Epigenetic modulation of metabolic decisions

Available online at www.sciencedirect.com

ScienceDirect Epigenetic modulation of metabolic decisions Anita O¨st1 and John Andrew Pospisilik2 In the recent years there has been a tremendous increase in our understanding of chromatin, transcription and the importance of metabolites in their regulation. This review highlights what is currently sparse information that suggest existence of a refined system integrating metabolic and chromatin control. We indicate possible regulatory modes, such as feed forward amplification, that may help effect and stabilize long-lasting phenotypic decisions within and even across generations using adipogenesis as the primary context. Addresses 1 Department of Clinical and Experimental Medicine, Linkoping University, 58183 Linkoping, Sweden 2 Max Planck Institute of Immunobiology and Epigenetics, Stuebeweg 51, 79108 Freiburg, Germany Corresponding author: Pospisilik, John Andrew ([email protected])

Current Opinion in Chemical Biology 2015, 33:88–94 This review comes from a themed issue on Cell regulation Edited by Johan Auwerx and Jodi Nunnari

The term epigenetics was initially used to describe the consecutive, deterministic events locking a cell into a specific cell fate. Its most common contemporary definition encompasses essentially all chromatin based modes of phenotypic regulation outside of DNA sequence. For the purposes of this review, we consider epigenetics to comprise stable changes in chromosomes that can be maintained through meiotic or mitotic division and that exclude alterations in DNA sequence [1], and, we consider metabolic decisions to be meiotically or mitotically stable metabolic regulatory events arising from epigenetic control. Chromatin organization is known to be regulated by DNA-modifications and histone-modifications as well as small and long non-coding RNA, and it has been proposed that these components are the essence of an epigenetic memory. Importantly, the interplay between metabolism and epigenetic control is bi-directional: Chromatin state helps define metabolic gene control, chromatin acts as a sink for select metabolites, and metabolism itself directly impinges upon chromatin state regulation. Here we summarize key recent developments in our understanding of the metabolic–chromatin interface and examine their reciprocal regulation.

http://dx.doi.org/10.1016/j.ceb.2014.12.005

Epigenetic metabolic decisions

955-0674/# 2014 Elsevier Ltd. All rights reserved.

As mentioned above, intergenerational or transgenerational regulation of metabolic set-points are prime examples of metabolic decisions. A wide range of parental nutritional interventions has been shown to modulate offspring metabolic phenotype (reviewed in [2–5]). The molecular mechanisms underlying the initiation and stabilization of such metabolic decisions are currently topics of intense investigation. Studies on DNA methylation in mammals [6–8] as well as plant [9,10] and invertebrate studies [11,12] have laid the foundation for our limited understanding of transgenerational inheritance of epigenetic marks. In mammals, one of the most heavily studied chromatin features changed by intergenerational metabolic challenges is DNA methylation. Parental dietary interventions in rodents change DNAmethylation at specific loci in the liver [13–15], white adipocytes [16], pancreatic islet [17,18] and sperm [19]. In order for such changes to be at the mechanistic core of parentally induced metabolic memory they must either survive or bypass the erasure of most epigenetic marks that occurs during germline development and early embryogenesis. Taking parentally induced metabolic decisions in adipocytes as an example, information about the nutritional status in parents must (i) be contained in the sperm or oocyte respectively, (ii) survive many rounds of cell division and of lineage decision, and then (iii) be maintained long-term in the fully differentiated adipocyte

Introduction The regulation of metabolic flux is fundamental in controlling systemic and cellular energy homeostasis. In addition, metabolism directly contributes to processes as diverse as cell differentiation, cancer, stress response, fecundity and life span. Rather than simply reflecting current nutritional status, cellular metabolic state represents consolidation of numerous ongoing environmental inputs (metabolite, hormonal, etc.) as well as past events such lineage decisions, cumulative age and chronic inflammation. How these intricate layers of information integrate to generate a specific metabolic response remains poorly understood. One enigmatic example is how ancestral nutritional state is ‘remembered’, as a stable metabolic phenotype, across one or more organismal generations. This particular example highlights the existence of mechanisms that stably modulate metabolic set-points and responsiveness that are distinct from those of ‘classical’ metabolic regulation. Current Opinion in Cell Biology 2015, 33:88–94

