Genetic and epigenetic control of metabolic health.

Review

Genetic and epigenetic control of metabolic health %

Robert Wolfgang Schwenk, Heike Vogel, Annette Schu¨rmann * ABSTRACT Obesity is characterized as an excess accumulation of body fat resulting from a positive energy balance. It is the major risk factor for type 2 diabetes (T2D). The evidence for familial aggregation of obesity and its associated metabolic diseases is substantial. To date, about 150 genetic loci identified in genome-wide association studies (GWAS) are linked with obesity and T2D, each accounting for only a small proportion of the predicted heritability. However, the percentage of overall trait variance explained by these associated loci is modest ( 5–10% for T2D, 2% for BMI). The lack of powerful genetic associations suggests that heritability is not entirely attributable to gene variations. Some of the familial aggregation as well as many of the effects of environmental exposures, may reflect epigenetic processes. This review summarizes our current knowledge on the genetic basis to individual risk of obesity and T2D, and explores the potential role of epigenetic contribution.

& 2013 The Authors. Published by Elsevier GmbH. All rights reserved.

Keywords Obesity; Type 2 diabetes; GWAS; Positional cloning; Epigenetics

1. INTRODUCTION Obesity is a worldwide epidemic associated with several health problems, including diabetes, cardiovascular disease, and cancer. Principally, it is a consequence of energy imbalance due to chronic fuel surfeit and reduced activity. In this regard the hypothalamus is of great interest as it integrates peripheral hormonal and neuronal signals of satiety and nutritional status [1–3]. It directly senses nutrients [4,5], regulates their utilization [6] and controls glucose homeostasis [7–9] as well as peripheral lipid metabolism [10–12]. Despite the tight hypothalamic control of energy balance, obesity develops in some individuals [13]. Therefore, genes and epigenetic markers affecting metabolic equilibrium are the primary focus of research into obesity predisposition. A major consequence of obesity is the development of T2D, which is characterized by destruction of pancreatic beta-cells following extended periods of insulin resistance and hyperglycemia [14]. In the early stages, insulin resistance is compensated by elevated insulin secretion and as a result many subjects are able to control blood glucose concentrations for extended periods. However, in a subgroup of patients an exhaustion of beta-cells occurs more rapidly, resulting in insufficient insulin responses to nutrient influx and further aggravation of hyperglycemia as well as hyperlipidemia, leading to accelerated beta-cell destruction [15,16]. This

chronic hyperglycemia is finally responsible for long-term damage and failure of multiple organs, such as the eyes, kidneys, nerves, heart, and blood vessels [14–16]. In concordance with obesity, T2D may be attributable to susceptibility genes and epigenetic alterations that underlie the initial mechanism of damage which, once hyperglycemia has manifested, results in beta-cell failure [14,15,17].

2. GENETIC FACTORS OF OBESITY AND T2D 2.1. Human studies The heritability of obesity and T2D in twin and adoption studies ranges from 45% to 85% and remains substantial in familial research [18–20]. Rare Mendelian syndromes with associated obesity have been revealed and especially linkage studies have identified regions on all chromosomes possibly harboring obesity/diabetes genes [21]. Since 2005, genome-wide association studies (GWAS) using hundreds of thousands of single nucleotide polymorphisms (SNPs) have contributed to a major leap forward in understanding the genetic basis of obesity (see reviews [22–24]). However, most of these SNPs have no clear functional significance. Up to mid-2012, about 40 genetic loci were described to be associated with obesity or body mass index (BMI)

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*Corresponding author. Tel.: þ49 33200 882368; fax: þ 49 33200 882334. Email: [email protected] (A. Schu¨rmann). Abbreviations: ADCY3, adenylate cyclase 3; AQP9, aquaporin 9; BDNF, brain-derived neurotrophic factor; CDKAL1, CDK5 regulatory subunit associated protein 1-like 1; CPEB4, cytoplasmic polyadenylation element binding protein 4; DUSP8, dual specificity phosphatase 8; DUSP22, dual specificity phosphatase 22; GALNT10, UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 10 (GalNAc-T10); GIPR, gastric inhibitory polypeptide receptor; GNPDA2, glucosamine-6-phosphate deaminase 2; GP2, glycoprotein 2 (zymogen granule membrane); HIPK3, homeodomain interacting protein kinase 3; IFI16, interferon, gamma-inducible protein 16; KCNQ1, potassium voltage-gated channel, KQT-like subfamily, member 1; KLHL32, kelch-like family member 32; LEPR, leptin receptor; MAP2K4, mitogen-activated protein kinase kinase 4; MAP2K5, mitogen-activated protein kinase kinase 5; MIR148A, microRNA 148a; MMP9, matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV collagenase); MNDA, myeloid cell nuclear differentiation antigen; NFE2L3, nuclear factor, erythroid 2-like 3; PACS1, phosphofurin acidic cluster sorting protein 1; PAX6, paired box gene 6; PCSK1, proprotein convertase subtilisin/kexin type 1; PGC1, peroxisome proliferative activated receptor, gamma, coactivator 1 alpha, PM2OD1; PRKCH, protein kinase C, eta; PRKD1, protein kinase D1; PRKG1, protein kinase, cGMP-dependent, type I; QPCTL, glutaminyl-peptide cyclotransferase-like; RBJ, DnaJ (Hsp40) homolog, subfamily C, member 27; RFC5, replication factor C (activator 1) 5; RMST, rhabdomyosarcoma 2 associated transcript (non-protein coding); SEC16B, SEC16 homolog B; TFAP2B, transcription factor AP-2 beta (activating enhancer binding protein 2 beta); TNNI3, troponin I type 3 (cardiac); TNNT1, troponin T type 1 (skeletal, slow) Received August 26, 2013 Revision received September 9, 2013 Accepted September 13, 2013 Available online 25 September 2013 http://dx.doi.org/10.1016/j.molmet.2013.09.002

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Review through GWAS and each single variant raises body weight approximately 180–1400 g. For BMI the strongest genetic factors that appear in nearly all studies are FTO, MC4R, and TMEM18 and for T2D TCF7L2, PPARG, and KCNJ11. Therefore, our focus in this review is primarily on the functions of these six genes.

