrate numerous metabolic pathway associations and functional gene categories (Figure 6(A) and Supplemental Tables S1 & S2). This included signaling, cytoskeleton, metabolism, transcription, and protease. As such, a variety of functional genes signaling pathways are associated with the DMRs for both the maternal outcross and paternal outcross of a multiple generation exposure. This included metabolic pathways, axon guidance, and neurodegeneration with POC DMRs, and metabolic pathways, amyotrophic lateral sclerosis, and lysine degeneration in MOC DMRs, Figure 6(B) and Supplemental Tables S1 & S2.

Figure 6.

DMR gene association pathway analysis with individual DMR‑associated genes and pathways obtained from Supplemental Tables S1 & S2. (A) Number of DMR-associated genes in each gene category; (B) DMR-associated gene pathways analysis. KEGG pathway identifier numbers and names are presented. The number of DMR-associated genes that fall into each pathway is indicated in parentheses ().

Due to extensive pathology analysis in the previous studies of this lineage of rats through the F3 and F5 generations [9], the current study simply analyzed obesity to confirm the transgenerational phenotype in the F6 outcross generation. For the analysis of obesity, multiple variables were assessed including average body weight and average body mass index (BMI), as well as excessive abdominal fat deposition, as previously described [20–22]. Sex-specific comparisons of obesity between the F3 control generation and both F6 outcross POC and MOC generations are presented in Figure 7. Observations in the obesity rates for females indicate a significant increase (~25% population) in the F6 POC generation (Figure 7(A)). For the male obesity rates, the F5 multigenerational lineage had the highest (~35% population) increase, followed by the F6 POC and MOC generation (Figure 7(B)). Previous studies have shown that within the same litter there can be both obese and non-obese litter mates who follow identical diets and exercise routines [23]. In these cases, changes in food consumption are not expected to play a significant role. Instead, the difference indicated a susceptibility for obesity despite having the same dietary intake. Observations confirm the transgenerational pathology phenotype in the F6 generation POC and MOC used obesity weight as a specific confirmatory transgenerational pathology.

Figure 7.

Obesity assessment for F3 control generation, the F6 POC generation, and the F6 MOC generation. (A) Obesity females; (B) Obesity males. The frequency of the population with obesity is listed as a (%) of the population with obesity. Statistical differences were determined by Fisher’s exact tests. *p < 0.05. **p < 0.01. ***p < 0.001.

Discussion

Epigenetics involves molecular factors around DNA that regulates gene expression, independent of the DNA sequence [1]. These epigenetic processes regulate genome activity and are heritably stable. Environmental exposures to toxicants or poor nutrition can influence these epigenetic mechanisms, such as DNA methylation and histone modifications [2]. This can lead to epigenetic transgenerational inheritance that is distinct from genetic inheritance. Multiple studies in which gestating female rats are exposed to single toxicants such as DDT or vinclozolin demonstrated that disease susceptibility and epigenetic alterations can be passed down from ancestral exposure to offspring transgenerationally to subsequent generations. Furthermore, previous outcross studies showed distinct differences of epigenetic inheritance and pathology expression depending on whether the transmission was maternal or paternal, suggesting a parent-of-origin affect [7]. Additionally, successive ancestral exposures to multiple toxicants (vinclozolin, jet fuel, and DDT) resulted in the increase of disease and DNA methylation alterations in the transgenerational F5 generation, which had no direct exposure [9]. The current study expands on these findings by analyzing the multigenerational exposure transgenerational F5 generation outcross to wild-type rats offspring to create a maternal outcross lineage and paternal outcross lineage. Analysis of the DNA-methylated regions and pathology expression of both outcross generation lineages was conducted to investigate whether epigenetic transgenerational inheritance from ancestral exposure to environmental toxicants is transgenerationally sex-specific.

While initial DMR analysis of the sperm for both of the F6 POC and MOC outcross lineages was difficult due to background effects that impacted preliminary results, a new method was derived to normalize the DMR data. Epigenetic DNA methylation occurs at cytosines adjacent to guanines (CpGs) in DNA. Specific zero CpG sites commonly found on all of the genomes served as control regions for normalization of the epigenetic DMR data for the F6 outcross generations [24]. Specific characteristics of the DMRs including size, CpG density, and genome-wide localization were similar between the two F6 outcross POC and MOC lineages (Figures 4 & 5). The resulting information is promising due to the high amount of DMRs in the POC lineage, indicating an increased level of epigenetic change (Figure 2). Furthermore, while some overlap in DMRs between the F6 outcross MOC and POC lineages is expected, the discovery of DMR overlap between the previous F5 transgenerational generation and the current F6 POC generation suggests the potential inheritance of epigenetic alterations from the previous multiple toxicant exposures lineage.