www.sciencedirect.com

Epigenetic modulation of metabolic decisions O¨st and Pospisilik 89

Figure 1

Sensitive periods for environmental input

embryogenesis and early postnatal period

sperm egg

One-cell embryo

Mesenchymal Prestem cells adipocytes

no data

no data

Diverse expression of adipogenic genes

Early adipocytes

E 14-18

1

Adipocytes Continus adipogenesis & turnover in adulthood

early postnatal

2

3

adulthood

early postnatal

adulthood

trimester of embryogenesis

Sensitive periods Current Opinion in Cell Biology

Plasticity of cell differentiation. The traditional view of cell differentiation pictures cell differentiation as a linear event leading to a predestined uniform cell population. Intergenerational/transgenerational inheritance of environmental states and epigenetic modulation of tissue generation now suggests that there is a substantial plasticity in these processes. In the figure this is illustrated for adipocyte development. The variation within a linage specification is set by parental, embryonic and early life cues and the sensitive periods correlate with vital stages in the process in cell differentiation.

(Figure 1). Considering this challenge, one of the simplest ways for a system to generate coordinated phenotypic outcomes evolutionarily would be to have them hard-wired into the chromatin state encoding of our genes within the genome. Indeed, our own data suggest that this is indeed the case [20]. Parentally induced effects aside, there appear to be plastic periods during development where epigenetic memories can be additionally set or modulated. Children born small for gestational age, most often as a result of in utero undernutrition in the third trimester, have a higher risk of developing obesity and complex metabolic disease as adults [21]. This risk is heightened if it occurs in www.sciencedirect.com

conjunction with rapid postnatal catch-up. Mimicking this situation in mice gives the same result and restricting food availability in early life to avoid the catch-up, for instance, results in smaller but metabolic healthier adults [22]. In general, sensitive periods for adipocyte programming appear to correlate with critical periods in the differentiation process (reviewed in [23]). The developmental time-point for adipogenesis differs between species. Adipocyte tracing experiments in mice show that adipocyte commitment occurs at E14-18 although adult adipocyte morphology is developed first after birth [24]. In humans, adipose tissue first appears during the second trimester of gestation [25], and compared with rodents, is further developed at birth. Data from the Dutch famine Current Opinion in Cell Biology 2015, 33:88–94

90 Cell regulation

study suggests that the second and third trimesters are the most sensitive periods when it comes to birth weight and adult glucose intolerance in humans (Figure 1). Starvation during the first trimester does not affect birth weight but is associated with increased risk of adult obesity [26]. Despite a constant annual turnover rate in humans of 10% annually [27], the adult fat cell number stays constant in lean and obese individuals, even after marked weight loss. These findings suggest that the developmental and metabolic decisions that define adipocyte cell number are set early in development [27]. The example above comprises one of hundreds of regulatory features of metabolic homeostasis. If we consider the many developmental and the functional set points encompassing all metabolically relevant tissues, the potential template for epigenetic reprogramming of metabolic state is vast, and, the outcomes will critically depend on the timing of nutritional challenge (reviewed in [28,29]). The net consequence of transient external stimuli will differ in a multi-potent one-cell zygote, in an embryo, a postnatal preadipocyte, and in terminally differentiated adipocytes in an adult. The final outcome of postnatal environmental stimuli will most likely be a mix of morphological changes, such as number of adipocytes, and epigenetic-based memories within each cell. Therapeutically, it would be useful to modulate epigenetic programs in terminally differentiated cells to obtain healthy metabolic profiles. Understanding the precise construction of epigenetic programs is the key to future reprogramming therapies in metabolic disease.

Epigenetic regulation of adipogenesis and adipocyte diversity A crucial step in adipogenesis is the reciprocal upregulation of Cebpa and Pparg controlling the transcription of adipogenic genes including Glut4, Fasn and Lpl. The H3K9 methyltransferases Suv39h1, Setdb1 and G9a inhibit Cebpa and/or Pparg transcription and thus adipogenesis [30,31], and congruently, the H3K9 demethylases Lsd1 (also called Kdm1A) and Phf2 favor adipogenesis [32–34]. The decrease in H3K9 methylation during adipogenesis correlates with an upregulation of Lsd1 and activation of Pfh2 [32,33]. Lsd1 has been described to demethylate H3K4me1,2 or H3K9 depending on the situation. In the case of adipogenesis it appears to work as an H3K9me2 demethylase, providing a permissive chromatin environment for deposition of H3K4me2 [31], a process possibly executed by the H3K4 methylase Mll3 [35]. An additional level of gene control in the context of adipogenesis is provided by the H3K27 methyltransferase Ezh2. Wnt genes, prominent negative regulators of adipogenesis, are repressed by Ezh2 and its activity throughout adipogenesis [36]. While far from systematically tested, a notable theme in the studies above is that changes in epigenetic tone do not seem to be deterministic for adipogenesis but rather Current Opinion in Cell Biology 2015, 33:88–94