2.1.1. Characteristics of FTO, MC4R, and TMEM18 The FTO (fat mass and obesity associated) gene encodes a protein that has been characterized as a nucleic acid demethylase [25]. Its deletion in mice results in growth retardation, reduced IGF-1 (insulin-like growth factor 1) levels, increased energy expenditure, and altered locomotor activity [26]. Mouse embryonic fibroblasts (MEFs) from Fto / mice exhibited reduced rates of proliferation and mRNA translation. This effect might be the consequence of an impaired action of members of the aminoacyl-tRNA synthesis pathway that interact with FTO and tether free amino acids to their cognate tRNA in the process of protein synthesis [27]. FTO was recently shown to act as an m6AmRNA demethylase in vivo affecting a selective subset of mRNAs that have distinct biological functions especially related to neuronal signal transduction [28]. Hess et al. [28] also demonstrated that FTO is expressed in dopaminergic neurons where its inactivation impairs dopamine receptor-dependent control of neuronal activity and behavioral responses without affecting body weight. Thus, it is speculated that FTO variants may affect not only feeding-related reward processes but also reward-based decisions [28]. This might also explain the observation that FTO variants were inversely correlated with the risk of alcohol dependence [29]. The MC4R (melanocortin-4 receptor) gene encodes a G-protein coupled receptor that is broadly expressed in the central nervous system. In a transgenic mouse line expressing the green fluorescent protein (GFP) under the control of the Mc4r promoter [30], the MC4R was detected in the paraventricular hypothalamic nucleus (PVH). Interestingly, MC4R was found particularly in those cells producing thyrotropin-releasing (TRH) hormone and in cholinergic cells of the dorsal motor nucleus of the vagus (DMV). Recently, MC4R expression was analyzed in human hypothalamus and detected in PVH, the supraoptic nucleus (SON), and the nucleus basalis of Meynert [31]. MC4R is activated by gamma-melanocytestimulating hormone (γ-MSH) that is processed from the prohormone proopiomelanocortin (POMC), whereas its activity is inhibited by agoutirelated peptide (AgRP), which shares sequence homology to the agouti signaling protein (ASIP) [32]. AgRP has been identified in vitro as a protein that competitively antagonizes MC4R and the melanocortin-3 receptor (MC3R). Several important studies discovered that alpha-melanocytestimulating hormone (α-MSH) and AgRP act as independent ligands that inhibit each other's binding and transduce opposite signals at the MC4R [33–35]. In addition to its action as a natural MC3R and MC4R antagonist, AgRP was shown to display inverse agonism in in vitro systems expressing constitutively active MC4R [36,37]. This finding was later confirmed in vivo in the Pomc / mouse [38]. The MC4R is coupled to adenlyate cyclase and acts via mobilizing intracellular calcium [39]. It plays a central role in weight regulation as its activation decreases food intake and elevates energy utilization [40,41], whereas its deletion in mice results in hyperphagia and changes in metabolism [42,43]. The importance of this receptor in appetite and energy regulation is illustrated by naturally occurring mutations that lead to partial or complete dysfunction of MC4R in patients. MC4R mutations are the most common known cause of monogenic obesity, manifesting in patients as a severe early-onset weight gain due to extreme hyperphagia, lack of satiety, and a decline in energy

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utilization [44,45]. As mentioned above, SNPs in the MC4R gene are also important contributors to polygenic obesity [46,47]. Except for the strong association of TMEM18 (transmembrane protein 18) with obesity, little is known about the molecular function of this gene, which is ubiquitously expressed. TMEM18 was discovered as novel modulator that promotes glioma-directed stem cell migration [48] and later detected in neurons of all key brain regions with no significant changes in the hypothalamus and brainstem of mice in response to starvation or feeding [49]. TMEM18 also appears to play a role in fat cell differentiation, as knockdown in 3T3-L1 cells by siRNA resulted in impaired adipocyte maturation [50]. Furthermore, TMEM18 expression was decreased in adipose tissue of obese patients and negatively correlated with anthropometric variables and adipocyte size [50]. Even for these three strong factors that appear in most obesity GWAS it was shown that their common variants (FTO: rs9939609; TMEM18: rs6548238; MC4R: rs17782313) are associated with modest effects on BMI (FTO 0.3, TMEM18 0.26, and MC4R 0.2 kg/m2 per allele) that translate into odds ratios of 1.1–1.3 for obesity [51].

