Genetic and epigenetic control of metabolic health

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.

Keywords: 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; DUSP22, dual specificity phosphatase 22; DUSP8, dual specificity phosphatase 8; Epigenetics; 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); GWAS; 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; Obesity; PACS1, phosphofurin acidic cluster sorting protein 1; PAX6, paired box gene 6; PCSK1, proprotein convertase subtilisin/kexin type 1; PGC1α, 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; Positional cloning; 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); Type 2 diabetes.

Graphical abstract

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.

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.

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.