modulatory. The idea is at least consistent with the role of chromatin modifying factors in regulating transcriptional noise and phenotypic variation (reviewed in [37]). In vitro, adipocyte phenotype can be shifted by simple modulation of glucose concentration during differentiation [38]. In this context the regulation of the glucose transporter, Glut4, and the glycolytic mediators hexokinase 2 (Hk2), phosphofructokinase-1 (Pfk), and lactate dehydrogenase A (Ldha) appear involved. Interestingly, these stable shifts in adipogenic transcriptional programs correlate with ATP citrate lyase (ACL) activity and H3 acetylation. As is likely true for most culture systems, substantial heterogeneity of preadipocytes exist that carry heritable phenotypic potential and the same may be true for in vivo precursors [39]. Preadipocytes from in utero starved newborn rats show increased adipogenic potential, including increased expression of PPARg, before the onset of obesity [40]. It is now clear from work in many labs and systems that cellular differentiation and ultimate phenotype, while very robust, are highly sensitive to multiple stimuli, including those modulating chromatin control.

The dynamic chromatin-energy landscape As mentioned above, chromatin modification has been postulated to contribute mechanistically to the establishment of epigenetic memories. That said, they are also directly sensitive to metabolism, and in particular, to those metabolites that serve as substrates for post-translational modification reactions such as acetylation and methylation. Extracellular availability of glucose, glutamine and growth factors will promote the flux of carbon into macromolecular synthesis such as fatty acids, membrane lipids and amino acids rather than full breakdown to single carbons to generate ATP [41]. In this anabolic scenario, intra-cellular acetyl-CoA pools increase (Figure 2a). In several cell types a large part, but not all, of histone acetylation is dependent on ATP Citrate Lyase, ACL [38]. In proliferating cells, it was recently demonstrated that there is an alternative source to ACLdependent generation of acetyl-CoA. Upon epidermal growth factor (EGF) stimuli, pyruvate dehydrogenase translocates from mitochondria to the nucleus where it converts pyruvate to acetyl-CoA supporting specific histone acetylation and S-phase entry [42]. In yeast as well as in mammalian cell lines there a strong positive correlation between glucose, cytosolic acetyl-CoA, histone acetylation and expression of growth-related genes (reviewed in [43,44]). Concomitant de novo fatty acid synthesis and reduced b-oxidation presumably lead to an accumulation of FAD, an activator of LSD1. LSD1 regulates SREBP-1 dependent fatty acid synthase (FAS) transcription and plays a positive role in de novo lipogenesis [45]. Considering the important role of H3K9me2 demethylation of LSD1 in adipogenesis, one can speculate that the FAS promotor in hepatocytes likewise is controlled by www.sciencedirect.com

Epigenetic modulation of metabolic decisions O¨st and Pospisilik 91

Figure 2

(a)

Glucose Glut1-4

Transcription of FAS

Glucose

FBP1, G6Pase

Transcription of Glut4, HK2

Glycogenesis De Novo Fatty Acid synthesis

Pyruvate

Ac-CoA

Ac

FAS

Me

Ac

H3K9? H3K4

Ac

Citrate

TCA

FAD LSD1 HAT

CoA

(b)

