Germline genomic and methylomic dynamics following three generations of early-life metabolic challenges

Technical Summary: Germline genomic and methylomic dynamics following three generations of early-life metabolic challenges

Problem Statement
Environmental and dietary factors, particularly early-life metabolic challenges such as overnutrition, are known to exert multigenerational effects on health and development. While previous research has established that metabolic disruptions can be transmitted via the paternal germline through epigenetic mechanisms (e.g., miRNA, DNA methylation), the long-term genomic impacts of multigenerational (successive) exposures remain poorly understood. Specifically, it is unclear how sustained early-life metabolic stress across three generations affects the germline genome (SNPs and CNVs) and whether these genomic changes are driven by or correlated with epigenomic alterations (DNA methylation). Most existing studies focus on single developmental exposures transmitted across generations (transgenerational) rather than continuous multigenerational exposure, which represents a more realistic scenario for environmental obesogens.

Methodology
The study employed a murine model using the outbred ICR (CD1) strain to simulate early-life metabolic challenge via litter size reduction.

• Experimental Design: Three generations (F0–F2) were tracked. The Overnutrition (ON) group consisted of litters reduced to 4 pups per dam, while the Control (CT) group had 8 pups per dam. This model is established to induce neonatal obesity and subsequent metabolic syndrome. Males from each generation were mated with external naïve females to isolate paternal lineage effects.
• Phenotyping: Physiological and metabolic parameters (body weight, glucose, insulin, liver, and adipose tissue weights) were assessed at 4 months of age.
• Sequencing Strategy: Sperm DNA was extracted and subjected to parallel analysis using Genotyping-by-Sequencing (GBS) for genomic variation and GBS coupled with Methyl-Immunoprecipitation (GBS-MeDIP) for methylomic profiling.
• Genomic Analysis:
  • SNPs: Identified using GATK best practices. Principal Component Analysis (PCA) and hierarchical clustering (Identity by State) were used to assess genetic similarity and treatment effects.
  • CNVs: Germline Copy Number Variants (CNVs) were called using GATK's GermlineCNVCaller. A rigorous filtering process was applied, including a randomized subsampling control to eliminate false positives.
  • Annotation: CNVs were annotated for Transposable Elements (TEs) and repetitive elements (REs) using permutation tests to determine enrichment.
• Epigenomic Analysis:
  • DMRs: Differential Methylated Regions were identified via intragenerational (CT vs. ON) and intergenerational (F1 vs. F2) comparisons using Mann-Whitney tests.
  • Pathway Analysis: Pathway enrichment was performed using KEGG and PathBIX on genes associated with methylated windows.
• Integration: Spearman rank correlations were calculated to investigate relationships between paternal methylation levels and the emergence of novel SNPs or CNVs in offspring.

Key Results

1. Genomic Impact (SNPs): PCA of SNP data revealed that unrelated ON families clustered together, distinct from the CT families which clustered strictly by kinship. This suggests the metabolic challenge induced a shared genomic signature. The treatment explained approximately 4.6% of the genetic variance. Furthermore, the ON group exhibited a reduced emergence of novel SNPs in offspring compared to the CT group, indicating a constraint on genetic variability.
2. Genomic Instability (CNVs): The ON group showed the emergence of specific CNV events (7 putative events maintained from F1 to F2). These events were significantly enriched in Long Interspersed Nuclear Elements (LINEs) and Long Terminal Repeats (LTRs). Deletions remained stable across generations, while duplications tended to expand.
3. Epigenomic Impact: Global PCA of the methylome showed no clear separation between ON and CT groups, indicating no widespread global methylation shift. However, pathway enrichment analysis revealed that the ON group had weaker or absent enrichment signals in pathways related to polysaccharide metabolism, lipolysis, P53, mTOR, and hedgehog signaling compared to CT.
4. Regional Methylation Changes: Differential Methylation Region (DMR) analysis identified specific changes in the F2 generation, including hypomethylation in LINEs and SINEs and hypermethylation in genes Fhod3 and Etl4. Intergenerational comparisons also highlighted DMRs in Fhod3 and gamma-satellite repeats.
5. Genome-Epigenome Dynamics:
   • SNPs: In the CT group, higher paternal methylation correlated with higher SNP emergence in offspring (consistent with the known mutability of methylated cytosines). Conversely, in the ON group, an inverse correlation was observed, suggesting that under metabolic stress, methylated CpGs may be less prone to mutation, potentially due to protective mechanisms against oxidative damage.
   • CNVs: The enrichment of LINEs/LTRs in CNVs, coupled with differential methylation in these elements, suggests that dysregulation of RE methylation may drive CNV formation.

Significance and Claims
The authors claim that this study provides evidence that multigenerational metabolic challenges can induce both genomic and epigenomic alterations in the germline. Key claims include:

• Constraint vs. Instability: Metabolic challenge appears to constrain genetic variability at the SNP level (fewer novel mutations) while simultaneously inducing genome instability at the structural level (CNVs).
• Mechanism: The observed genomic instability (CNVs) is likely mediated by the dysregulation of transposable elements (specifically LINEs and LTRs) rather than by widespread changes in global DNA methylation.
• Realism: By tracking three generations of continuous exposure, the study offers a more realistic picture of how environmental obesogens impact evolutionary potential and genomic architecture compared to single-exposure transgenerational models.
• Novelty: This is the first controlled study to address how the genome is affected by sperm DNA methylation changes specifically in a multigenerational exposure scenario, highlighting the complex interplay where epigenetic perturbations may lead to structural genomic variations.

The paper concludes that early-life metabolic challenges have long-lasting impacts on genomic architecture, potentially mediated by transposable element activity, and underscores the necessity of studying genome and epigenome dynamics together under realistic exposure scenarios.