Integrative Cut&Tag/RNA-Seq analysis of histone variant macroH2A1-dependent orchestration of human iPSCs reprogramming
Liorni, N.; Napoli, A.; Castellana, S.; Giallongo, S.; Rehakova, D.; Lo Re, O.; Koutna, I.; Mazza, T.; Vinciguerra, M.
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Human-induced pluripotent stem cells (iPSCs) can be derived from adult stem cells by forced expression of defined transcription factors. This paves the way for autologous iPSC-derived therapies, which, however, are not yet considered safe. Moreover, reprogramming of somatic cells into iPSCs is an inefficient process, in the range of 0.1%-1%. The epigenetic mechanisms implicated in iPSCs reprogramming are not well understood. The substitution of canonical histone H2A with macroH2A1 histone variant exon-spliced isoforms (macroH2A1.1 and macroH2A1.2) appears as an emerging regulator of iPSCs identity. In particular, we have previously shown that overexpression of macroH2A1.1 led to a more efficient iPSCs reprogramming, by not fully defined mechanisms. Cleavage under targets and tagmentation (CUT&Tag) is a recent methodology used for robust epigenomic profiling of a limited amount of cells. Here, we performed the first integrative CUT&Tag/RNA-Seq analysis of the histone variant macroH2A1-dependent orchestration of iPSCs reprogramming using human umbilical vein endothelial cells (HUVEC) during their reprogramming into iPSC over-expressing tagged macroH2A1.1 or macroH2A1.2. Our results demonstrate a higher and more widespread genome occupancy and a greater number of differentially expressed genes orchestrated by macroH2A1.1 in HUVEC undergoing reprogramming as compared to macroH2A1.2, which involved pervasive functions related to the three embryonic germ layers and increased overlap with CTCF, FOS, GATA2, and POLR2A transcription factor binding sites. In particular, all predicted macroH2A1.1 activating pathways were related to ectoderm/neural processes. As macroH2A1 isoforms have been previously associated with pathologies of the nervous system, our findings may provide relevant molecular insights for modeling neurodegenerative diseases using iPSCs.
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