Glucose Glut1-4

Glucose

Lipolysis β-oxidation

Transcription of Pparα

NAD+ rRNA

Glycogenolysis

Ac Ac

Pyruvate SIRT1 SUV39H1 Ac-CoA

Me Me

TSA

H3K9

Me H3K9

H3K36 α-ketoglutarate KDM3A

KDM2A

(c) Exogenous nutrients or Strored energy

Transcription of metabolic genes

AMPLIFICATION LOOP ensures fast response to metabolic fluctuations

Metabolites

Epigenetic enzyme

Hormones, Stress or Ancestral nutrition metabolic inflexibility

Current Opinion in Cell Biology

Recent insights into the molecular interactions of metabolism and chromatin control. (a) Glucose excess will be stored as glycogen and triglycerides. Cytosolic acetyl-CoA produced during de novo fatty acid synthesis is the substrate for histone acetyltransferases (HATs). Histone acetylations are in general associated with an anabolic and growth related transcriptome. FAD activated histone demethylase, LSD1, regulate transcription that favors fatty acid synthesis over gluconeogenesis. (b) In a catabolic situation, stores of glycogen and triglycerides will be used, and ATP production is prioritized before macromolecular synthesis. As a consequence, the carbons in glucose and fatty acids will to a larger extent be catabolized to CO2 via full TCA cycles. The TCA cycle intermediate a-ketoglutarate is an activator of several KDMs, including KDM3A and KDM2A that control Ppara and rDNA transcription, respectively, during starvation. The starvation sensitive complex of NML/SIRT1/SUV39H1 also targets the rDNA locus, reducing histone acetylation and enhancing H3K9me2 to turn of rRNA transcription. (c) Schematic model of a metabolism-chromatin based amplification loop. The many interactions between metabolism and chromatin modifying enzymes indicate a generalized function of chromatin modifying enzymes in metabolic responses.

www.sciencedirect.com

Current Opinion in Cell Biology 2015, 33:88–94

92 Cell regulation

demethylation of this repressive mark. The authors show that LSD1 demethylase activity is needed but what specific site it demethylates is unknown. Another study shows that LSD1 inhibits gluconeogenic genes such as FBP1 and G6Pase via demethylation of the active H3K4me2 site [46]. In addition to promoting adipogenesis, therefore, LSD1 appears to balance metabolic control to favor de novo fatty acid synthesis over gluconeogenesis. In the absence of glucose, glutamine and growth factors, cells need to rely on stored energy. In this situation ATP synthesis is prioritized, and more carbon goes through the full TCA cycle and leaves the body as CO2 thereby restricting anabolism of carbon-based macromolecules (Figure 2b). Histone demethylases KDM2 through KDM7 are dependent on the TCA cycle intermediate a-ketoglutarate (also called 2-oxoglutarate). In line with a catabolic state induced upregulation of a-ketoglutarate, two KDMs have been shown to be activated by starvation and adrenergic stimulation. KDM2A demethylation of the active H3K36me1/2 marks is associated with repressed rDNA transcription during starvation [47] and KDM3A facilitates beta-adrenergic-stimulated glycerol release and oxygen consumption in brown fat and skeletal muscle by demethylating H3K9me2 on the Ppara enhancer PPRE [48]. In agreement with a role in fat oxidation, KDM3A deficient mice develop obesity phenotypes without hyperphagia [48,49]. Under conditions of prolonged catabolism, the rate of NADH oxidation in the respiratory chain exceeds the reduction of NAD+ by glycolysis and the TCA cycle thereby increasing the NAD+/NADH ratio. The resulting elevation in NAD+ activates the histone deacetylase SIRT1. SIRT1 has been found in an energy sensitive protein complex with NML and SUV39H1 that, during starvation, decreases H3Ac and increases H3K9me2 on rDNA [42]. Both SIRT1 and SUV39H1 are required for this energy dependent transcriptional repression that protects cells from starvationinduced apoptosis. In all, catabolic state is reflected in a general increase of H3K9me2 and decreased histone acetylation on the rDNA locus [50]. Thus, metabolic state and metabolites are important, well-documented players in chromatin regulation and the sensitivity of epigenetic enzymes to these intermediate metabolites represents a way to directly translate metabolic flux changes into virtually instantaneous transcriptional responses. In humans, as in mice, DNA methylation is highly plastic to, and correlates with, altered physiological states and outcomes [51,52]. Indeed, such acute chromatin state flexibility is one of the primary challenges when trying to experimentally prove causal elements for metabolic decisions.

Metabo-epigenetic (epi-metabolic) self-sustaining loops The intricate inter-dependence between metabolites and chromatin modifications provides an ideal template for Current Opinion in Cell Biology 2015, 33:88–94

the generation of positive feedback loops, a common and essential modality to ensure biological robustness [53]. A textbook example of a metabolic positive feedback loop is the regulation of the lacZ operon in yeast. Lactose induces a conformational change in the lactose repressor allowing the transcription of b galactosidase, b-galactoside permease and b-galactoside transacetylase thereby promoting more uptake and metabolism of lactose. Similarly, in 3T3-L1 cells, an increase in glucose leads to an ACL dependent increase of cytosolic acetyl-CoA, histone acetylation and Glut4, Hk2, Ldha and Pfk transcription [38]. When translated these transcripts will enhance glucose uptake and glycolysis reinforcing a positive feed forward loop. This loop presumably holds as long as extracellular sugar is high. Another example is that a-ketoglutarate that controls the activity of KDM3A likely is generated as a consequence of its activation. Tateishi et al. showed that isoproterenol-induced oxygen consumption is at least in part KDM3A dependent [48]. The increase in oxygen consumption is consistent with increased KDM3A dependent lipolysis and PPARa transcription, increased beta-oxidation and increased flux through the TCA cycle including the intermediary, a-ketoglutarate. These examples of metabolism and chromatin interdependence indicate the existence of ‘metabo-epigenetic’ feedback loops, whose potential roles include stabilization of phenotypic output. Possibly, as in the case of LacZ operon regulation, these loops enable amplification circuitry for distinct metabolic situations as well (Figure 2c).

