Open Access

Maternal high fat diet during pregnancy and lactation alters hepatic expression of insulin like growth factor-2 and key microRNAs in the adult offspring

  • Junlong Zhang1Email author,
  • Fang Zhang1,
  • Xavier Didelot2,
  • Kimberley D Bruce3,
  • Felino R Cagampang3,
  • Manu Vatish1, 4,
  • Mark Hanson3,
  • Hendrik Lehnert1, 5,
  • Antonio Ceriello6 and
  • Christopher D Byrne3
BMC Genomics200910:478

DOI: 10.1186/1471-2164-10-478

Received: 13 February 2009

Accepted: 16 October 2009

Published: 16 October 2009

Abstract

Background

miRNAs play important roles in the regulation of gene functions. Maternal dietary modifications during pregnancy and gestation have long-term effects on the offspring, but it is not known whether a maternal high fat (HF) diet during pregnancy and lactation alters expression of key miRNAs in the offspring.

Results

We studied the effects of maternal HF diet on the adult offspring by feeding mice with either a HF or a chow diet prior to conception, during pregnancy and lactation, and all offspring were weaned onto the same chow diet until adulthood. Maternal HF fed offspring had markedly increased hepatic mRNA levels of peroxisome proliferator activated receptor-alpha (ppar-alpha) and carnitine palmitoyl transferase-1a (cpt-1a) as well as insulin like growth factor-2 (Igf2). A HF diet induced up-regulation of ppar-alpha and cpt-1a expression in the wild type but not in Igf2 knock out mice. Furthermore, hepatic expression of let-7c was also reduced in maternal HF fed offspring. Among 579 miRNAs measured with microarray, ~23 miRNA levels were reduced by ~1.5-4.9-fold. Reduced expression of miR-709 (a highly expressed miRNA), miR-122, miR-192, miR-194, miR-26a, let-7a, let7b and let-7c, miR-494 and miR-483* (reduced by ~4.9 fold) was validated by qPCR. We found that methyl-CpG binding protein 2 was the common predicted target for miR-709, miR-let7s, miR-122, miR-194 and miR-26a using our own purpose-built computer program.

Conclusion

Maternal HF feeding during pregnancy and lactation induced co-ordinated and long-lasting changes in expression of Igf2, fat metabolic genes and several important miRNAs in the offspring.

Background

miRNAs are small (~21 nt) non-coding RNAs that were originally discovered to regulate development in C. elegans [13]. A significant number of miRNAs are conserved across different species [47]. miRNAs regulate gene functions mainly through degradation of their cognate mRNAs by perfect matches with the mRNA molecules; or via inhibition of protein translation through base pairing of ~7 nucleotides (called "seed sequence") between miRNA and the 3'-untranslated region (3'-UTR) of the target mRNA molecules [8]. Expression of miRNAs may be regulated by transcription factors (e.g. myogenin and myoD regulate expression of a number of miRNAs [9]), and transcription factors per se may also be regulated by miRNAs (e.g. miR-1 promotes myogenesis by targeting histone deacetylase 4, a transcriptional repressor of muscle gene expression) [10]. A single miRNA can repress the production of hundreds of proteins, but this repression is relatively mild [11]. On the other hand one mRNA can be targeted by several miRNAs, which have additive effects in regulation of protein synthesis [12]. For example, SMAD-1 gene has two predicted binding sites for miR-26a [12], and greater suppression effects on protein translation have been observed in mRNAs containing multiple binding sites for a miRNA [13].

miRNAs are involved in the regulation of almost all important biological processes including development [14], differentiation, cell proliferation, cell cycle regulation [15, 16] and energy metabolism [17], including fat metabolism and glucose homeostasis [18, 19]. For example, miR-375 suppresses glucose-induced insulin secretion in pancreatic β-cells [20], thus demonstrating an essential role in plasma glucose homeostasis. Knocking down of endogenous miR-122, a miRNA abundantly expressed in the liver, reduces plasma cholesterol concentrations in mice [21], with parallel up-regulation of 363 mRNA transcripts and down-regulation of 305 mRNA transcripts in the liver [21]. MiR-143 stimulates human adipocyte differentiation [22]. Analysis of global profiles of miRNA expression in skeletal muscle with microarray shows that expression of 4 miRNAs (miR-29a, miR-29b, miR-29c and miR-150) are up-regulated [23], whereas expression of 11 miRNAs (miR-379, miR-127, miR299-5p, miR-434-3p, miR-335, miR130a, miR-19b, miR-451, miR-148a, miR-199a and miR-152) are down-regulated in skeletal muscle of type 2 diabetic rats [23].

The prevalence of obesity is increasing markedly in industrialised countries [2428], and high fat, high protein, low carbohydrate diets including proprietary diets such as the Atkins diet are widely consumed [2931]. The prevalence of obesity in women of reproductive age continues to rise [32], and it is likely that many women of reproductive age also consume a low carbohydrate, high fat and high protein diet during pregnancy and lactation. However the effects of increased maternal dietary consumption of fat during pregnancy and weaning on the long term health of the offspring are not fully characterized.

Many studies have indicated long-term consequences of maternal dietary modifications (e.g. caloric or protein restrictions) during pregnancy and lactation on the development of insulin resistance and risk of cardiovascular disease in the offspring [3337]. We have previously shown in mice that adult offspring of dams fed a low carbohydrate, high fat and high protein diet during pregnancy and lactation but weaned onto a chow diet have reduced hepatic triglyceride levels in association with increased protein levels of key genes regulating fatty acid oxidation including carnitine palmitoyltransferase-1a (CPT-1a) and peroxisome proliferator-activated receptor-alpha (PPARα) predominantly in the female offspring [33]. Pups born to dams on a high fat (HF) diet during gestation and lactation have increased percentage of body fat, plasma glucose, free fatty acids, insulin and cholesterol levels, liver weight and lipid concentrations at weaning or in adulthood [38, 39].

Fetal growth is regulated by insulin-like growth factor 2 (IGF2) [40]. Recent data suggest that IGF2 may regulate fat metabolism. For example, body weight is affected by several polymorphisms in the Igf2 gene [41, 42], and low circulating IGF2 concentrations are associated with weight gain and obesity [43]. In contrast, high circulating IGF2 levels associated with the Apal polymorphism of Igf2 are associated with low body weight in middle aged men [44]. Mice overexpressing Igf2 have increased fatty acid oxidation [45]. Maternal dietary protein restriction reduces hepatic expression of Igf2 in the male fetal offspring. However, whether maternal HF feeding alters offspring Igf2 expression has not been documented.