2.1.2. Characteristics of TCF7L2, PPARG, and KCNJ11 TCF7L2 (T-cell factor 7-like 2) is known to be a nuclear effector of the Wnt/β-catenin pathway; activation of Wnt signaling promotes accumulation and nuclear entry of β-catenin, enabling its association with TCF7L2 to promote target gene expression [52]. The phenotypic changes associated with the risk genotype of TCF7L2 suggest that T2D arises as a consequence of reduced islet mass and/or impaired function, and it has become clear that TCF7L2 plays an important role for several vital functions in the pancreatic islet. Five of the 17 exons of the TCF7L2 gene are alternative (exons 4 and 13–16) and four splice variants are predominantly expressed in pancreatic islets [53]. Manipulating Tcf7l2 expression in mice modulates glucose tolerance; a lower expression improves and a higher expression impairs glucose tolerance [54]. The beta-cell specific deletion of Tcf7l2 during early endocrine pancreas development resulted in an age dependent glucose intolerance by 20 weeks of age. Isolated islets of these mice displayed impaired glucose stimulated insulin secretion and a weaker response to the incretin GLP-1 (glucagon-like peptide 1) [55]. These phenotypes are consistent with results obtained by siRNA-mediated silencing of TCF7L2 in isolated human and mouse islets and beta-cell lines [54]. Oh et al. [56] demonstrated that TCF7L2 is also critical in mediating transcriptional control of hepatic glucose production. Hepatic expression of medium and short isoforms of Tcf7l2 was specifically reduced in mouse models of insulin resistance. According to this, specific depletion of Tcf7l2 in the liver of mice increased blood glucose levels, an effect that was associated with glucose intolerance and an up-regulation of gluconeogenic genes [56]. PPARG encodes a member of the peroxisome proliferator-activated receptor (PPAR) subfamily of nuclear receptors and is a regulator of adipocyte differentiation [57–59]. It is most highly expressed in white adipose tissue (WAT) and brown adipose tissue (BAT), where it is a master regulator of adipogenesis as well as a potent modulator of whole-body lipid metabolism and insulin sensitivity [60,61]. Fatty acids, as well as their derivatives and synthetic ligands such as thiazolidinedione (TZD), can bind and activate PPARγ. The latter are potent activators of PPARγ with robust insulin-sensitizing activities [62,63]. Among the diabetes genes identified by GWAS, PPARG2 is one of the few genes in which polymorphisms have been linked to insulin resistance but not to beta-cell function [64].



The ATP-sensitive potassium (KATP) channel is the key for glucose stimulated insulin release from the pancreatic beta-cell and is composed of two subunits, SUR1 and Kir6.2 [65], that are encoded by ABCC8 (ATPbinding cassette, sub-family C (CFTR/MRP), member 8) and KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11) respectively. Mutations in KCNJ11 are a cause of familial persistent hyperinsulinemic hypoglycemia of infancy (PHHI), an autosomal recessive disorder characterized by unregulated insulin secretion [66]. Defects in this gene may also contribute to transient neonatal diabetes mellitus type 3 (TNDM3; [67]) and permanent neonatal diabetes mellitus (PNDM; [68]). Furthermore, polymorphisms in KCNJ11 associate with polygenic T2D [69].

2.1.3. Approaches for the identification of obesity and T2D genes Our current knowledge on genetic variability accounts only for a small fraction of the inter-individual variability of BMI and T2D. Therefore, several approaches have been followed to identify additional obesity and diabetes genes. Novel strategies are to escalate the number of participants, to perform exon sequencing of subjects with robust phenotypes or to screen populations that have not previously been under investigation. For instance, Wen et al. [70] performed a metaanalysis in Asian populations to identify variants associated with BMI in contrast to earlier studies that were conducted in European and North American populations. The group compared their results with former studies in order to allow an evaluation of whether genetic markers of obesity can be generalized to Asians, but also to facilitate the dissection of the genetic architecture of obesity to reveal genetic variants of particular importance to Asians. The authors could confirm 7 loci previously identified in studies conducted on populations of European ancestry (FTO, SEC16B, MC4R, GIPR/QPCTL, ADCY3/RBJ, BDNF, and MAP2K5) and three novel loci overlapping or in close proximity to the CDKAL1, PCSK1, and GP2 genes. Furthermore, three additional loci just below the genome-wide significance threshold were identified, including two previously identified SNPs in the GNPDA2 and TFAP2B genes and a new locus near PAX6. Thus, several variances of the previously reported loci exhibited a lower association in East Asians than in Europeans, while variances for the newly identified loci from the study by Wen et al. [70] were generally larger in East Asians than in Europeans. Recently, Monda et al. [71] conducted a meta-analysis of African ancestry to examine the association of 43.2 million SNPs with BMI in about 40,000 men and women and followed up the most significant associations in an additional 30,000 Africans. Besides the identification of three new loci (GALNT10, MIR148A-NFE2L3, KLHL32), 32 of the 36 previously established BMI variants showed directionally consistent effects providing strong support for shared BMI loci across populations. An integrative method as conducted by Naukkarienen et al. [72], who aimed at identifying obesity genes by utilizing genome-wide transcript profiling of adipose tissue samples and analysis of GWAS data, is an additional approach to clarify the heritability of metabolic diseases. The group performed a transcriptome profile of the adipose tissue of young monozygotic twin pairs that exhibited a 15.4 kg mean weight difference. Expression differences observed between lean and obese monozygotic co-twins in discordant pairs were designated as ‘reactive’ to obesity and were presumed to be the result of lifestyle-induced alterations. Furthermore, the expression pattern of adipose tissue of about 80 unrelated individuals was evaluated and differences were ascertained to correlate with BMI due to a mixture of ‘reactive’ as well as ‘causal’ processes. More than 2500 transcripts were differentially regulated in the fat tissue of the obese twins compared to that of the lean co-twins.