Regulation of epigenetic factor availability KDM4B (also called JMJD2B) is regulated by both ERa and HIF-1a [54], and KDM4 in complex with MLL2 is needed for shifting the balance of methylation from the repression associated H3K9 to the activation associated H3K4 and promoting ERa mediated transcription, thus establishing yet another feed-forward loop [55]. Similarly, activated p53 expression upregulates KDM4B and downregulates SUV39H1, resulting in decreased H3K9me3 and enhanced transcriptional activity on p53 target genes [56,57,58]. This SUV39H1-KDM4B rebalancing has been shown to reduce heterochromatic integrity thereby supporting DNA double strand break repair [47]. Of note, KDM4B stability is regulated by Hsp90 [59] and heatshock can generate or erase defined inter/transgenerational effects [12,20,60–62]. Moreover, we have found that extremes of dietary sugar in Drosophila males, two days before mating, elicits enhanced obesity-susceptibility as well as a slight down-regulation of multiple heterochromatic factors in their offspring [20]. This is reflected in upregulation of Su(var)3-9/SetDB1/Su(var)4-20 controlled genes. Remarkably, these gene-sets include members of multiple metabolic pathways including glycolysis and fatty acid synthesis. Thus, the availability of epigenetic factors is not constant, but rather regulated by hormones, cellular stress and ancestral nutrition. www.sciencedirect.com

Epigenetic modulation of metabolic decisions O¨st and Pospisilik 93

Conclusions It is an attractive hypothesis that the mechanisms above underlie a portion of the metabolic inflexibility observed in complex metabolic disease (Figure 2c). At this point, the big promises touted in the literature and media for epigenetic therapies need much additional research. The research community is avidly probing mechanisms for transgenerational and developmental reprogramming phenotypes but a full understanding will take time. There are at least eight different metabolic tissues that interact, multiple environmental inputs, multiple time-windows (maternal, paternal and zygotic) and many layers of epigenetic modulation that appear to be integrated to signal phenotype. Perhaps the biggest hurdle is the plasticity of the systems in question and their sensitivity to both known and unknown environmental inputs. These appear as noise in the system and we are just beginning to learn how best to minimize, control and learn from these. In order for the field to move forward effectively, these issues, which heavily affect the reproducibility of all models, need to be freely and well communicated. They are not to be judged as flaws or caveats, but rather to be highlighted as challenges and interesting features of the system. A departure from conventional approaches is in order. The payoff for the community will be profound. After all, obesityrelated disease represents one of the greatest socioeconomic issues of our day.

Acknowledgements We apologize to all colleagues whose work could not be extensively quoted ¨ was supported by Swedish VR K2011-78PK-21893in this short review. AO 01-2 and SSMF grants. JAP was supported by the Max-Planck Society, EU (NoE Epigenesys), BMBF (DEEP), the DFG and the ERC (281641).

References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:  of special interest  of outstanding interest 1.

Berger SL, Kouzarides T, Shiekhattar R, Shilatifard A: An operational definition of epigenetics. Genes Dev 2009, 23:781-783.

2.

Somer RA, Thummel CS: Epigenetic inheritance of metabolic state. Curr Opin Genet Dev 2014, 27:43-47.

3.

Aiken CE, Ozanne SE: Transgenerational developmental programming. Hum Reprod Update 2014, 20:63-75.

4.

Lim JP, Brunet A: Bridging the transgenerational gap with epigenetic memory. Trends Genet 2013, 29:176-186.

5.

Patti ME: Intergenerational programming of metabolic disease: evidence from human populations and experimental animal models. Cell Mol Life Sci 2013, 70:1597-1608.

6.

Morgan HD, Sutherland HG, Martin DI, Whitelaw E: Epigenetic inheritance at the agouti locus in the mouse. Nat Genet 1999, 23:314-318.

7.

Rakyan VK, Chong S, Champ ME, Cuthbert PC, Morgan HD, Luu KV, Whitelaw E: Transgenerational inheritance of epigenetic states at the murine Axin(Fu) allele occurs after maternal and paternal transmission. Proc Natl Acad Sci U S A 2003, 100:2538-2543.

www.sciencedirect.com

8.

