Summary Trimethylation of Histone H3 at Lysine 4 (H3K4me3) is a chromatin modification known to mark the transcription start sites of active genes. Here we show that H3K4me3 domains that spread more broadly over genes in a given cell type preferentially mark genes essential for the identity and function of that cell type. Using the broadest H3K4me3 domains as a discovery tool in neural progenitor cells, we identify novel regulators of these cells. Machine learning models reveal that the broadest H3K4me3 domains represent a distinct entity, characterized by increased marks of elongation. Broadest H3K4me3 domains also have more paused polymerase at their promoters, suggesting a unique transcriptional output. Indeed, genes marked by broadest H3K4me3 domains exhibit enhanced transcriptional consistency rather than increased transcriptional levels, and perturbation of H3K4me3 breadth leads to changes in transcriptional consistency. Thus, H3K4me3 breadth contains information that could ensure transcriptional precision at key cell identity/function genes.

In the adult brain, the neural stem cell (NSC) pool comprises quiescent and activated populations with distinct roles. Transcriptomic analysis revealed that quiescent and activated NSCs exhibited differences in their protein homeostasis network. Whereas activated NSCs had active proteasomes, quiescent NSCs contained large lysosomes. Quiescent NSCs from young mice accumulated protein aggregates, and many of these aggregates were stored in large lysosomes. Perturbation of lysosomal activity in quiescent NSCs affected protein-aggregate accumulation and the ability of quiescent NSCs to activate. During aging, quiescent NSCs displayed defects in their lysosomes, increased accumulation of protein aggregates, and reduced ability to activate. Enhancement of the lysosome pathway in old quiescent NSCs cleared protein aggregates and ameliorated the ability of quiescent NSCs to activate, allowing them to regain a more youthful state.

Antisense transcription is a widespread phenomenon in the mammalian genome. It is thought to play a role in regulation of gene expression, but its exact functional significance is largely unknown. We have identified a natural antisense transcript of p53, designated Wrap53, that regulates endogenous p53 mRNA levels and further induction of p53 protein by targeting the 5' untranslated region of p53 mRNA. siRNA knockdown of Wrap53 results in significant decrease in p53 mRNA and suppression of p53 induction upon DNA damage. Conversely, overexpression of Wrap53 increases p53 mRNA and protein levels. Blocking of potential Wrap53/p53 RNA hybrids reduces p53 levels nearly as efficiently as Wrap53 knockdown, strongly suggesting that Wrap53 regulates p53 via Wrap53/p53 RNA interaction. Furthermore, induction of Wrap53 sensitizes cells for p53-dependent apoptosis. This discovery not only reveals a regulatory pathway for controlling p53, but also proposes a general mechanism for antisense-mediated gene regulation in human cells.

Aging is accompanied by the functional decline of tissues. However, a systematic study of epigenomic and transcriptomic changes across tissues during aging is missing. Here, we generated chromatin maps and transcriptomes from four tissues and one cell type from young, middle-aged, and old mice-yielding 143 high-quality data sets. We focused on chromatin marks linked to gene expression regulation and cell identity: histone H3 trimethylation at lysine 4 (H3K4me3), a mark enriched at promoters, and histone H3 acetylation at lysine 27 (H3K27ac), a mark enriched at active enhancers. Epigenomic and transcriptomic landscapes could easily distinguish between ages, and machine-learning analysis showed that specific epigenomic states could predict transcriptional changes during aging. Analysis of data sets from all tissues identified recurrent agerelated chromatin and transcriptional changes in key processes, including the up-regulation of immune system response pathways such as the interferon response. The up-regulation of the interferon response pathway with age was accompanied by increased transcription and chromatin remodeling at specific endogenous retroviral sequences. Pathways misregulated during mouse aging across tissues, notably innate immune pathways, were also misregulated with aging in other vertebrate species-African turquoise killifish, rat, and humans-indicating common signatures of age across species. To date, our data set represents the largest multitissue epigenomic and transcriptomic data set for vertebrate aging. This resource identifies chromatin and transcriptional states that are characteristic of young tissues, which could be leveraged to restore aspects of youthful functionality to old tissues.

Author contributions L.X. and A.M contributed equally to this study. S.M., E.M. and A.B. planned the study, with input from M.P.S. and J.W. S.M. and E.M. performed the reprogramming experiments, and analysed and interpreted data. S.M., E.M. and L.X. wrote the manuscript with the help of A.B. S.M. generated, processed and analysed bulk and single-cell RNA-seq datasets, analysed the metabolomics data, and performed most THY1-related and conditioned medium experiments. E.M. generated and propagated transgene-free iPS cell lines. All other studies were done by both E.M and S.M., unless otherwise noted. A.M. and S.M. performed wound healing experiments under the supervision of M.T.L. L.X. helped with reprogramming, FACS, and immunofluorescence experiments. F.J. generated the in vitro single-cell RNA-seq data under the supervision of M.P.S. R.S. generated the ChIP-seq libraries under the supervision of J.W. K.H. helped with statistics and PAGODA analysis. X.L. performed metabolomics experiments and helped with metabolomics data analysis and validation under the supervision of M.P.S. K.D. helped with reprogramming and western blotting experiments. L.P. helped with reprogramming and RT-qPCR experiments. C.E.A. and Y.S. performed the induced neuron reprogramming experiment under the supervision of M.W. B.A.B. helped with analysis of the epigenomic data. A.L.S.C. identified and collected the human samples. All authors discussed the results and commented on the manuscript. Data availabilityAll raw sequencing reads for population RNA-seq, ChIP-seq and single-cell RNA-seq data can be found under BioProject PRJNA316110. The command and configuration files, in addition to a list of all versioned dependencies present in the running environment, are available on the Github repository for this paper (https://github.com/brunetlab/Mahmoudi_et_al_2018) (except for the code for the processing of metabolomics data, which is available upon request).