Following our previous studies on maternal high fat, high protein and low carbohydrate diet[33], we used a modified diet to investigate whether maternal HF feeding during pregnancy and lactation altered mRNA levels of ppar-α and cpt-1a and whether changes in ppar-α and cpt-1a were related to changes in Igf2 expression. We also analyzed global miRNA expression profile in the liver to determine which miRNAs were altered in the offspring born to dams fed a HF diet during pregnancy and lactation.

Results

Maternal HF fed offspring had increased mRNA levels of ppar-α, cpt-1a and Igf2 in the liver

We have previously shown that maternal high fat, high protein and low carbohydrate diet fed offspring had increased protein levels of PPARα and CPT-1a levels in the liver, in association with reduced hepatic lipid levels, despite having no significant changes in body weight, plasma glucose and lipid profile [33]. In this study, a modified HF diet was fed to dams, in which the percentage of fat was increased by more than 2-fold with a small increase in protein levels compared to the chow diet (see methods section). Consistently, no significant difference in body weight, fasting plasma triglyceride, total cholesterol and glucose levels were observed between maternal HF fed offspring weaned on a chow diet (HF/C) and control mice (C/C, data not shown). mRNA levels of ppar-α and cpt-1a in the HF/C mice were increased by ~1.6 and ~3.7-fold respectively compared to C/C mice (p < 0.05 and 0.01 for ppar-α and cpt-1a respectively, Table 1).
Table 1

Effects of maternal HF feeding on hepatic mRNA levels in adult offspring

Genes

C/C (n = 7)

HF/C (n = 7)

ppar-α

61873 ± 7638

97445 ± 11712*

cpt-1a

34193 ± 4420

126777 ± 23720**

Igf2

520 ± 70

1404 ± 266**

Levels of mRNA expression (arbitrary units) were measured with real time qPCR as described in the method section. Mean ± S.E. * p < 0.05 and ** p < 0.01.

As the maternal HF diet was implemented prior to conception and continued throughout pregnancy and lactation, we investigated whether expression of Igf2, an imprinted gene encoding a growth factor expressed during early development [40] was altered in the maternal HF fed offspring. The mRNA level of Igf2 was increased by ~2.7-fold in maternal HF diet fed offspring compared to the control animals (p < 0.01, Table 1).

To determine whether increased expression of ppar-α and cpt-1a was related to increased expression of Igf2 in the maternal HF fed offspring, we measured mRNA levels of ppar-α and cpt-1a in Igf2 KO mice. A HF diet modestly increased hepatic expression of ppar-α and cpt-1a in the WT mice (p < 0.05 and 0.01 for ppar-α and cpt-1a respectively, Table 2), but the HF diet had no effects on ppar-α and cpt-1a expression in the KO mice (p = 0.98 and 1.0 for ppar-α and cpt-1a respectively, Table 2), suggesting that expression of Igf2 was required for the HF diet induced up-regulation of expression of ppar-α and cpt-1a.
Table 2

Hepatic gene expression in wild type and Igf2 knock out mice

Genes

KO-C (n = 5)

WT-C (n = 6)

KO-HF (n = 6)

WT-HF (n = 6)

ppar-α

210.34 ± 33.17

220.32 ± 40.99

283.88 ± 15.97

370.91 ± 31.35*

cpt-1a

266.71 ± 17.41

236.68 ± 50.41

266.05 ± 36.59

391.26 ± 21.61**

Both the wild type (WT) and Igf2 knock out (KO) mice were fed either a HF or chow diet as described in the methods section. mRNA levels were measured using real time PCR. Mean ± S.E. * p < 0.05 and ** p < 0.01 (HF v chow diet).

Hepatic expression of let-7c was reduced in maternal HF offspring

Let-7 was originally discovered due to its regulation of developmental timing in C. elegans, through binding to the 3'-UTR region of Lin-41 [46, 47]. Levels of let-7c and other members of let-7 including let-7a, let-7b and let-7d were reduced by 2-2.5-fold in maternal HF fed offspring compared to the control animals (p < 0.01 for let-7a, let-7b and let-7d, Table 3).
Table 3

Hepatic let-7s levels in maternal HF fed offspring.

Genes

Expression levels (arbitrary units)

 

C/C (n = 7)

HF/C (n = 7)

let-7c

118.10 ± 9.71

60.31 ± 6.80**

let-7a

278.62 ± 15.64

107.64 ± 11.82*

let-7b

107.10 ± 5.45

45.82 ± 4.28**

let-7d

164.68 ± 15.50

80.29 ± 8.10**

Levels (arbitrary units) of let-7s were measured as described in the methods section. Mean ± S.E. ** p < 0.01.

Having observed reduced expression of let-7s in maternal HF fed offspring, we measured the global miRNA expression profile with microarrays.

Expression of ~5.7% of miRNAs was altered in the maternal HF fed offspring

A cut-off threshold of 1.5-fold change [48] in miRNAs was used to determine whether altered miRNAs levels were likely to be significant. Of 579 miRNAs measured, expression of 10 miRNAs (~1.7%) was increased by ~1.5-2-fold (average increase was ~1.64-fold, Table 4), whereas expression of 23 miRNAs (~3.97%) were reduced by 1.51 - 4.93 fold (average reduction of 2.16-fold, Table 5), with miR-483* showing the biggest reduction (by ~4.9-fold, Table 5). In contrast, expression of most miRNAs remained unchanged (Additional file 1: Table S1).
Table 4

Hepatic miRNA levels increased in maternal HF fed offspring

miRNAs

C/C

HF/C

↑ Fold

miR-503*

12765

19148

1.50

miR-379

13463

20313

1.51

miR-770-3p

17118

26761

1.56

miR-369-3p

14441

22857

1.58

miR-197

20128

32193

1.60

miR-21*

12420

19874

1.60

miR-328

18750

30348

1.62

miR-471

12420

20167

1.62

miR-207

21638

38458

1.78

miR-667

17689

36156

2.04

Mean

16083

26628

1.61

Levels of miRNAs were measured with microarrays as described in the method section. Relative values of signals for each miRNAs are presented in the two columns.