Anway MD, Cupp AS, Uzumcu M, Skinner MK: Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science 2005, 308:1466-1469.

9.

Meyer P, Heidmann I, Niedenhof I: Differences in DNAmethylation are associated with a paramutation phenomenon in transgenic petunia. Plant J 1993, 4:89-100.

10. Walker EL: Paramutation of the r1 locus of maize is associated with increased cytosine methylation. Genetics 1998, 148:1973. 11. Greer EL, Maures TJ, Ucar D, Hauswirth AG, Mancini E, Lim JP, Benayoun BA, Shi Y, Brunet A: Transgenerational epigenetic inheritance of longevity in Caenorhabditis elegans. Nature 2011, 479:365-371. 12. Seong KH, Li D, Shimizu H, Nakamura R, Ishii S: Inheritance of stress-induced, ATF-2-dependent epigenetic change. Cell 2011, 145:1049-1061. 13. Lillycrop KA, Phillips ES, Jackson AA, Hanson MA, Burdge GC: Dietary protein restriction of pregnant rats induces and folic acid supplementation prevents epigenetic modification of hepatic gene expression in the offspring. J Nutr 2005, 135:1382-1386. 14. Burdge GC, Slater-Jefferies J, Torrens C, Phillips ES, Hanson MA, Lillycrop KA: Dietary protein restriction of pregnant rats in the F0 generation induces altered methylation of hepatic gene promoters in the adult male offspring in the F1 and F2 generations. Br J Nutr 2007, 97:435-439. 15. Carone BR, Fauquier L, Habib N, Shea JM, Hart CE, Li R, Bock C, Li C, Gu H et al.: Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell 2010, 143:1084-1096. 16. Masuyama H, Hiramatsu Y: Effects of a high-fat diet exposure in utero on the metabolic syndrome-like phenomenon in mouse offspring through epigenetic changes in adipocytokine gene expression. Endocrinology 2012, 153:2823-2830. 17. Ng SF, Lin RC, Laybutt DR, Barres R, Owens JA, Morris MJ: Chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring. Nature 2010, 467:963-966. 18. Sandovici I, Smith NH, Nitert MD, Ackers-Johnson M, UribeLewis S, Ito Y, Jones RH, Marquez VE, Cairns W et al.: Maternal diet and aging alter the epigenetic control of a promoter– enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci U S A 2011, 108:5449-5454. 19. Radford EJ, Ito M, Shi H, Corish JA, Yamazawa K, Isganaitis E, Seisenberger S, Hore TA, Reik W et al.: In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science 2014, 345:1255903. 20. Ost A, Lempradl A, Casas E, Weigert M, Tiko T, Deniz M,  Pantano L, Boenisch U, Itskov PM et al.: Paternal diet defines offspring chromatin state and intergenerational obesity. Cell 2014, 159:1352-1364. We show that an acute paternal diet intervention results in epigenetic changes and metabolic phenotypes in offspring. 21. Barker DJ, Hales CN, Fall CH, Osmond C, Phipps K, Clark PM: Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth. Diabetologia 1993, 36:62-67. 22. Jimenez-Chillaron JC, Hernandez-Valencia M, Lightner A, Faucette RR, Reamer C, Przybyla R, Ruest S, Barry K, Otis JP, Patti ME: Reductions in caloric intake and early postnatal growth prevent glucose intolerance and obesity associated with low birthweight. Diabetologia 2006, 49:1974-1984. 23. Lukaszewski MA, Eberle´ D, Vieau D, Breton C: Nutritional manipulations in the perinatal period program adipose tissue  in offspring. Am J Physiol Endocrinol Metab 2013, 305: E1195-E1207. A very extensive review covering the developmental timing of adipogenesis in different species and how nutritional changes during sensitive periods affect metabolic health in adults. 24. Wang QA, Tao C, Gupta RK, Scherer PE: Tracking adipogenesis during white adipose tissue development, expansion and regeneration. Nat Med 2013, 19:1338-1344. Current Opinion in Cell Biology 2015, 33:88–94

94 Cell regulation

25. Poissonnet CM, Burdi AR, Garn SM: The chronology of adipose tissue appearance and distribution in the human fetus. Early Hum Dev 1984, 10:1-11. 26. Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, Bleker OP: Glucose tolerance in adults after prenatal exposure to famine. Lancet 1998, 351:173-177. 27. Spalding KL, Arner E, Westermark PO, Bernard S, Buchholz BA, Bergmann O, Blomqvist L, Hoffstedt J, Na¨slund E et al.: Dynamics of fat cell turnover in humans. Nature 2008, 453:783-787.