Among the unrelated individuals 84 transcripts correlated with BMI, of which 56 were classified as ‘reactive’ because they overlapped with those identified in the twin study. The remaining 28 transcripts were candidates for being related to causal processes; they strongly correlated with BMI but were not different in the adipose tissue of discordant twins. Indeed, 13 genes out of those candidates harbored a total of 23 SNPs that were nominally associated with BMI [71]. Such an alternative integrative approach combined with comprehensive sequencing data is advantageous as it does not only identify candidate genes and specific loci, but will give us a more distinct picture of the genetic contribution of rare and common variants to complex metabolic diseases. Despite all endeavors, genetic variants identified so far do not account for the observed heritability. Possible explanations for the absent empirically postulated heritability of metabolic disorders might be the presence of (1) a much higher number of gene variants contributing to metabolic diseases, (2) copy number variations (CNV), (3) miRNAs, (4) epigenetics which will be discussed below, and/or (5) more complex alterations such as large deletions or duplications. Wheeler et al. [73] recently combined SNP and CNV analysis in children with extreme differences in body weight and identified in addition to FTO, MC4R, and TMEM18 four new loci associated with severe obesity (LEPR, PRKCH, PACS1, and RMST). Comparing their results with broader studies such as the GIANT analysis [74] the authors observed only a partial overlap (LEPR, POMC, MC4R, BDNF, and SH2B1) between the loci influencing the risk of severe obesity and those influencing the more common obesity. Furthermore, the relative contribution of each locus was different in severe versus common obesity indicating that the genetic predisposition for moderate weight gain and extreme obesity is somewhat different. Interestingly, similar to findings in intellectual disability, severe obesity without a developmental delay was associated with a significantly greater burden of rare, typically singular CNVs. As expected, rare CNVs were found in severely obese cases and those alterations often resulted in a deletion of genes involved in the neuronal regulation of energy homeostasis such as OPRM1 (m-opioid receptor) or OCTR (oxytocin receptor; [73]). The group of Froguel [75] described one example for a larger chromosomal alteration, causing a severe form of obesity often associated with hyperphagia and intellectual disabilities. This 16p11.2 deletion carries around 30 genes and is equated with reduced intelligence and/or congenital anomalies, which may further be associated with obesity [75]. One of the affected genes is SH2B1 (Src-homology 2B adaptor protein 1), in which GWAS meta-analyses already identified obesity risk alleles [76]. In contrast, duplications of this region often result in a low body mass [77]. Another approach that has been initiated by several consortia, e.g. by the Wellcome Trust in the UK, is to identify rare mutations by sequencing the complete genome of 4000 individuals and exome sequencing of a further 6000 persons with diseases (including 2000 with extreme obesity) [78]. 2.2. Mouse studies In order to deepen our understanding about the pathomechanisms and the genetic basis of human obesity and T2D, animal models that closely resemble the human disease are essential. The New Zealand obese (NZO) mouse strain for example is a model of polygenic obesity that also develops T2D in response to glucolipotoxic conditions [17,79]. Like most obese human subjects, NZO mice increase their body weight rapidly due to a combination of hyperphagia, reduced energy expenditure, and insufficient physical activity [80]. Of several polygenic mouse models of



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Review Gene

Cross

Chr.

Function

Reference

B6 DBA Lepob

1

Beta-cell development

[96]

Ifi202b

NZO B6 F2

1

Adipogenesis

[91]

Ube2l6

B6 BALB/c Lepob

2

Adipogenesis

[92]

Zfp69

NZO SJL

4

Lipid storage

[97]

Tbc1d1

NZO SJL

5

Fatty acid oxidation

[84]

Bhlhe40

Akita A/J

6

Fatty acid oxidation

[89]

Tomosyn-2

B6 BTBR Lepob

16

Insulin secretion

[95]

Sorcs1

B6 BTBR Lepob

19

Islet vasculature

[93]

Lisch-like

X

F2 or N2 population Genotype

Phenotype

LOD-Score

Linkage study 18 16 14 12 10 8 6 4 2 0

Table 1: Summary of key obesity and diabetes modifier genes identified by positional cloning in mice.

QTL

• Sequencing • Expression studies Critical fragment Disease gene RCS 1 2 3 4 Figure 1: Experimental strategy for the identification of disease genes by positional cloning. Two mouse strains differing in at least one trait (e.g. body weight) are used to generate a F2 or backcross population. Comparison of genotype and phenotype allows linkage studies for the identification of QTL. Introgression of single QTL and smaller fragments of the QTL to a healthy strain results in recombinant congenic strains that need to be phenotyped. Since lines 1 and 2 exhibit an altered phenotype, whereas lines 3 and 4 do not, a critical interval can be defined. The responsible gene variant within this interval can be identified by sequencing and expression profiling.

obesity, such as the well-characterized TSOD, KK and the TallyHO strain (http://phenome.jax.org), NZO mice exhibit the most severe phenotype with fat depots accounting for more than 40% of total body weight at 6 months of age [81]. A powerful tool for the identification of gene variants linked to obesity and diabetes is the conventional strategy of positional cloning (Figure 1). This method is initiated with an intercross of mouse strains that differ in one or more traits of interest, e.g. body weight or blood glucose, in order to identify causative genomic regions that are designated quantitative trait loci (QTL). To narrow down these QTL, the loci are broken down into smaller fragments by generating recombinant congenic lines that lead to introgression of various components of the loci onto the background of a healthy strain. This strategy allows defining a critical genomic fragment and finally to identify the disease gene(s) by fine-mapping of the narrowed region (Figure 1) [82]. A general overview for a protocol of chromosomal mapping and subsequent characterization of the fragment harboring the disease gene is summarized in the review by Brockmann and Neuschl [82]. Here, we give a summary of the key obesity and T2D modifier genes discovered in mouse studies by positional cloning thus far (Table 1). Genome-wide linkage analysis of an outcross population of NZO with the lean SJL mice exposed a major QTL (Nob1) for obesity on Chr. 5 [83]. The subsequent breeding of recombinant congenic mouse lines in combination with gene-expression studies led to the identification of Tbc1d1 (TBC1 domain family, member 1) as the causal gene variant underlying the Nob1 QTL. The lean SJL strain carries a loss-of-function variant of the protein