Table 5

Hepatic miRNAs levels reduced in maternal HF fed offspring

miRNAs

C/C

HF/C

↓ Fold

miR-410

18072

11997

1.51

miR-804

19831

13138

1.51

miR-323-5p

17402

11500

1.51

let-7c

108628

71343

1.52

miR-302a*

16365

10647

1.54

miR-711

25823

16057

1.61

miR-26a

95209

58142

1.64

miR-122

324798

192912

1.68

miR-216b

16179

9463

1.71

miR-294*

17402

10168

1.71

miR-185

27121

15378

1.76

miR-192

80361

44865

1.79

miR-29a

30002

16331

1.84

miR-194

92178

49817

1.85

miR-145

28108

14841

1.89

miR-126-3p

41868

20020

2.09

miR-762

76284

32872

2.32

miR-16

109631

43004

2.55

miR-1224

69200

24241

2.85

miR-22

130448

44304

2.94

miR-30c-2*

67031

20460

3.28

miR-494

298733

82842

3.61

miR-483*

366458

74387

4.93

Mean

90310

38640

2.16

Levels of miRNAs were measured with microarrays as described in the method section. Relative values of signals for each miRNAs were presented in the two columns.

Among those miRNAs showing reduced expression, average levels of expression were 90310 arbitrary units (Table 5), whereas in those showing increased expression, the average levels of the 10 miRNAs were 16083 arbitrary units (Table 4), which was 5.6-fold lower than those miRNAs showing reduced expression.

We validated microarray data with the stem-loop RT-PCR method [49] using purchased miRNA primers (ABI). 5 miRNAs (let-7c, miR-483*, miR-22, miR-29a and miR-30c) were measured as these miRNAs showed different magnitude of reduced expression in the HF offspring (Table 6). Expression of miR-483*, let-7c and miR-29a measured with qPCR were consistent with values obtained from microarray data, with minor differences in the magnitude of changes in expression (Table 6). However, a discrepancy in levels of miR-30c and miR-22 between qPCR and microarray was observed (Table 6). Levels of miR-30c between maternal HF and chow fed offspring were similar when measured with microarrays, but significantly different when measured with qPCR. A ~2.9-fold reduction was obtained with microarray analyses whereas a ~42% increase occurred in levels of miR-22 in maternal HF offspring measured with qPCR (Table 6). We also noted that levels of miR-483* were very low when measured with qPCR, consistent with poorly expressed Igf2 mRNA levels. However, data from microarray suggested that miR-483* was abundantly expressed, which was not consistent with qPCR data (Table 6).
Table 6

Validation of microarray data with stem-loop real-time qPCR

Genes

qPCR (arbitrary units)

Microarray

↓ Fold (HF/C v C/C)

 

C/C (n = 7)

HF/C (n = 7)

C/C

HF/C

qPCR

Microarray

Stem-loop real time PCR

miR-30c

1572 ± 670

1168 ± 230*

19436

18861

1.35

1.03

miR-22

309 ± 163

440 ± 179*

130448

44304

0.70

2.94

miR-29a

370 ± 117

276 ± 109*

30002

16331

1.34

1.84

Let-7c

330 ± 31

227 ± 19*

108628

71343

1.45

1.52

miR-483*

1.9 ± 0.2

0.3 ± 0.1***

366458

74387

6.89

4.93

Poly dT adaptor qPCR method

miR-709

1304 ± 118

768 ± 129**

1387121

1258883

1.70

1.10

miR-122

948 ± 61

473 ± 40***

324798

192912

2.00

1.68

miR-192

321 ± 24

121 ± 10***

80361

44865

2.64

1.79

miR-194

124 ± 11

81 ± 7**

92178

49817

1.53

1.85

miR-26a

157 ± 17

58 ± 10***

95209

58142

2.72

1.64

Let-7c

118 ± 10

60 ± 7***

108628

71343

1.96

1.52

let-7a

279 ± 16

108 ± 12***

60650

43004

2.59

1.41

let-7b

107 ± 5

46 ± 4***

83730

63907

2.34

1.31

let-7d

165 ± 16

80 ± 8***

91702

72642

2.05

1.26

miR-494

12 ± 2

5.5 ± 0.8***

298733

82842

2.18

3.61

miR-483

1.3 ± 0.2

1.0 ± 0.2

17689

22100

0.78

0.80

Levels of miRNAs were measured either with purchased primers for specific miRNAs (stem-loop real time PCR) or poly dT adaptor as described in the method section. Mean ± S.E. * p < 0.05 and *** p < 0.001 (HF/C v C/C). ** p < 0.01, and ***p < 0.001 (HF/C v C/C).

We examined the miR-483* genomic DNA location because miR-483* showed the greatest reduction in expression in maternal HF fed offspring, and found that miR-483* was encoded in an intron of Igf2. As intronic miRNAs may share common promoters as their host genes, many intronic miRNAs show significantly correlated expression profiles with their host genes[50, 51]. Thus, we would expect that the levels of intronic miRNA (e.g. miR-483*) are increased with the host gene (Igf2) in the HF/C mice. To our surprise, expression of miR-483* in the HF/C was reduced (shown by qPCR and microarray, Table 4a) in association with increased Igf2 levels. Mir-483 is also processed from the same mmu-mir-483 gene and share most of the complementary sequence of miR-483* [52]. We then examined expression levels of miR-483 from microarray, and found that expression of miR-483 was not markedly different in HF/C mice compared to the controls (Additional file 1: Table S1 and Table 6). We could not validate levels of miR-483 with the stem-loop qPCR because the primers for miR-483 were not available at the time of the study (ABI). Therefore, we measured expression of miR-483 with another qPCR method involving reverse-transcribed poly(T) adaptor during the RT step [53], and found that the low levels of miR-483 expression were consistent with microarray data, similar to poorly expressed miR-483* obtained with the stem-loop method (Table 6). We repeated measurements of let-7c using the poly (T) adaptor method and the results were consistent with those obtained from microarray or the stem-loop qPCR (Table 6). We therefore carried out further validation of miRNAs with the poly (T) adaptor method as this methodology provided flexibility in primer design.

We tried to validate miRNAs showing increased expression with the poly (T) adaptor method. However, among those showing increased expression measured with microarray, miR-667, miR-207, miR-197, miR-770-3p and miR-369-3p were very poorly expressed (data not shown). miR-328 was expressed at much higher levels, but a reduced rather than increased expression in maternal HF diet fed offspring was observed (data not shown). We then focused our study on those miRNAs showing reduced expression in maternal HF fed offspring but excluded those poorly expressed miRNAs (Table 5) plus miR-709, as microarray data suggested that miR-709 had the greatest level of expression in the liver (Additional file 1: Table S1 and Table 6). Data from qPCR confirmed the highly expressed miR-709, but also showed marked reduction in expression in maternal HF fed offspring (p < 0.01, Table 6), which was not consistent with microarray results. However, the levels of most miRNA expression measured with qPCR were consistent with data obtained from microarrays except minor differences in the magnitude of changes (Table 6).

