Depletion of DNMT1 in differentiated human cells highlights key classes of sensitive genes and an interplay with polycomb repression [expression]

DNA methylation plays a vital role in the cell, but loss-of-function mutations of the maintenance methyltransferase DNMT1 in normal human cells are lethal, precluding target identification, and existing hypomorphic lines are tumour cells. We generated instead a hypomorphic series in normal hTERT-immortalised fibroblasts using stably integrated short hairpin RNA. Approx 2/3 of sites showed demethylation as expected, with 1/3 showing hypermethylation, and targets were shared between the three independently-derived lines. Enrichment analysis indicated significant losses at promoters and gene bodies with four gene classes most affected: 1)protocadherins, which are key to neural cell identity; 2)genes involved in fat homeostasis/body mass determination; 3)olfactory receptors and 4) cancer/testis antigen (CTA) genes. Overall effects on transcription were relatively small in these fibroblasts, but CTA genes showed robust derepression. Comparison with siRNA-treated cells indicated that shRNA lines show substantial remethylation over time. Regions showing persistent hypomethylation in the shRNA lines were associated with polycomb repression, and were derepressed on addition of an EZH2 inhibitor. Persistent hypermethylation in shRNA lines was in contrast associated with poised promoters. Our results suggest polycomb marking blocks remethylation and indicate the sensitivity of key neural, adipose, and cancer-associated genes to chronic depletion of maintenance methylation activity. Stable knockdowns of the maintenance methyltransferase DNMT1 were generated in hTERT-immortalised adult fibroblasts using shRNA. WThTERT-1604 cells were used in triplicate as a control . Genome wide methylation and transriptional levels were assayed using the Illumina 450k BeadChip and HT12 arrays respectively and subsequently analysed using bioinformatic approaches. Genes targets of relevant interest identified through bioinformatic mining were validated using wet lab approaches.