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generated by a 7 bp in-frame deletion, producing a truncated version of the Rab-GAP protein. The obesity suppressing potential of this mutation was demonstrated in recombinant congenic mice lacking Tbc1d1. They were protected against diet-induced obesity, as indicated by reduced body weight, decreased respiratory quotient, increased fatty acid oxidation, and reduced glucose uptake in isolated skeletal muscle [84], an effect later confirmed in corresponding Tbc1d1 / mice [85]. The reported association of a mutation in human TBC1D1 (R125W) that was identified in a genome-wide approach using multigenerational Utah pedigrees underpins the significance of the discovery of the TBC1D1 variant [86,87]. Similar to Tbc1d1, another positionally cloned diabetic modifier gene, Bhlhe40 (basic helix-loop-helix family, member e40), was speculated to modify fatty acid oxidation in muscle. Bhlhe40 was discovered within the QTL Dbm1 initially identified in an F2-intercross progeny derived from non-obese diabetic Akita and non-diabetic A/J mice [88]. The generation of congenic lines and the analysis of sub-congenics allowed the responsible diabetogenic region to be narrowed down to a 9 Mb fragment carrying 14 genes, of which Bhlhe40 exhibited a markedly reduced expression in muscle. By in vitro functional analyses Bhlhe40 was shown to negatively control fatty acid oxidation in cultured myocytes [89]. The Ifi202b (interferon inducible gene 202b) gene is another example for the successful outcome of a positional cloning project. In this case an intercross of obese NZO mice with the lean and diabetes resistant C57BL/6 (B6) strain was performed and the subsequent linkage study lead to the mapping of a major obesity QTL (Nob3) on distal mouse Chr. 1 [90]. The positional cloning of the responsible obesity gene, including the generation of panels of recombinant congenic mouse lines to define the critical segment, and the characterization of the annotated genes revealed Ifi202b as the most likely candidate for obesity effects of the QTL Nob3. Due to a microdeletion spanning the first exon and the 5′flanking region of the gene, transcripts of Ifi202b were undetectable in tissues derived from the B6 strain. Furthermore, the Ifi202b deficiency seems to modulate fat accumulation through the expression of adipogenic genes such as 11β-HSD1 (11β-hydroxysteroid dehydrogenase type 1). The fact that two human orthologues of Ifi202b (IFI16 and MNDA) exhibit an altered expression in visceral adipose tissue of obese subjects support the hypothesis that Ifi genes are somehow involved in adiposity and presumably in adipose tissue function [91]. Another gene variant involved in adipogenesis and also discovered by a linkage study is the Ube2l6 (ubiquitin-conjugating enzyme E2L6) gene on Chr. 2 (Lipq1 locus). The loss-of-function allele(s) present in the obesity resistant BALB/c strain operate to decrease adipocyte size and number [92]. Further factors that promote diabetes in a state of obesity have been elucidated from genetic studies on two obese mouse strains differing in diabetes susceptibility; the B6 mice homozygous for the Lepob mutation with mild fasting hyperglycemia and the BTBR strain with severe diabetes carrying the leptin mutation [93]. The obese F2 intercross



derived from these strains mapped several loci for the traits of fasting plasma glucose and insulin levels. Based on these linkage results Sorcs1 (VPS10 domain receptor protein SORCS 1) was discovered within the diabetes locus T2dm2 on mouse Chr. 19 by positional cloning. Sorcs1 encodes a protein that appears to be involved in both insulin secretion and islet survival [94]. Moreover, it was the first genetic association study allowing the translation of a diabetes-susceptibility gene identified in mice to humans. In two Mexican-American family cohorts SORCS1 variants were associated with fasting insulin levels and insulin secretion [95]. Similarly, syntaxin binding protein 5 like (Stxbp5l), also known as Tomosyn-2, has been identified with the same intercross model and seems to be responsible for the T2D susceptibility locus on Chr. 16. Functional studies revealed Tomosyn-2 to be a negative regulator of insulin secretion by binding to proteins involved in the fusion of insulin containing granules with the plasma membrane [95]. An additional example of the use of positional cloning to identify diabetes susceptibility genes utilized an intercross between the diabetessusceptible DBA mouse and the diabetes-resistant B6 strain, which additionally harbored the obesity mutation Lepob. The data collected from the Lepob/Lepob F2 progeny revealed a genetic interval on Chr.1 associated with reduced beta-cell numbers and elevated blood glucose. This region was reduced using molecular genetics and computational approaches to identify a novel gene which was designated Lisch-like (LI). Mice with an induced mutation, reflecting a hypomorphism for this gene, are impaired in both beta-cell development and glucose metabolism [96]. A striking allelic variant of the zinc finger domain transcription factor 69 (Zfp69) underlines the diabetic effect of the QTL Nidd/SJL. This QTL on Chr. 4 was responsible for acceleration and aggravation of diabetes development in a backcross population of NZO with SJL and the effect was markedly enhanced by NZO Chr. 5 (Tbc1d1). Interestingly, the lean SJL strain contributed the diabetogenic allele and carries the “intact” Zfp69 gene in contrast to NZO and B6 in which the gene is disrupted by the insertion of a retroviral transponson (IAPLTR1a). The diabetogenic effect of Zfp69 seems to be mediated via impaired triglyceride storage in white adipose tissue leading to hepatosteatosis and hyperglycemia. Furthermore, the human orthologue of Zfp69 (ZNF642) appears to be involved in the pathogenesis of human diabetes, since its expression was increased in adipose tissue of human T2D patients [97]. In summary, by using mouse models that mimic the human disease, genes like Tbc1d1 and Sorcs1 were discovered and provide new insights into the pathophysiology of obesity and diabetes. Moreover, the successful translation from rodent models to humans can form the basis for the development of new therapeutic strategies.