45. Abdulla A, Zhang Y, Hsu FN, Xiaoli AM, Zhao X, Yang ES, Ji JY, Yang F: Regulation of lipogenic gene expression by lysine specific histone demethylase-1 (LSD1). J Biol Chem 2014, 289:29937-29947. This article shows the importance of LSD1 in lipid metabolism. 46. Pan D, Mao C, Wang YX: Suppression of gluconeogenic gene expression by LSD1-mediated histone demethylation. PLOS ONE 2013, 8:e66294.

28. Godfrey KM, Barker DJ: Fetal nutrition and adult disease. Am J Clin Nutr 2000, 71:1344S-1352S.

47. Tanaka Y, Okamoto K, Teye K, Umata T, Yamagiwa N, Suto Y, Zhang Y, Tsuneoka M: JmjC enzyme KDM2A is a regulator of rRNA transcription in response to starvation. EMBO J 2010, 29:1510-1522.

29. Duque-Guimara˜es DE, Ozanne SE: Nutritional programming of insulin resistance: causes and consequences. Trends Endocrinol Metab 2013, 24:525-535.

48. Tateishi K, Okada Y, Kallin EM, Zhang Y: Role of Jhdm2a in regulating metabolic gene expression and obesity resistance. Nature 2009, 458:757-761.

30. Wang L, Xu S, Lee JE, Baldridge A, Grullon S, Peng W, Ge K: Histone H3K9 methyltransferase G9a represses PPARg expression and adipogenesis. EMBO J 2013, 32:45-59.

49. Inagaki T, Tachibana M, Magoori K, Kudo H, Tanaka T, Okamura M, Naito M, Kodama T, Shinkai Y, Sakai J: Obesity and metabolic syndrome in histone demethylase JHDM2adeficient mice. Genes Cells 2009, 14:991-1001.

31. Zhang ZC, Liu Y, Li SF, Guo L, Zhao Y, Qian SW, Wen B, Tang QQ,  Li X: Suv39h1 mediates AP-2a-dependent inhibition of C/EBPa expression during adipogenesis. Mol Cell Biol 2014, 34: 2330-2338. This paper clearly points out H3K9me3-centric inhibition of the Cebpa promotor as a vital factor in inhibiting adipogenesis. 32. Musri MM, Carmona MC, Hanzu FA, Kaliman P, Gomis R, Pa´rrizas M: Histone demethylase LSD1 regulates adipogenesis. J Biol Chem 2010, 285:30034-30041. 33. Okuno Y, Ohtake F, Igarashi K, Kanno J, Matsumoto T, Takada I, Kato S, Imai Y: Epigenetic regulation of adipogenesis by PHF2 histone demethylase. Diabetes 2013, 62:1426-1434. 34. Lee KH, Ju UI, Song JY, Chun YS: The histone demethylase PHF2 promotes fat cell differentiation as an epigenetic activator of both C/EBP alpha and C/EBP delta. Mol Cells 2014, 37:734-741. 35. Lee J, Saha PK, Yang QH, Lee S, Park JY, Suh Y, Lee SK, Chan L, Roeder RG, Lee JW: Targeted inactivation of MLL3 histone H3-Lys-4 methyltransferase activity in the mouse reveals vital roles for MLL3 in adipogenesis. Proc Natl Acad Sci U S A 2008, 105:19229-19234. 36. Wang L, Jin Q, Lee JE, Su IH, Ge K: Histone H3K27 methyltransferase Ezh2 represses Wnt genes to facilitate adipogenesis. Proc Natl Acad Sci U S A 2010, 107:7317-7322. 37. Whitelaw NC, Chong S, Whitelaw E: Tuning in to noise: epigenetics and intangible variation. Dev Cell 2010, 19:649-650. 38. Wellen KE, Hatzivassiliou G, Sachdeva UM, Bui TV, Cross JR, Thompson CB: ATP-citrate lyase links cellular metabolism to histone acetylation. Science 2009, 324:1076-1080. 39. Katz LS, Geras-Raaka E, Gershengorn MC: Heritability of fat accumulation in white adipocytes. Am J Physiol Endocrinol Metab 2014, 307:E335-E344. 40. Yee JK, Lee WN, Ross MG, Lane RH, Han G, Vega J, Desai M:  Peroxisome proliferator-activated receptor gamma modulation and lipogenic response in adipocytes of small-forgestational age offspring. Nutr Metab (Lond) 2012, 9:62. This article shows that in utero starvation in rats programs preadipocytes to an obesogenic profile evident before obesity onset. 41. Ward PS, Thompson CB: Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell 2012, 21:297-308. 42. Sutendra G, Kinnaird A, Dromparis P, Paulin R, Stenson TH, Haromy A, Hashimoto K, Zhang N, Flaim E, Michelakis ED: A nuclear pyruvate dehydrogenase complex is important for the generation of acetyl-CoA and histone acetylation. Cell 2014, 158:84-97. 43. Kaelin WG, McKnight SL: Influence of metabolism on epigenetics and disease. Cell 2013, 153:56-69. 44. Salminen A, Kaarniranta K, Hiltunen M, Kauppinen A: Krebs cycle dysfunction shapes epigenetic landscape of chromatin: novel insights into mitochondrial regulation of aging process. Cell Signal 2014, 26:1598-1603. Current Opinion in Cell Biology 2015, 33:88–94

