BMC GenomicsGenomicsPeer-reviewed journal article
Genome-wide mapping and profiling of γH2AX binding hotspots in response to different replication stress inducers
Abstract
BackgroundReplication stress (RS) gives rise to DNA damage that threatens genome stability. RS can originate from different sources that stall replication by diverse mechanisms. However, the mechanism underlying how different types of RS contribute to genome instability is unclear, in part due to the poor understanding of the distribution and characteristics of damage sites induced by different RS mechanisms.\n\nResultsWe use ChIP-seq to map {gamma}H2AX binding sites genome-wide caused by aphidicolin (APH), hydroxyurea (HU), and methyl methanesulfonate (MMS) treatments in human lymphocyte cells. Mapping of {gamma}H2AX ChIP-seq reveals that APH, HU, and MMS treatments induce non-random {gamma}H2AX chromatin binding at discrete regions, suggesting that there are {gamma}H2AX binding hotspots in the genome. Characterization of the distribution and sequence/epigenetic features of {gamma}H2AX binding sites reveals that the three treatments induce {gamma}H2AX binding at largely non-overlapping regions, suggesting that RS may cause damage at specific genomic loci in a manner dependent on the fork stalling mechanism. Nonetheless, {gamma}H2AX binding sites induced by the three treatments share common features including compact chromatin, coinciding with larger-than-average genes, and depletion of CpG islands and transcription start sites. Moreover, we observe significant enrichment of SINEs in {gamma}H2AX sites in all treatments, indicating that SINEs may be a common barrier for replication polymerases.\n\nConclusionsOur results identify the location and common features of genome instability hotspots induced by different types of RS, and help in deciphering the mechanisms underlying RS-induced genetic diseases and carcinogenesis.
- organism: human
- assay: ChIP-seq
- research focus: genome assembly
- research focus: variant calling
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 29
- FWCI
- 0.91
- Field percentile
- top 29%
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