3. ROLE OF EPIGENETIC ALTERATIONS ON OBESITY AND T2D Considering that the proportion of gene variants, SNPs, and CNVs that associate with changes in body weight and the development of hyperglycemia are modest, it is suggested that epigenetic alterations contribute to the complexity of metabolic disease. In response to environmental stimulation, epigenetic processes at the chromatin template significantly sensitize transcriptional and phenotypic outcomes including metabolic state and nutritional requirements. Epigenetic mechanisms impact gene expression and could predispose individuals to a particular metabolic phenotype. These mechanisms can be active during intrauterine and early postnatal development, as well as throughout adult life [98,99].


Figure 2: Plasticity of epigenetic gene regulation. DNA methylation (red circles) is generally recognized to result in packing of chromatin and gene silencing (upper panel). Transcriptionally active DNA has an open chromatin structure and is associated with unmethylated DNA (white circles) and specific histone modifications (e.g. acetylation, de-acetylation, methylation). The resultant DNA is readily accessible for the transcription machinery (lower panel). Environmental factors (malnutrition, sedentary lifestyle) as well as imprinting affect the methylation status of genes and alter gene expression. Dietary intervention or increasing physical activity might counteract these modifications.

In contrast to gene variants or SNPs, epigenetic mechanisms modulate gene expression without affecting the genomic sequence and have therefore been challenging to depict. In particular, DNA methylation and numerous histone modifications modify the structure of the chromatin, making a gene either more or less accessible for the transcription machinery. Another epigenetic mechanism of transcriptional regulation is conducted by microRNAs (miRNAs) which are excellently introduced and discussed by Rottiers and Näär [100], and therefore no subject of this review. On the basis of GWAS, genome-wide methylation studies have also been performed to identify regions with extreme inter-individual variability in DNA methylation, thus presenting an epigenetic fingerprint of an individual [101]. These variably methylated regions (VMRs) can be divided into either stable or dynamic groups, with the latter potentially being subject to environmental influence. Association of VMRs with certain traits, e.g., blood glucose or BMI, could lead to markers for obesity or T2D. Indeed, analysis of 74 samples of the Age, Gene/Environment Susceptibility (AGES) study cohort led to the identification of 13 VMRs associated to BMI [101]. Among the 13 genes that are in spatial proximity to BMI-associated VMRs in this cohort, PM2OD1, MMP9, PRKG1, RFC5 and the previously mentioned SORCS1 displayed the strongest association. Therefore, both genetic variations as well as variable epigenomic imprinting could affect gene function and risk for disease. Although the mechanisms of epigenetic gene regulation are not fully understood, it is generally accepted that increased methylation of DNA is associated with packing of the chromatin and silencing of a gene. Conversely, demethylation of DNA is associated with an opening of the chromatin and transcriptional activation. It is important to understand that DNA methylation and histone modifications are not independent modulators of chromatin structure but rather go hand in hand. The source of causation, i.e. whether histone modifications result in



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Review alterations of DNA methylation or if DNA methylation is an initial step in histone modification, is unclear [102]. The most studied form of DNA methylation is methylation of a cytosine preceeding a guanine (CpG), however cytosine methylation in a non-CpG context (CpA, CpT, and CpC) might additionally be of importance for regulation of transcription [103]. For example, the reduced expression of PPARGC1A (PGC1α) in the vastus lateralis muscle of diabetic individuals is associated with elevated promoter methylation in both a CpG and non-CpG context [104]. These findings were confirmed in cell culture experiments, where incubation of primary human myotubes with palmitate or TNFα (tumor necrosis factor alpha) resulted in elevated CpG and non-CpG cytosine methylation of the PPARGC1A promoter. These data also indicate that environmental factors, like stress and nutrition, modulate gene expression via epigenetic mechanisms (Figure 2). In rats, 8 weeks of high-fat feeding resulted in decreased expression of Pklr (hepatic L-type pyruvate kinase) and Gck (glucokinase) due to increased methylation of their promoters [105]. Hepatic Scd1 (stearoyl-Coenzyme A desaturase 1) expression and promoter methylation has also been shown to be differentially affected by carbohydrate and high-fat feeding [106]. Besides alterations in promoter methylation, histone modifications are also affected by nutrition. In Apoe (apolipoprotein E)-deficient mice, HFD altered lysine trimethylation of histone 3 in the peroxisome proliferator-activated receptor alpha (PPARα)-network [107]. Together, these data indicate that nutrition directly affects metabolism on the level of epigenetic gene regulation. The question now becomes: are these effects long term or just transient? Generally, it is assumed that epigenetic modulations like DNA methylation are stable enough to be transmitted to following generations. This “heredity of acquired skills” is of particular interest as it would further explain the increased susceptibility of children of obese or diabetic parents to themselves suffer from metabolic diseases