In a specific obesity pathology analysis, used to confirm the transgenerational pathology, the abdominal fat deposition, size, and average BMI results for the males and females of each F6 outcross lineage were used to assess obesity and compared to the F5 multigen lineage and the F3 control lineage (Figure 7), as previously described [20–22]. While the obesity and average BMI frequency for males was still the highest in the F5 generation, the F6 POC and MOC generation had the highest significant frequency for obesity in females (Figure 7). However, the transgenerational obesity pathology was confirmed in both MOC and POC F6 generations. The current study continues to demonstrate that ancestral environmental exposures can promote the epigenetic transgenerational inheritance of epigenetic changes such as DNA methylation alterations that increase disease risk in future, unexposed generations. These results, particularly the differences between the F6 MOC generation and the F6 POC generation suggests that the impact of transgenerational epigenetic inheritance is sex-specific and partially dependent on the parent-of-origin impacts. This has significant implications for the long-term health trajectory of the global human population, suggesting that genetics or lifestyle decisions are not the only determining factor for disease susceptibility. The influence of ancestral exposures to stressors and toxicants for current generations is likely contributing to the overall global increase of disease frequencies in the later generation human population [25]. Therefore, further discussions regarding our responsibility to the health of future generations is required, and how society can address these new observations are needed.

Methods

Animal studies and breeding

The female and male rats of the F6 outcross generation were derived from an outbred strain Hsd: Sprague Dawley SD (Harlan) and were fed with a standard diet of tap water, both ad lib [13,26–30]. Every animal and their cages were housed within the same room and environment, except gestating females and females with litters who were housed separately in their own cages. These conditions and environments were set up in a way to minimize any maternal effects brought on by stress or other factors. Unrelated males and females in the age range of 70–100 d and of a specific exposure lineage were bred. This optimized both the maternal and paternal contributions to the observed phenotypes [7]. There was no inbreeding within the colonies of lineages as it is linked to the suppression of epigenetic expression [31–33]. The original unrelated breeding pairs of the treated F0 generation lineage were 16 in total, allowing for the F1 generation through F6 outcross generation pups to not be inbred [9]. Through timed pregnancies, the females of the F0 generation were mated as well as given daily intraperitoneal IP injections of the treatment compound (vinclozolin 100 mg/kg) (Chem Service Inc., West Chester, PA, USA) on embryonic days 8 through 14 (E8-E14) [34]. The pregnant F1 generation females were injected with jet fuel hydrocarbons (250 mg/kg J8P), while the F2 generation females were injected with 25 mg/kg of dichlorodiphenyltrichloroethane (DDT) (Chem Service Inc.). Then the F3 generation and F4 generation were bred without receiving any treatments. The F5 generation was then considered to be the transgenerational generation since neither they nor their germ cells were exposed directly to the three environmental toxicants previously used [1]. The F6 maternal and paternal outcross generations were then created through the breeding of the wild-type rats and the F5 generation. The paternal outcross F6 generation lineage (POC) is the offspring of the male F5 generation rats breeding with the wild-type female rats. The maternal outcross F6 generation lineage (MOC) is the offspring of the female F5 generation rats breeding with the wild-type male rats. The control lineage was created by injecting the F0 control generation female gestating rats with dimethyl sulfoxide (DMSO) (Sigma–Aldrich) from E8-E14 of gestation. The F1 control generation and F2 control generation were bred without any DMSO injections. The resulting F3 generation control was then considered as the transgenerational control lineage generation [9]. To avoid inbreeding artifacts, neither cousins nor siblings were bred. Housing, food consumption, exercise, and health for all the housed animals were exactly the same for all animals and other crossed housed animals showed differences in obesity within the same cages. Therefore, these parameters were not found to be factors in the obesity phenotype observed. The animals were euthanized via a $\mathit{C}{O}_2$ chamber, followed by cervical dislocation as the designated secondary method. All protocols and procedures were pre-approved by the Washington State University Animal Care and Use Committee (IACUC approval #2568 & #6931) and were performed in accordance with the relevant guidelines and regulations of IACUC and ARRIVE.

Epididymal sperm collection

As previously described [35], the fat and connective tissue of the epididymis were dissected, the cauda was cut open and then placed into 6 ml of phosphate buffered saline (PBS) for 20 minutes at room temperature. Afterward, the tissue was minced, releasing the sperm, which was subsequently pelleted at $4^o\mathit{C}$ via centrifuging at 300 g for 10 minutes. It was then resuspended in 250 $\mathit{\mu l}$ NIM buffer and stored at $-{80}^o\mathit{C}$.

