Remodeling of Epigenome and Transcriptome Landscapes with Aging in Mice Reveals Widespread Induction of Inflammatory Responses
Total Page：16
File Type：pdf, Size：1020Kb
Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses Bérénice A. Benayoun,1,5,6,7 Elizabeth A. Pollina,1,8 Param Priya Singh,1 Salah Mahmoudi,1 Itamar Harel,1,9 Kerriann M. Casey,2 Ben W. Dulken,1 Anshul Kundaje,1,3 and Anne Brunet1,4 1Department of Genetics, 2Department of Comparative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA; 3Department of Computer Science, Stanford University, Stanford, California 94305, USA; 4Paul F. Glenn Laboratories for the Biology of Aging, Stanford University, Stanford, California 94305, USA 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 tran- scriptomic 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 age- related 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 iden- tifies chromatin and transcriptional states that are characteristic of young tissues, which could be leveraged to restore aspects of youthful functionality to old tissues. [Supplemental material is available for this article.] The functional decline of organs and tissues is a hallmark of aging, tin marks are relatively stable and can even persist through cell divi- and it is accompanied by changes in gene expression and chroma- sion (Kouskouti and Talianidis 2005), sustained alterations to the tin modifications across cell types and tissues (Benayoun et al. chromatin landscape may mediate the propagation of age-associat- 2015; Booth and Brunet 2016; Pal and Tyler 2016; Sen et al. ed functional decline. 2016). Aging is the primary risk factor for a variety of chronic diseas- Age-dependent changes in chromatin marks (e.g., DNA meth- es, including neurodegeneration, cardiovascular disease, and can- ylation, histone modifications) have been observed in multiple cer. Several conserved pathways are misregulated during aging, species and tissues (Benayoun et al. 2015; Booth and Brunet defining hallmarks or pillars of aging (López-Otín et al. 2013; 2016; Pal and Tyler 2016; Sen et al. 2016). However, most of this Kennedyet al. 2014). One such hallmark is the accumulation of epi- knowledge has relied on DNA methylation or global assessments genetic alterations, defined here as changes to gene regulation by of histone modification changes (e.g., mass spectrometry, western chromatin modifications. Perturbation in chromatin-modifying blot) rather than locus-specific evaluation (e.g., ChIP-seq) (Horvath enzymes can extend lifespan in invertebrate models (Benayoun 2013; Benayoun et al. 2015; Wagner 2017; Cheung et al. 2018). et al. 2015; Pal and Tyler 2016; Sen et al. 2016), suggesting that Several genome-wide studies have interrogated locus-specific loss of chromatin homeostasis drives aspects of aging. As chroma- changes in histone modifications and chromatin states, as well as changes in gene expression in several cell and tissue types with mammalian aging (e.g., tissue stem cells, liver cells, pancreatic Present addresses: 5Leonard Davis School of Gerontology, University beta cells, neurons, and T cells) (Rodwell et al. 2004; Cheung of Southern California, Los Angeles, CA 90089, USA; 6USC Norris Com- et al. 2010; Liu et al. 2013; Shulha et al. 2013; Bochkis et al. 2014; 7 prehensive Cancer Center, Los Angeles, CA 90089, USA; USC Stem Sun et al. 2014; Avrahami et al. 2015; White et al. 2015; Zheng Cell Initiative, Los Angeles, CA 90089, USA; 8Harvard Medical School, Boston, MA 02115, USA; 9Department of Genetics, Silberman Insti- et al. 2015; Moskowitz et al. 2017; Stegeman and Weake 2017; tute of Life Sciences, The Hebrew University of Jerusalem, Givat Ucar et al. 2017; Nativio et al. 2018). While these studies have Ram, Jerusalem, 91904, Israel Corresponding authors: [email protected], [email protected] Article published online before print. Article, supplemental material, and publi- © 2019 Benayoun et al. This article, published in Genome Research, is available cation date are at http://www.genome.org/cgi/doi/10.1101/gr.240093.118. under a Creative Commons License (Attribution-NonCommercial 4.0 Interna- Freely available online through the Genome Research Open Access option. tional), as described at http://creativecommons.org/licenses/by-nc/4.0/. 29:697–709 Published by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/19; www.genome.org Genome Research 697 www.genome.org Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Benayoun et al. provided important insights into genome-wide chromatin and et al. 2013; Benayoun et al. 2014; Wagner et al. 2016) and indicate transcriptome remodeling with age, they have remained restricted that overall, tissue and cell identities remain quite stable during to specific cell types and/or a single histone mark. Thus, whether aging. general rules and patterns govern age-related chromatin and tran- To understand how age impacts the global epigenomic scriptional changes with age—and how they are linked—remains and transcriptomic landscapes in each tissue or cell type, we per- largely unknown. formed MDS or PCA on individual tissues/cell types (Fig. 2A–J; Supplemental Fig. S2A–T). In all tissues and for all features (RNA, H3K4me3 intensity and breadth, H3K27ac intensity and breadth), Results there was a progressive separation based on age, with the young samples clustering closer to the middle-aged samples and further A genome-wide epigenomic and transcriptomic landscape of four away from the old samples (Fig. 2A–J; Supplemental Fig. S2A–T). tissues and one primary cell type during mouse aging For primary NSC cultures, there was also a separation with age for To understand how chromatin and transcriptional profiles change H3K4me3 intensity, H3K4me3 breadth, and H3K27ac super-en- during aging, we collected several tissues and cells from C57BL/6N hancers (Supplemental Fig. S2F,I,O). However, the transcriptome male mice at three different time points: youth (3 mo), middle age and H3K27ac intensity of NSCs did not separate well with respect (12 mo), and old age (29 mo). We focused on a subset of tissues— to age (Supplemental Fig. S2C,L), possibly because of technical heart, liver, cerebellum, and olfactory bulb—that are known to dis- noise. Our observations are consistent with previous reports of play age-related functional decline and are clearly anatomically de- age-associated epigenetic changes at enhancers in liver tissue and fined. We also derived primary cultures of neural stem cells (NSCs) pancreatic beta cells (Avrahami et al. 2015; Cole et al. 2017). from these young, middle-aged, and old mice. For each tissue or cell Thus, genome-wide RNA and features of H3K4me3 and H3K27ac culture from all three ages, we generated transcriptomic maps (i.e., deposition can distinguish between ages across tissues and cells. RNA-seq) and epigenomic maps (i.e., ChIP-seq of total Histone 3 [H3] for normalization, trimethylation of Histone 3 at lysine 4 Machine learning reveals that age-related epigenomic changes [H3K4me3], and acetylation of Histone 3 at lysine 27 [H3K27ac]), can predict transcriptional changes yielding 143 data sets (Fig. 1A,B; Supplemental Fig. S1A,B; Supplemental Table S1A). We chose H3K4me3 and H3K27ac To understand how age-related changes in the epigenome predict because both marks are associated with ‘active chromatin’ and age-related transcriptional changes, we took advantage of machine because spread of these marks is linked to cell identity and specific learning (Fig. 3A; Supplemental Fig. S3A). Using four algorithms transcriptional states. Indeed, H3K4me3 and H3K27ac are preferen- (i.e., neural networks [NNETs], support vector machines [SVMs], tiallyenrichedatactivepromoters and activeenhancers, respective- gradient boosting [GBM], and random forests [RFs]), we trained ly (Heintzman et al. 2007). Broad H3K4me3 domains that spread models to discriminate between transcriptional changes with age beyond the promoter region are known to mark genes that are im- —up-regulated, down-regulated, or unchanged gene expression portant for cell identity