[108,109]. Recurrently, animal studies help to understand how paternal and maternal nutrition affects metabolism in the offspring. In mice, maternal high-fat feeding led to increased body mass and impaired insulin sensitivity in the two subsequent generations, suggesting epigenetic transmission of HFD-induced changes [110]. Furthermore, postnatal livers of offspring born to high-fat fed mothers displayed increased expression of the cell cycle inhibitor Cdkn1a (cyclin-dependent kinase inhibitor 1A), which was also differentially methylated [111]. Other effects have been observed in pancreatic islets. Here, maternal high-fat diet epigenetically silenced the transcription factor Hnf4a (hepatocyte nuclear factor 4 alpha), which is associated with an increased risk to develop T2D [112]. Interleukin-13 (Il13) has been implicated in glucose homeostasis [113]; in pancreatic islets of the female offspring of male high-fat fed rats, promoter methylation of the interleukin-13 receptor (Il13ra2) was decreased with a concomitant increase in expression [108]. The functional consequences of this increased Il13ra2 expression is not clear, still, it serves as an example how paternal nutrition epigenetically affects islet function in the offspring. Alterations in pancreatic DNA methylation might therefore be relevant for the onset of T2D. In humans, methylation of the key regulator PDX1 (pancreatic and duodenal homeobox 1) was increased in islets of T2D patients, associated with a decreased expression of this gene [114]. The GLP-1 receptor (GLP1R) in beta-cells mediates the incretin-effect, leading to increased glucosedependent insulin release. Reduced expression of GLP1R in T2D patients might also be mediated by increased methylation of its promoter [115]. Protein restriction in male mice led to substantial differences in hepatic gene expression of the offspring; pathways regulating lipid biosynthesis and proliferation were specifically upregulated and accompanied by numerous changes in DNA methylation [116]. Most strikingly, an intergenic CpG island in an enhancer locus of Ppara (PPARα) displayed increased methylation, which was associated with decreased expression of this gene in the lowprotein offspring. Contrary to these results, maternal protein restriction in

Figure 3: Impact of genetics and epigenetics on the onset of metabolic diseases. Several genetic variants have been identified that are associated to increased risk of metabolic disease. Due to their molecular manifestation in the DNA sequence they are depicted as static and exhibit a lifelong risk potential. Epigenetic modifications can be inherited as well as acquired due to adverse lifestyle. Epigenetic marks can be modified upon lifestyle changes. Therefore, they are depicted as dynamic and hold the potential to further increase (adverse lifestyle) or dampen (beneficial lifestyle) the individual risk for metabolic disease. Furthermore, genetic variants might influence epigenetic marks. Note that the genes and modifications displayed in this figure serve as examples for the proposed mechanisms.

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rats led to decreased promoter methylation and increased expression of hepatic Ppara [117]. Remarkably, supplementation of the maternal diet with folic acid as a methyl donor prevented hypomethylation and transcriptional upregulation of Ppara in the offspring. Alterations of folic acid availability in the maternal diet has been shown to modulate the expression of DNA methyltransferases (DNMTs) in endometrial tissues [118,119], indicating a mechanism by which maternal nutrition could affect intrauterine imprinting. In Apoe-deficient mice, this maternal folic acid supplementation improved the lipid profile of the offspring subjected to HFD and decreased the expression of pro-inflammatory cytokines [120]. High-fat feeding of dams has also been shown to result in altered dopamine-related gene expression in the CNS of the offspring, with an up-regulated expression of the dopamine reuptake transporter (DAT, Dat gene) being the most prominent target [121]. The authors suggested that these alterations would result in a hypodopaminergic state, linking a desensitized reward circuitry to increased food consumption. Most importantly, increased Dat expression in the offspring was associated with decreased Dat promoter methylation in different regions of the brain, suggesting a causal link. Supplementing the maternal HFD with folic acid prevented hypomethylation and increased Dat expression in the offspring [122], implying that the content of methyl donors in the maternal diet indeed could affect methylation patterns, and thus gene expression, in the offspring. As indicated above, hunger and satiety are tightly regulated in the hypothalamus and linked to the reward circuitry, located in the ventral tegmental area (VTA), nucleus accumbens (NAc), and pre-frontal cortex (PFC) of the midbrain [123–125]. Dopamine (DA), released by VTA neurons, is a key neurotransmitter related to rewarding effects of food consumption [126,127]. In the hypothalamus, genes involved in dopamine synthesis and release have been shown to be upregulated upon high-fat feeding [128]. More recent data suggest an epigenetic contribution to this observation: the genes of hypothalamic Th (tyrosine hydroxylase, rate limiting enzyme in dopamine synthesis) and Dat were hypomethylated and transcriptionally up-regulated in diet-induced obese (DIO) mice [129]. In contrast, the expression of both genes was decreased in the VTA due to increased promoter methylation. Together, these data indicate an epigenetic deregulation of feeding behavior in the central nervous system due to early high-fat feeding. As mentioned above, GWAS on obesity and BMI also led to the identification of SNPs in genes associated to the central regulation of feeding, such as MC4R, LEPR, and POMC [130]. POMC products such as α-MSH increase MC4R signaling, whereas agouti-related protein (AgRP) reduces MC4R signaling thereby playing a major role in the regulation of feeding behavior [33–38,131]. In Wistar rats, rearing in small litters (1–3 pups) as a model for neonatal overfeeding resulted in an early inhibition of POMC expression in the hypothalamus [132]. Neuropeptide Y (NPY), which induces orexigenic signaling, was hypomethylated without ensuing changes to gene expression. In contrast, the POMC promoter showed strong hypomethylation in a region close to an Sp1-binding motif, which was inversely correlated to POMC expression. Because the Sp1-binding motif is necessary for insulin or leptin-induced POMC expression, the authors concluded that methylation in this region would be responsible for the inhibition of POMC expression, despite elevated levels of both hormones in circulation. Differential POMC methylation has also been observed in peripheral blood cells from obese vs. lean children, even prior to the onset of weight gain [133]. These data indicate two important principles: firstly, DNA methylation can indeed serve as a risk marker for future onset of metabolic disease. Secondly, this risk marker can be assessed in easily accessible DNA from blood cells. This is not self-evident, because DNA methylation directly affects gene transcription and is therefore seen to