Bioinformatic analysis of predicted targets for miRNAs

As miR-709 was the highest expressed miRNA in the liver, it might be an important miRNA for the regulation of hepatic gene expression. We analysed the predicted targets with widely used algorithms. 1241 hits were found using the miRanda algorithm [54], whereas 353 targets were found using the TargetScan algorithm [12]. At the time of writing, miR-709 was not in the data base of PicTar [55]. We compared the outcome from miRanda and TargetSan algorithms using our own purpose-built computer program and found that 28 common targets (Additional file 2: Table S2) were predicted by both algorithms.

A feature of miRNA function is that several miRNAs tend to act together to generate greater effects than single miRNA [13]. We therefore undertook bioinformatics analysis to investigate whether it was possible to identify common targets for those validated miRNAs that showed reduced expression in the maternal HF fed offspring using our own purpose-built computer program. We found that ZSWIM3 (zinc finger, SWIM domain containing 3), a protein whose function was yet to be characterised [56], was targeted by 5 miRNAs namely miR-122, miR-192, miR-194, miR-709 and miR-483*. 14 genes were targeted by 3 different miRNAs ' [see Additional file 22: Table S3]' and 10 genes (including citrate synthase and Igf-1 receptor) were targeted by miR-122 and miR-494 ' [see Additional file 2: Table S4]'. These results suggested that functions of specific genes might be co-ordinately regulated by a small number of miRNAs.

Discussion

Maternal HF fed offspring mice have: 1) increased hepatic expression of key genes including those regulating fetal growth (such as Igf2) and fat metabolism (such as ppar-α and cpt-1a); 2) altered expression of a small percentage (~5.7%) of important miRNAs. Among the miRNAs showing reduced expression, let-7c regulates developmental timing [46, 47] and miR-122 regulates fat oxidation [21, 57]. Thus, these data suggest co-ordinated changes in key metabolic genes and miRNAs that regulate early fetal growth and fat metabolism in offspring of dams fed HF diet.

As both offspring from HF- and chow-fed dams are weaned onto the same chow diet and maintained on chow until adulthood, changes in expression of key metabolic genes and miRNAs in adult offspring are likely to occur prior to weaning. IGF2 is a growth factor highly expressed during early development [58]. Offspring Igf2 gene expression can be altered by maternal dietary modifications during early development. For example, a maternal low protein diet restricted only to the preimplantation period reduces hepatic Igf2 mRNA in fetal rats [59] and maternal dietary calorie restriction increases Igf2 mRNA levels in the liver and skeletal muscle in fetal sheep [60]. Here we further show that hepatic mRNA levels of Igf2 are elevated in the adult mouse offspring born to dams fed a HF diet, suggesting that maternal HF feeding increases offspring hepatic Igf2 expression prior to weaning. This is supported by our observation that hepatic Igf2 levels in fetal offspring from dams fed a HF diet are increased (unpublished data). Similarly, it is likely that altered expression of hepatic ppar-α and cpt-1a and miRNAs in maternal HF fed adult offspring might have also occurred prior to weaning.

Growth

IGF2 is an early growth factor expressed at the two-cell stage in the mouse embryo[61], and mice deficient in IGF2 have reduced birth weight [58]. In contrast, excess IGF2 in transgenic mice promotes fetal overgrowth resulting in increased birth weight [62]. Our data showing that HF/C offspring have increased Igf2 expression and a trend toward increased liver weight [~16% increase, compared with the control mice (p = 0.44)], suggest that liver growth prior to weaning may be increased in maternal HF fed offspring (Fig 1). This finding is consistent with an early observation in rats showing that maternal HF feeding during gestation increases offspring liver weight, in association with increased body weight and percentage body fat at weaning[38]. In our study, the body weight of maternal HF fed offspring at weaning is ~19.6% (p < 0.05) greater than control mice (data not shown), consistent with increased hepatic Igf2 expression. However, we observed no significant difference in body weights between maternal HF and chow fed adult offspring, when both sets of offspring were fed the same chow diet from weaning, which could be due to that circulating IGF2 is markedly decreased after birth in rodents [63, 64], suggesting that IGF2 is unlikely to play a major role in post weaning growth.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-478/MediaObjects/12864_2009_Article_2362_Fig1_HTML.jpg
Figure 1

Maternal high fat diet during gestation and lactation alters hepatic expression of key genes and miRNAs in the offspring. A maternal HF diet during gestation and lactation increased hepatic Igf2 expression in the offspring, which may be required for the up-regulation of ppar-α/cpt-1a by HF diet as suggested by our data presented in the Table 2. Increased ppar-α suppresses expression of let-7c, facilitates hepatic growth. Igf2 could down regulate let-7c through increased expression of ppar-α. Increased expression of ppar-α and reduced expression of miR-122 may increase hepatic fatty acid oxidation in the offspring. Igf1 receptor (Igf1R) and citrate synthase (CS) are predicted targets shared by both miR-122 and miR-494. Inhibition of Igf1R has been confirmed very recently [86]. Similar to miR-122, maternal HF offspring have reduced miR-494 levels, which favour increased Igf1R and CS activities. Several key proteins involved in epigenetics are predicted targets for miRNAs, in particular, methyl-CpG binding protein 2 are predicted targets for 5 miRNAs (miR-709, let-7s, miR-122, miR-194 and miR-26a) showing reduced levels in maternal HF fed offspring. Histone 4 H4 are predicted targets for 5 miRNAs (miR-503*, miR-770-3p, miR-369-3p, miR-197 and miR-667) showing increased levels in maternal HF fed offspring. Arrows suggest stimulatory and blocked arrows inhibitory effects. Solid lines represent established relationships whereas broken lines represent relationships not yet confirmed experimentally. FFA: free fatty acids. CS: citrate synthase, ppar-a: peroxisome proliferator activated receptor-alpha, cpt: carnitine pamitoyltransferase, MBD: methyl-CpG binding domain protein, MECP2:Methyl-CpG-binding protein 2, CHD4:chromodomain helicase DNA binding protein 4, DOT1L: DOT1-like, histone H3 methyltransferase, HIC2: hypermethylated in cancer 2, Hist4H4, histone 4 H4.

PPARα promotes hepatic proliferation through inhibition of let-7c [65, 66]. Let-7c plays a critical role in the regulation of growth[67]. Overexpression of let-7c decreases c-myc and miR-17, suppressing the growth of hepatocytes [66]. Consistently, we have observed that mRNA levels of ppar-α (and protein [33]) are elevated whereas levels of let-7c reduced in maternal HF fed offspring (Fig. 1), suggesting a co-ordinated regulation of mRNA and miRNA expression in favour of promoting hepatic growth.