DNA isolation

As previously described [36,37] for the molecular analysis, an appropriate amount of the collected rat sperm was suspended ($\sim 50\mathit{\mu l}$) for DNA extraction. Multiple previous studies have demonstrated the sonication resistance of mammalian sperm heads, as opposed to somatic cells [36,37]. As such, brief sonication (Fisher Sonic Dismembrator, model 300, power level 25) was utilized to remove somatic cell contamination and debris, followed by centrifugation and washing 1 to 2 times in 1× PBS. This purified sperm pellet was then resuspended in $820\mathit{\mu L}$ DNA extraction buffer with the addition of $80\mathit{\mu l}$ 0.1 M DTT and incubated at ${65}^o\mathit{C}$ for 15 minutes. After, proteinase K ($80\mathit{\mu l}$ of 20 mg/ml) was added before being incubated again at ${55}^o\mathit{C}$ in a constant rotation for 2 to 3 hours. Using an addition of protein precipitation solution ($300\mathit{\mu l}$, Promega A795A) to remove protein and being incubated for 15 minutes on ice, the samples were then centrifuged at 13 500 g for 30 minutes at $4^o\mathit{C}$, washed with 70% cold ethanol, air-dried for about 5 minutes, and then resuspended in $100\mathit{\mu l}$ of nuclease-free water.

MeDIP (methylated DNA immunoprecipitation)

The sperm DNA obtained from individual animals was pooled with an additional 5–10 DNA samples from different animals in equal amounts and prepared as previously described [38]. Although individual animal analyses for the epigenetic analyses would be optimal, this was found to be cost prohibitive, so pools were used. As a confirmation, previous studies with individual versus pooled samples were done and similar data obtained, as previously described [38]. The genomic DNA from the samples were sonicated and run on 1.5% agarose gel to verify fragment size. The sonicated DNA then went under the process of single-stranded DNA fragments creation by diluting the samples with 1× TE buffer to 400 $\mathit{\mu l},$ heat denature for 10 minutes at $95$, and instant cooling on ice for 10 minutes. Afterward, 100 $\mathit{\mu l}$ of 5× IP buffer and 5 $\mathit{\mu g}$ of antibody (monoclonal mouse anti-5-methyl cytidine; Diagenode #C15200006) were combined with the individual DNA samples and incubated overnight with rotation at $4$. The next day, 50 $\mathit{\mu l}$ of magnetic beads (Dynadbeadds M280 Sheep anti-Mouse IgG; Life Technologies 11201D), after pre-washing per the manufacturer’s instruction, were added to each 500 $\mathit{\mu l}$ DNA-antibody sample from the prior night’s mixture and incubated for 2 hours with rotation at $4$. Once finished, the mixtures were washed three times with 1× IP buffer with the help of a magnetic rack, resuspended in 250 $\mathit{\mu l}$ digestion buffer (5 mM Tris PH 8, 10 mM EDTA, 0.5% SDS) with 3.5 $\mathit{\mu l}$ Proteinase K (20 mg/ml), and then incubated for 2 to 3 hours with rotation at $55$. The DNA samples were then cleaned by utilizing a Phenol–Chloroform–Isoamyl–Alcohol extraction, as well as supernatant precipitated with 2 $\mathit{\mu l}$ of GlycoBlue (20 mg/ml), 20 $\mathit{\mu l}$ of 5 M NaCl, and 500 $\mathit{\mu l}$ ethanol at −20°C for the duration of one to multiple hours. The precipitate of the DNA samples was then pelleted at 13 500 g for 30 minutes at 4°C, followed by a 70% ethanol wash twice, drying, and resuspension in 20 $\mathit{\mu l}$ $H_2\mathit{O}$. The concentration derived was then measured in a Qubit apparatus (Life Technologies) and with the help of the ssDNA analysis kit (Molecular Probes Q10212). The MeDIP-Seq technology is optimal for low density CpG/100 bp 0–90% bp regions that consist of the vast majority of the epigenome, while other procedures such as bisulfite sequencing are biased to higher density CpG/100 bp regions which constitute < 15% of the epigenome. This is why MeDIP-Seq was selected to capture the majority of the epigenome, while newer short read sequencing is currently cost prohibitive for such an analysis. For comparisons, the within sex comparison was made to allow interpretation of genetic impacts between the F5 and F6 generations for each sex. Previous studies have demonstrated the comparison between sexes for epigenetics generates excessive epigenetic differences which are difficult to interpret, while the F6 generation comparison was useful to assess similarities between the MOC and POC.