be regulated in a cell type- or tissue-specific manner. Nonetheless, it is important to determine those metabolic states or diseases that are reflected in the methylation state of blood cell DNA, and those that are not. Blood lymphocyte derived DNA has already been used to study IGF2 (insulin-like growth factor 2) methylation in age and BMI-matched young obese individuals [134]. While IGF2 methylation was not associated to blood glucose or insulin levels, it showed clear association with Cpeptide and triglyceride levels. Furthermore, methylation of FABP3 (fatty acid binding protein 3) in peripheral white blood cells associated with plasma total cholesterol, insulin sensitivity, and blood pressure [135]. Blood markers like these could be used to estimate the risk of an individual to disease or to predict the success of an upcoming intervention. In regard to weight loss interventions, methylation of AQP9, DUSP22, HIPK3, TNNT1, and TNNI3 in blood-derived DNA was associated to responsiveness of a diet intervention and could therefore be used to predict individual success rates [136]. Since it is known that environmental factors like nutrition accelerate the onset of metabolic diseases by altering the epigenome, several studies investigated if an exercise intervention would conversely improve metabolic health by re-tuning the epigenome. This hypothesis was supported by a study showing that acute exercise increases the expression of PPARGC1A and PPARD amongst other genes in skeletal muscle by decreasing their promoter methylation in an exercise intensity-dependent manner [137]. Six months of exercise intervention also altered the epigenome of the adipose tissue [138]. Interestingly, differential DNA methylation after exercise occurred also at known risk genes for obesity (e.g., CPEB4, MAP2K4, PRKD1) and T2D (e.g., DUSP8, KCNQ1, TCF7L2) which were previously identified by GWAS. Together, these data indicate that both genetic variations and epigenetic modifications could independently modulate the expression of key metabolic genes. Nutritional and exercise interventions have been shown to be able to counteract acquired (or imprinted) epigenomic patterns to normalize expression of these genes. However, the durability of these changes, or even heritability, is indistinct and future research will be needed to clarify these important issues. 4. CONCLUSION Obesity and T2D are caused by (poly)genetic and epigenetic changes in combination with reduced levels of physical activity and increased consumption of energy-dense food (Figure 3). Different approaches (GWAS, animal studies) have identified risk genes and numerous epigenetic alterations that participate in these metabolic diseases. However, future studies will be required to understand the complexity as well as the different levels of modifications that finally cause overweight, impaired insulin sensitivity, and hyperglycemia. Important future challenges will also be to (1) establish how dietary components and/or exercise affects the epigenome to trigger the development of the metabolic disease and to (2) identify makers in blood cells (e.g. methylated genes) that predict later onset of the disease and/or subgroups that will successfully respond to an intervention.

ACKNOWLEDGMENTS The author's work is supported by the German Ministry of Education and Research (NGFNplus: 01GS0821; NEUROTARGET: 01GI0847; DZD: 01GI0922) and by the State of Brandenburg. The authors would like to thank Nicole Hallahan for critically reading the manuscript.



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The authors wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

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Genetic and metabolic determinants of human epigenetic variation.

Genetic and epigenetic control of early lymphocyte development.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

Epigenetic inheritance of metabolic state.

Metabolic mechanisms of epigenetic regulation.

Epigenetic control of immunity.

Genome-wide analysis of genetic and epigenetic control of programmed DNA deletion.

Genetic and epigenetic control of the spatial organization of the genome.

Natural and conditional ageing of mouse fibroblasts: genetic vs. epigenetic control.

Assembly of the cricket cercal sensory system: genetic and epigenetic control.

Genetic and epigenetic control of gene expression by CRISPR-Cas systems.

Genetic and epigenetic basis of psoriasis pathogenesis.

Epigenetic and genetic dispositions of ovarian carcinomas.

Genetic and Epigenetic Regulation of CCR5 Transcription.

Genetic and Epigenetic Regulation of Aortic Aneurysms.

[Genetic and epigenetic aspects of celiac disease].

Genetic and epigenetic underpinnings of eosinophilic esophagitis.

Genetic and epigenetic controls of plant regeneration.

Genetic and epigenetic regulation of intestinal fibrosis.

Mitochondrial quality control pathways as determinants of metabolic health.

Virus control goes epigenetic.

Oncogenic epigenetic control.

Editorial: Refining Prevention: Genetic and Epigenetic Contributions.

Genetic and epigenetic contributors to FSHD.