It is uncertain whether let-7c is regulated by IGF2. Our data showing increased Igf2 expression in association with reduced let-7c expression, is consistent with the negative regulation of let-7c by PPAR-α as discussed above. In Igf2 KO mice, expression of let-7c levels are increased by ~31% (p = 0.004). These data suggest a negative correlation between Igf2 and let-7c expression (Fig. 1). Further studies are required to determine whether let-7c expression is directly regulated by IGF2. Taking together, our data suggest that maternal HF feeding has induced co-ordinated changes in expression of early growth factor (e.g. Igf2), transcription factor (e.g. ppar-α) and miRNA (e.g. let-7c) to promote hepatic growth.

Fat metabolism

PPAR-α is a master transcription factor regulating hepatic fatty acid oxidation [6873]. We have shown previously that maternal high fat high protein and low carbohydrate diet-fed offspring have increased protein levels of PPAR-α and CPT-1a in association with reduced hepatic lipid levels [33]. Here we further show that mRNA levels of ppar-α and cpt-1a are increased in the maternal HF fed offspring, suggesting that maternal HF feeding increases expression of ppar-α and cpt-1a mRNA and protein levels.

IGF2 may also regulate fat metabolism as low circulating IGF2 concentrations are associated with weight gain and obesity [43], whereas high circulating IGF2 levels are associated with low body weight in middle aged men [44]. Mice overexpressing Igf2 have increased fatty acid oxidation [45]. Our data show that maternal HF offspring have increased Igf2 expression with parallel increased ppar-α, whereas a HF induced increase in ppar-α expression is suppressed in the Igf2 KO mice. These data suggest that Igf2 might regulate fat metabolism through regulation of ppar-α expression, and that up-regulation of ppar-α in maternal HF diet fed offspring is mediated, at least in part through increased expression of Igf2.

miR-122 is abundantly expressed in the liver and regulates fat metabolism [21], as knocking down miR-122 increases hepatic fatty-acid oxidation [21, 57]. Hepatic expression of miR-122 is reduced in the maternal HF adult offspring (Fig. 1), which is consistent with increased expression of PPARα and CPT-1a, two key molecules regulating hepatic fatty acid oxidation (Fig. 1). Maternal HF fed adult offspring have reduced hepatic lipid levels when weaned onto a chow diet and maintained on the chow diet until adulthood[33]. It is likely that increased capacity of fat oxidation (due to reduced miR-122 and increased PPARα and CPT-1a) prior to weaning are maintained until adulthood. This continuing increased fatty acid oxidation capacity leads to reduced hepatic lipid levels when HF offspring mice are weaned onto a chow diet. Taking together, our data suggest that maternal HF feeding increases expression of key genes regulating hepatic fatty acid oxidation and miRNA in the offspring.

The mechanisms by which early changes are maintained until adulthood require further studies. However, it is likely that epigenetic mechanisms may play an important role. Igf2 expression is regulated by DNA methylation[74], and increased Igf2 expression is associated with changes in DNA methylation [75]. Gestational choline deficiency causes global and Igf2 gene DNA hypermethylation through up-regulation of Dnmt1 expression in fetal offspring [75]. Thus, it is likely that increased Igf2 expression in maternal HF adult offspring is associated with altered DNA methylation in the offspring. Similar mechanisms may exist in ppar-α, because hepatic expression of ppar-α is regulated by DNA methylation [76]. A maternal low protein diet during pregnancy and lactation reduces DNA methylation in the promoter region of ppar-α [77], in association with increased ppar-α mRNA levels [77]. Expression of miRNAs can also be regulated by epigenetic mechanisms. Let-7a-3 belongs to the let-7 miRNA gene family and is heavily methylated by the DNA methyltransferases Dnmt1 and Dnmt3b [78]. Let-7a-3 hypomethylation facilitates epigenetic reactivation of the gene and elevates expression of let-7a-3 in human lung cancer cells [78]. Thus, altered DNA methylation could be involved in early changes maintained until adulthood.

Interestingly, several important proteins involved in epigenetics are predicted targets for those miRNAs showing altered expression in the HF offspring. For example, miR-709 is the most abundantly expressed miRNA in the liver detected with microarray (greater than miR-122). According to the TargetScan algorithm [12], miR-709 targets include methyl-CpG binding domain protein 6 and methyl CpG binding protein2 (MECP2, Fig. 1). Among predicted targets of let-7c are proteins including hypermethylated in cancer 2 (HIC2), chromodomain helicase 4, DOT1-like histone H3 methyltransferase (Fig. 1).

A feature of miRNA function is that several miRNAs tend to act together, and a relatively small set of miRNAs account for most of the differences in miRNA profiles between cell lineages and tissues [52]. For example, it has been shown that expression of 4 miRNAs (miR-29a, miR-29b, miR-29c and miR-150) is up-regulated [23], whereas expression of 11 miRNAs (miR-379, miR-127, miR299-5p, miR-434-3p, miR-335, miR130a, miR-19b, miR-451, miR-148a, miR-199a and miR-152) is down-regulated in skeletal muscle of type 2 diabetic rats [23]. Here we show that levels of only ~5.7% of miRNAs are altered in the maternal HF fed mouse offspring, whereas levels of the remaining miRNAs are unchanged. These data suggest that 1) these 23 miRNAs are likely to be expressed during early development and play active roles in the regulation of metabolism and fetal growth; and 2) if these miRNAs have common targeted transcripts, they are likely to have greater effects than a single miRNA in suppressing protein synthesis [13].

However, it remains a challenge to identify common targets shared by several miRNAs, because several hundreds or even over 1000 predicted targets may arise from one single miRNA using current algorithms. Experimentally, it is impractical to knock down each of the miRNAs. In this study, we have written a computer program that allows us to analyse quickly common targets shared by several miRNAs. For example, we have undertaken analysis of common targets among 11 miRNAs and found that the maximum number of shared targets is 5 miRNAs and no common targets are found among 6 different miRNAs. MECP2 is a common predicted target for 5 miRNAs including two abundantly expressed miRNAs (miR-709 and miR-122, Fig. 1). MeCP2 is required to maintain CpG status of genomic DNA[79, 80]. Maternal nutrient restriction decreases MeCP2 levels in the brain in offspring rats[81]. Among those miRNAs showing increased expression in the HF fed offspring, histone 4 H4 is a common target for 5 different miRNAs (miR-503*, miR-770-3p, miR-369-3p, miR-197 and miR-667, Fig. 1).

Finally, despite the observation that offspring born to dams fed a HF diet during pregnancy and lactation and fed a chow diet from weaning have no significant changes in phenotype compared to the control animals, marked changes in expression of important genes such as Igf2, ppar-α and cpt-1a and a class of miRNAs have occurred. Such altered expression of metabolic genes and miRNAs are likely to affect the homeostatic responses of such offspring to dietary challenges in later life.

