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Published on: June 3, 2012
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Genome graphs detect human polymorphisms in active epigenomic state during influenza infection
Cristian Groza1, Xun Chen2, Alain Pacis3
1Quantitative Life Sciences, McGill University, Montréal, QC, Canada.
Cell Genomics
|May 25, 2023
Summary
Genome graphs reveal hidden epigenomic signals by incorporating genetic diversity. This approach identified novel regulatory regions and mobile element insertions impacting immunity and gene expression, particularly after influenza infection.
Area of Science:
- Genomics
- Epigenetics
- Immunology
Background:
- Genetic variants, including mobile element insertions (MEIs), influence the epigenome.
- Genome graphs can capture genetic diversity to uncover novel biological insights.
Purpose of the Study:
- To investigate if genome graphs can identify epigenomic signals missed by traditional methods.
- To explore the role of MEIs in immunity using epigenomic data from diverse individuals before and after influenza infection.
Main Methods:
- Sequencing epigenomes (H3K4me1, H3K27ac ChIP-seq, ATAC-seq) of macrophages from 35 diverse individuals.
- Characterizing genetic variants and MEIs using linked reads and constructing a genome graph.
- Mapping epigenetic data onto the genome graph to identify novel regulatory regions and polymorphic MEIs.
Main Results:
- Genome graph mapping identified 2.3%-3% novel epigenomic peaks.
- 375 polymorphic MEIs were found in active epigenomic states.
- An AluYh3 polymorphism's chromatin state changed post-infection, affecting TRIM25 expression, which is crucial for restricting influenza.
Conclusions:
- Graph genomes offer a powerful approach to uncover regulatory regions previously overlooked.
- MEIs play a significant role in epigenomic regulation and immune response, particularly in the context of viral infections.
- This study highlights the utility of genome graphs in understanding complex genetic and epigenomic interactions.
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