MeDIP-Seq analysis sequencing libraries

The MeDIP DNA was utilized for the creation of libraries for next-generation sequencing (NGS) with the NEBNext Ultra II RNA Library Prep Kit for Illumina (E770L, New England Biolabs, San Diego, CA, USA) via the manufacturer’s protocol for generating double-stranded DNA from single-stranded DNA derived from MeDIP results. Sample sequencing was conducted by the WSU Spokane Genomics Core via the Illumina HiSeq 2500 at PE50, at a read size of about 50 bp and about 6 to 22 million reads per pool, with the most being at 12 million reads and 12 libraries being run on one lane.

Statistics and bioinformatics

The DMR identification and annotation methods follow those presented in previously published papers [28,38]. Data quality was assessed using the FastQC program (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The data were cleaned and filtered to remove adapters and low-quality bases using Trimmomatic [39]. The reads for each MeDIP sample were mapped to the Rnor 6.0 rat genome using Bowtie2 with default parameter options [40]. The mapped read files were then converted to sorted BAM files using SAMtools [41]. The MEDIPS R package was used to calculate differential coverage between disease and non-disease sample groups [42]. The edgeR p-value was used to determine the relative difference between the two groups for each genomic window [43]. Windows with an edgeR p-value less than the selected p < 1e-04 threshold were considered as DMR. The site edges were extended until no genomic window with an edgeR p < 0.1 remained within 1000 bp of the DMR. The edgeR p-value was used to assess the significance of the DMR identified. The DMR‑associated genes were annotated using the biomaRt R package to access the Ensembl database [44,45]. Genes were sorted into categories by converting Panther protein classifications into more general groups [46]. All molecular data has been deposited into the public database at NCBI (GEO # GSE335443) and R code computational tools available at GitHub (https://github.com/skinnerlab/MeDIP-seq) and www.skinner.wsu.edu.

The zero CpG (0) CpG sites in the genome with similar DMR characteristics except the lack of any CpG were identified and shown in Supplemental Table S3. The 0 CpG sites were similar in size (length) and genomic characteristics without any unique (e.g., repeat element) characteristics. These sites were used as a group to normalize the data obtained from the sequencing prior to DMR analysis. Following 0 CpG normalization of the sequencing data, the DMR analysis was utilized. This removes any baseline variation in the sequencing for the analysis. The baseline variation was speculated to be due to the time frame of the experiment to generate 6 generations of adult rats with different exposures and aging to 1 y of age. Over this period, the MeDIP-Seq technology was also altered some and required normalization. Therefore, this 0 CpG normalization was developed to normalize the differences in MeDIP-Seq data obtained over time to provide the optimal comparison and reduce variation in the epigenetic data. Clearly, this can become a standard protocol to assist in MeDIP-Seq and other epigenetic analysis protocols.

Manual pathology analysis

Histopathology examination and obesity classification

Pathology analysis was overseen by co-author Dr. Eric Nilsson, DVM/PhD, who has over 20 y of experience in analyzing the pathology of rats [28,47]. Board-certified veterinary pathologists from the Washington Animal Disease Diagnostic Laboratory (WADDL) at Washington State University College of Veterinary Medicine assisted in the original establishment of the pathology analysis criteria and identified specific parameters for assessment [48]. For all animals that died before the scheduled sacrifice at 1 y, WADDL performed full necropsies as well as tumor classifications for this study. A brief examination of both the abdominal and thoracic organs was conducted during each dissection to detect abnormalities. Previous literature detailing tissues shown to have pathology in transgenerational models guided the researchers’ selection of reproductive organ tissues for histology analysis [13,18,20–22,27–29,34,48].

For the transgenerational pathology confirmation, obesity classification involved the assessment of criteria such as abdominal adiposity, total body weight, and BMI [30,34,49–51]. The BMI for each rat was calculated with weight (g)/length ($\mathit{c}{m}^2$), measuring the length of the rats from nose to the base of their tail. The abdominal fat deposition and weight was assessed at animal sacrifice and used to determine obesity. As previously described, for results that yielded continuous data (age at puberty, weight at euthanization, sex ratio, litter size, fertility rate, and parturition abnormality), treatment groups were analyzed using Student’s t-test [26]. For results expressed as the proportion of affected animals that exceeded a predetermined threshold (obesity), groups were analyzed using Fisher’s exact test.

Funding Statement

This study was supported by The Libra Foundation [GF007237] and John Templeton Foundation [50183], [61174] grants to MKS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All molecular data have been deposited into the public database at the NCBI repository (GEO # GSE335443), and R code computational tools are available at GitHub (https://github.com/skinnerlab/MeDIP-seq) and www.skinner.wsu.edu.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15592294.2026.2719246