Conclusion

A maternal HF diet prior to conception, during pregnancy and lactation induces coordinated and long-lasting changes in expression of Igf2 and key fat metabolic genes and miRNAs in the offspring, which may have long-term effects on their health.

Methods

Animal

All procedures in this study were carried out in accordance with the UK Animal Scientific Procedures Act of 1986 and approved by a local ethics committee. Female C57 BL6J black mice were maintained under controlled conditions (room temperature at 22 ± 2°C; 12 hr light/dark cycle) and randomly assigned to either a HF (22.6% fat, 23% protein and 48.6% carbohydrate, W/W) or standard chow diet (10% fat, 18% protein and 68.8% carbohydrate, W/W [82, 83], RM1 - special diet services) diet. They were provided with water ad-libitum. Dams were fed either the HF or chow diet 4 weeks prior to conception, during pregnancy (day 1 of pregnancy indicated by presence of copulation plug) and lactation. Litter size were standardised to 6 pups. All offspring were weaned at 3 weeks of age and fed the same chow diet for 12 weeks. At 15 weeks of age, female mice (n = 7 per group from different litters) were sacrificed and liver samples were quickly removed, snap frozen in liquid nitrogen, and stored at -80°C for further analysis.

Igf2 KO mice

Female B6CBF1 mice were bred with male Igf2-knock (+/-) out mice [40]. Offspring of either WT or Igf2 KO mice (females only) were determined with genotyping using PCR as previously described [84]. Female animals (KO and WT) were housed individually under controlled conditions. At two months of age, female mice (both WT and KO, from different dams) were age-matched, divided into two groups, and were fed ad-libitum either a HF or chow diets for 6 months. Mice were sacrificed at 8 months of age, liver samples were quickly removed, snap frozen in liquid nitrogen, and stored at -80°C for further analysis.

Preparation of total RNAs

For preparation of miRNA containing total RNAs, ~100 mg of liver tissue was homogenised in lyses buffer provided with the mirVana™ miRNA Isolation Kit (P/N: 1560, Ambion, Austin, TX) and total RNA were prepared according to the manufacturer's protocol. Purified total RNA was eluted in 100 μl of elution buffer. Concentrations of total RNAs were measured using a Nanodrop (ND-1000, NanoDrop products, Bancroft Building, Wilmington, USA). The RNA integrity was analysed using an Agilent Bioanalyser 2100 (Agilent Technologies UK Limited, Cheshire, UK) with the RNA integrity number > 8.0, and the ratio of OD260/280 = ~2.0.

MiRNA microarray

This work was carried out at Febit Biomed Gmbh (Febit biomed gmbh, Heidelberg, Germany). Each sample from the control (n = 7) or HF (n = 7) offspring were pooled for microarray analysis (two-class experiment [48]). Each array contains the reverse complements of all major mature miRNAs and the mature* sequences published in the Sanger miRBase release (version 10.1, December 2007, see http://microrna.sanger.ac.uk/sequences/index.shtml) for mice. Each miRNA contains 10 replicates to increase the statistical confidence. For each array 2 μg of total RNA were labelled according to the manufacturer's instructions (miRVana labelling kit from Ambion). After labelling, samples were dried in a speed-vac and re-suspended in febit's proprietary miRNA Hybridization Buffer (18 μl per array). Samples were loaded onto a chip, and overnight (16 hours) hybridization was undertaken at 42°C, using argon pressure to move the samples within the arrays. After the hybridization, the array was washed with the 'febit miRNA standard (external incubation)' hybridization profile and a standard detection using the appropriate filter set. Data was normalised using the software "R" with the "VSN" package and are presented in Additional file 1: Table S1.

Measurement of mRNA expression using real time PCR

During cDNA synthesis, ~200-500 ng of total RNA was used in a 20 μl cDNA synthesis reaction. Total RNA was denatured at 70°C for 5 min and chilled in ice. Then the reaction was added with random hexamers (2.5 ng/μl). The reactions were undertaken at 42°C for 60 min and the reaction was stopped by denaturing at 95°C for 5 min.

For PCR reactions, allsamples from 4 groups of animals were measured in one single 96-well plate, with each reaction undertaken in triplicates. Equal volume of cDNA (0.5 μl/reaction) was added to Sensimix Lowref SYBR green qPCR reagent (Quantace Ltd, London, UK) with gene specific primers (1.0 μM) ' [see Additional file 2: Table S5]'. PCR reactions for all samples including no temperate controls were run on a 7500 Fast Realtime PCR System (Applied Biosystems, Warrington UK, which was also used for all miRNA analysis described below). The reaction conditions were 95°C for 15 min (hotstart) at 95°C for 15 sec, 60°C for 30 sec and 72°C for 30 sec. Results were analyzed using 7500 System SDS software (v1.4). Expression levels were calculated by normalisation to a standard curve using the total amount of RNA as a denominator and expressed as arbitrary units.

Measurement of miRNAs using real time RT-PCR

Two qPCR methods were used in the validation of microarray: the stem-loop RT-PCR method [49], using miRNA specific primers purchased from Applied Biosystems and polyadenylated and reverse-transcribed with a poly(T) adapter into cDNAs for real-time PCR using sequence complementary to the poly(T) adapters during RT reactions [53].

The stem-loop RT PCR (Taqman based technology) was performed according to the manufacturer's protocol (Applied Biosystems, Foster City, CA, USA). ~100 ng total RNA was added to each reverse transcription reaction (RT) for each miRNA. Three replicates were done for each miRNA from RT to PCR and the results were averaged.

For poly(T) adaptor RT-PCR, ~100 ng total RNA was added to a reaction containing 2.5 units E. Coli Poly A polymerase (New England Biolabs Ltd. Herts. UK), 0.75 mM rATP and 1 × Pol A polymerase buffer containing 250 mM NaCl, 50 mM Tris-HCl, 10 mM MgCl2. The reaction (10 μl) was incubated at 37°C for 30 min for extension of the poly A tail. The reaction was heated to 60°C for 5 min, cooling to 4°C and added with an Oligo dT adaptor (0.5 μl of 5 μM) with RT buffer, dNTP mix and MMLV and H2O to a total 20 μl reaction volume (Applied Biosystems, according to the manufacturers' conditions) and incubated at 42°C for 60 min for cDNA synthesis. The cDNA synthesis reaction was stopped by heating at 95°C for 5 min.

