Nucleic Acids ResearchGenomicsPeer-reviewed journal article
Germline DNA replication timing shapes mammalian genome composition
Yishai YehudaiD, Britny Blumenfeld, Nina MayorekiD, Kirill Makedonski, Oriya VardiiD, Leonor Cohen‐DanieliD, Yousef Mansour, Shulamit Sebban, Hagit Masika, Marganit FaragoiD, Michael F. BergeriD, Shai CarmiiD, Yosef BuganimiD, Amnon KoreniD, Itamar SimoniD
Abstract
Mammalian DNA is replicated in a highly organized and regulated manner. Large, Mb-sized regions are replicated at defined times along S phase. DNA Replication Timing (RT) has been suggested to play an important role in shaping the mammalian genome by affecting mutation rates. Previous analyses relied on somatic DNA RT profiles, while to fully understand the influences of RT on the mammalian genome, germ cell RT information is necessary, as only germline mutations are passed to offspring and thus affect genomic composition. Using an improved RT mapping technique that allows mapping the RT from limited amounts of cells, we measured RT from two stages in the mouse germline - primordial germ cells (PGCs) and spermatogonial stem cells (SSCs). The germ cell RT profiles were distinct from those of both somatic and embryonic tissues. The correlations between RT and both mutation rate and recombination hotspots were not only confirmed in the germline tissues, but were shown to be stronger compared to correlations with RT of somatic tissues, emphasizing the importance of using RT profiles from the correct tissue of origin. Expanding the analysis to additional genetic features such as GC content, transposable elements (SINEs and LINEs) and gene density, also revealed a stronger correlation with the germ cell RT maps. GC content stratification along with multiple regression analysis revealed the independent contribution of RT to SINE, gene, mutation and recombination hotspot densities. Taken together, our results point to the centrality of RT in shaping multiple levels of mammalian genome composition.
- organism: mouse
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 36
- FWCI
- 1.60
- Field percentile
- top 16%
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