50. Murayama A, Ohmori K, Fujimura A, Minami H, YasuzawaTanaka K, Kuroda T, Oie S, Daitoku H, Okuwaki M et al.: Epigenetic control of rDNA loci in response to intracellular energy status. Cell 2008, 133:627-639. 51. Milagro FI, Campio´n J, Cordero P, Goyenechea E, Go´mezUriz AM, Abete I, Zulet MA, Martı´nez JA: A dual epigenomic approach for the search of obesity biomarkers: DNA methylation in relation to diet-induced weight loss. FASEB J 2011, 25:1378-1389. 52. Barre`s R, Yan J, Egan B, Treebak JT, Rasmussen M, Fritz T, Caidahl K, Krook A, O’Gorman DJ, Zierath JR: Acute exercise remodels promoter methylation in human skeletal muscle. Cell Metab 2012, 15:405-411. 53. Kitano H: Biological robustness. Nat Rev Genet 2004, 5:826-837. 54. Yang J, Jubb AM, Pike L, Buffa FM, Turley H, Baban D, Leek R, Gatter KC, Ragoussis J, Harris AL: The histone demethylase JMJD2B is regulated by estrogen receptor alpha and hypoxia, and is a key mediator of estrogen induced growth. Cancer Res 2010, 70:6456-6466. 55. Shi L, Sun L, Li Q, Liang J, Yu W, Yi X, Yang X, Li Y, Han X et al.: Histone demethylase JMJD2B coordinates H3K4/H3K9 methylation and promotes hormonally responsive breast carcinogenesis. Proc Natl Acad Sci U S A 2011, 108:7541-7546. 56. Palomera-Sanchez Z, Bucio-Mendez A, Valadez-Graham V, Reynaud E, Zurita M: Drosophila p53 is required to increase the levels of the dKDM4B demethylase after UV-induced DNA damage to demethylate histone H3 lysine 9. J Biol Chem 2010, 285:31370-31379. 57. Mungamuri SK, Benson EK, Wang S, Gu W, Lee SW, Aaronson SA:  p53-Mediated heterochromatin reorganization regulates its cell fate decisions. Nat Struct Mol Biol 2012, 19:478-484 S1. This paper adds that p53 activation downregulates SUV39H1 to reduce H3K9me3 and activates p53 target genes. 58. Zheng H, Chen L, Pledger WJ, Fang J, Chen J: p53 promotes repair of heterochromatin DNA by regulating JMJD2b and SUV39H1 expression. Oncogene 2014, 33:734-744. 59. Ipenberg I, Guttmann-Raviv N, Khoury HP, Kupershmit I, Ayoub N: Heat shock protein 90 (Hsp90) selectively regulates the stability of KDM4B/JMJD2B histone demethylase. J Biol Chem 2013, 288:14681-14687. 60. Cavalli G, Paro R: The Drosophila Fab-7 chromosomal element conveys epigenetic inheritance during mitosis and meiosis. Cell 1998, 93:505-518. 61. Specchia V, Piacentini L, Tritto P, Fanti L, D’Alessandro R, Palumbo G, Pimpinelli S, Bozzetti MP: Hsp90 prevents phenotypic variation by suppressing the mutagenic activity of transposons. Nature 2010, 463:662-665. 62. Ruden DM, Lu X: Hsp90 affecting chromatin remodeling might explain transgenerational epigenetic inheritance in Drosophila. Curr Genomics 2008, 9:500-508. www.sciencedirect.com