Real time PCR was undertaken with Sensimix Lowref SYBR green qPCR reagent (Quantace Ltd, London, UK) in triplicates. 0.5 μl of cDNA was added to PCR reaction containing 1 × PCR reagent mix and universal primer (0.25 μM), miRNA specific primer (0.5 μM, designed based on miRNA sequences released (Release 12.0 Sept 2008) by the Sanger Institute [85]. The reaction conditions were 95°C for 15 min (hotstart), and 95°C for 15 sec, 60-62°C for 60 sec (optimised according to each specific miRNA primers) and a total of 40 cycles.

Computer analysis of target predictions for miRNAs

Prediction of targets for a single miRNA was undertaken using three algorithsms: TargetScan [12], miRanda [54] and PicTar [55]. To identify groups of miRNAs having common predicted targets, we have written a computer program which can be used in conjunction with any of the three algorithms. As the list of targets generated by TargetScan or miRanda are more comprehensive than those from PicTar, we based our analysis of common targets on TargetScan and miRanda.

Statistical analysis

Data from real time PCR were presented as mean ± SE. Skewed data were transformed before statistical analysis. A student t-test was used to compare results between two groups, and a p value < 0.05 was considered to be significant.

Declarations

Acknowledgements

The maternal high fat animal model for this project was funded by a BBSRC grant awarded to CDB, the publication cost was partly funded by a BBSRC grant awarded to FC, Igf2 KO mice were provided by University of Warwick animal unit. Molecular analysis was mainly funded by Research Develop Fund of Warwick University awarded to JZ. MAH is supported by British Heart Foundation.

Authors’ Affiliations

(1)
Clinical Science Research Institute, Warwick Medical School, Clinical Sciences Building, University Hospital - Walsgrave Campus
(2)
Department of Statistics, University of Warwick
(3)
Institute of Developmental Sciences, Developmental Origins of Health and Disease Division, University of Southampton Medical School, Southampton General Hospital
(4)
Albert Einstein College of Medicine
(5)
1st Medical Department, University of Lübeck Medical School
(6)
Chair of Endocrinology, University of Udine

References

  1. Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75 (5): 843-854. 10.1016/0092-8674(93)90529-Y.View ArticlePubMedGoogle Scholar
  2. Wightman B, Ha I, Ruvkun G: Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell. 1993, 75 (5): 855-862. 10.1016/0092-8674(93)90530-4.View ArticlePubMedGoogle Scholar
  3. Olsen PH, Ambros V: The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation. Dev Biol. 1999, 216 (2): 671-680. 10.1006/dbio.1999.9523.View ArticlePubMedGoogle Scholar
  4. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-858. 10.1126/science.1064921.View ArticlePubMedGoogle Scholar
  5. Lagos-Quintana M, Rauhut R, Meyer J, Borkhardt A, Tuschl T: New microRNAs from mouse and human. RNA. 2003, 9 (2): 175-179. 10.1261/rna.2146903.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP: The microRNAs of Caenorhabditis elegans. Genes Dev. 2003, 17 (8): 991-1008. 10.1101/gad.1074403.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Pasquinelli AE, Reinhart BJ, Slack F, Martindale MQ, Kuroda MI, Maller B, Hayward DC, Ball EE, Degnan B, Muller P, et al: Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature. 2000, 408 (6808): 86-89. 10.1038/35040556.View ArticlePubMedGoogle Scholar
  8. Zamore PD, Haley B: Ribo-gnome: the big world of small RNAs. Science. 2005, 309 (5740): 1519-1524. 10.1126/science.1111444.View ArticlePubMedGoogle Scholar
  9. Rao PK, Kumar RM, Farkhondeh M, Baskerville S, Lodish HF: Myogenic factors that regulate expression of muscle-specific microRNAs. Proc Natl Acad Sci USA. 2006, 103 (23): 8721-8726. 10.1073/pnas.0602831103.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, Hammond SM, Conlon FL, Wang DZ: The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet. 2006, 38 (2): 228-233. 10.1038/ng1725.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N: Widespread changes in protein synthesis induced by microRNAs. Nature. 2008, 455 (7209): 58-63. 10.1038/nature07228.View ArticlePubMedGoogle Scholar
  12. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115 (7): 787-798. 10.1016/S0092-8674(03)01018-3.View ArticlePubMedGoogle Scholar
  13. Doench JG, Petersen CP, Sharp PA: siRNAs can function as miRNAs. Genes Dev. 2003, 17 (4): 438-442. 10.1101/gad.1064703.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Wienholds E, Plasterk RHA: MicroRNA function in animal development. FEBS Letters. 2005, 579 (26): 5911-5922. 10.1016/j.febslet.2005.07.070.View ArticlePubMedGoogle Scholar
  15. Liu W, Mao SY, Zhu WY: Impact of tiny miRNAs on cancers. World J Gastroenterol. 2007, 13 (4): 497-502.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Oakley EJ, Van Zant G: Unraveling the complex regulation of stem cells: implications for aging and cancer. Leukemia. 2007, 21 (4): 612-621.PubMedGoogle Scholar
  17. Boehm M, Slack FJ: MicroRNA control of lifespan and metabolism. Cell Cycle. 2006, 5 (8): 837-840.View ArticlePubMedGoogle Scholar
  18. Gauthier BR, Wollheim CB: MicroRNAs: 'ribo-regulators' of glucose homeostasis. Nat Med. 2006, 12 (1): 36-38. 10.1038/nm0106-36.View ArticlePubMedGoogle Scholar
  19. Krutzfeldt J, Stoffel M: MicroRNAs: A new class of regulatory genes affecting metabolism. Cell Metab. 2006, 4 (1): 9-12. 10.1016/j.cmet.2006.05.009.View ArticlePubMedGoogle Scholar
  20. Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, et al: A pancreatic islet-specific microRNA regulates insulin secretion. Nature. 2004, 432 (7014): 226-230. 10.1038/nature03076.View ArticlePubMedGoogle Scholar
  21. Krutzfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M: Silencing of microRNAs in vivo with 'antagomirs'. Nature. 2005, 438 (7068): 685-689. 10.1038/nature04303.View ArticlePubMedGoogle Scholar
  22. Esau C, Kang X, Peralta E, Hanson E, Marcusson EG, Ravichandran LV, Sun Y, Koo S, Perera RJ, Jain R, et al: MicroRNA-143 Regulates Adipocyte Differentiation. J Biol Chem. 2004, 279 (50): 52361-52365. 10.1074/jbc.C400438200.View ArticlePubMedGoogle Scholar
  23. He A, Zhu L, Gupta N, Chang Y, Fang F: Over-expression of miR-29, highly upregulated in diabetic rats, leads to insulin resistance in 3T3-L1 adipocytes. Mol Endocrinol. 2007, 21 (11): 2785-94. 10.1210/me.2007-0167.View ArticlePubMedGoogle Scholar
  24. Mokdad AH, Ford ES, Bowman BA, Nelson DE, Engelgau MM, Vinicor F, Marks JS: Diabetes trends in the U.S.: 1990-1998. Diabetes Care. 2000, 23 (9): 1278-1283. 10.2337/diacare.23.9.1278.View ArticlePubMedGoogle Scholar
  25. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL: Overweight and obesity in the United States: prevalence and trends, 1960-1994. Int J Obes Relat Metab Disord. 1998, 22 (1): 39-47. 10.1038/sj.ijo.0800541.View ArticlePubMedGoogle Scholar
  26. Wild S, Roglic G, Green A, Sicree R, King H: Global Prevalence of Diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care. 2004, 27 (5): 1047-1053. 10.2337/diacare.27.5.1047.View ArticlePubMedGoogle Scholar
  27. Kuskowska-Wolk A, Bergström R: Trends in body mass index and prevalence of obesity in Swedish men 1980-89. J Epidemiol Community Health. 1993, 47 (2): 103-108. 10.1136/jech.47.2.103.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Seidell JC: Time trends in obesity: an epidemiological perspective. Horm Metab Res. 1997, 29 (4): 155-158. 10.1055/s-2007-979011.View ArticlePubMedGoogle Scholar
  29. Foster GD, Wyatt HR, Hill JO, McGuckin BG, Brill C, Mohammed BS, Szapary PO, Rader DJ, Edman JS, Klein S: A randomized trial of a low-carbohydrate diet for obesity. N Engl J Med. 2003, 348 (21): 2082-2090. 10.1056/NEJMoa022207.View ArticlePubMedGoogle Scholar
  30. Atkins RC: Dr Atkins New Diet Revolution. 2003, Vermillion, LondonGoogle Scholar
  31. Shai I, Schwarzfuchs D, Henkin Y, Shahar DR, Witkow S, Greenberg I, Golan R, Fraser D, Bolotin A, Vardi H, et al: Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N Engl J Med. 2008, 359 (3): 229-241. 10.1056/NEJMoa0708681.View ArticlePubMedGoogle Scholar
  32. Nelson SM, Fleming R: Obesity and reproduction: impact and interventions. Curr Opin Obstet Gynecol. 2007, 19 (4): 384-389. 10.1097/GCO.0b013e32825e1d70.View ArticlePubMedGoogle Scholar
  33. Zhang J, Wang C, Terroni PL, Cagampang FRA, Hanson M, Byrne CD: High-unsaturated-fat, high-protein, and low-carbohydrate diet during pregnancy and lactation modulates hepatic lipid metabolism in female adult offspring. Am J Physiol Regul Integr Comp Physiol. 2005, 288 (1): R112-118.View ArticlePubMedGoogle Scholar
  34. Zhang J, Lewis RM, Wang C, Hales N, Byrne CD: Maternal dietary iron restriction modulates hepatic lipid metabolism in the fetuses. Am J Physiol Regul Integr Comp Physiol. 2005, 288 (1): R104-111.View ArticlePubMedGoogle Scholar
  35. Ozanne SE, Olsen GS, Hansen LL, Tingey KJ, Nave BT, Wang CL, Hartil K, Petry CJ, Buckley AJ, Mosthaf-Seedorf L: Early growth restriction leads to down regulation of protein kinase C zeta and insulin resistance in skeletal muscle. J Endocrinol. 2003, 177 (2): 235-241. 10.1677/joe.0.1770235.View ArticlePubMedGoogle Scholar
  36. Khan IY, Dekou V, Douglas G , Jensen R, Hanson MA, Poston L, Taylor PD: A high-fat diet during rat pregnancy or suckling induces cardiovascular dysfunction in adult offspring. Am J Physiol Regul Integr Comp Physiol. 2005, 288: R127-R133.View ArticlePubMedGoogle Scholar
  37. Trottier G, Koski KG, Brun T, Toufexis DJ, Richard D, Walker C-D: Increased Fat Intake during Lactation Modifies Hypothalamic-Pituitary-Adrenal Responsiveness in Developing Rat Pups: A Possible Role for Leptin. Endocrinology. 1998, 139 (9): 3704-3711. 10.1210/en.139.9.3704.PubMedGoogle Scholar
  38. Guo F, Jen KL: High-fat feeding during pregnancy and lactation affects offspring metabolism in rats. Physiol Behav. 1995, 57 (4): 681-686. 10.1016/0031-9384(94)00342-4.View ArticlePubMedGoogle Scholar
  39. Srinivasan M, Katewa SD, Palaniyappan A, Pandya JD, Patel MS: Maternal high-fat diet consumption results in fetal malprogramming predisposing to the onset of metabolic syndrome-like phenotype in adulthood. Am J Physiol Endocrinol Metab. 2006, 291 (4): E792-799. 10.1152/ajpendo.00078.2006.View ArticlePubMedGoogle Scholar
  40. DeChiara TM, Efstratiadis A, Robertson EJ: A growth-deficiency phenotype in heterozygous mice carrying an insulin-like growth factor II gene disrupted by targeting. Nature. 1990, 345 (6270): 78-80. 10.1038/345078a0.View ArticlePubMedGoogle Scholar
  41. Gaunt TR, Cooper JA, Miller GJ, Day IN, O'Dell SD: Positive associations between single nucleotide polymorphisms in the IGF2 gene region and body mass index in adult males. Hum Mol Genet. 2001, 10 (14): 1491-1501. 10.1093/hmg/10.14.1491.View ArticlePubMedGoogle Scholar
  42. Gu D, O'Dell SD, Chen XH, Miller GJ, Day IN: Evidence of multiple causal sites affecting weight in the IGF2-INS-TH region of human chromosome 11. Hum Genet. 2002, 110 (2): 173-181. 10.1007/s00439-001-0663-5.View ArticlePubMedGoogle Scholar
  43. Sandhu MS, Gibson JM, Heald AH, Dunger DB, Wareham NJ: Low circulating IGF-II concentrations predict weight gain and obesity in humans. Diabetes. 2003, 52 (6): 1403-1408. 10.2337/diabetes.52.6.1403.View ArticlePubMedGoogle Scholar
  44. O'Dell SD, Miller GJ, Cooper JA, Hindmarsh PC, Pringle PJ, Ford H, Humphries SE, Day IN: Apal polymorphism in insulin-like growth factor II (IGF2) gene and weight in middle-aged males. Int J Obes Relat Metab Disord. 1997, 21 (9): 822-825. 10.1038/sj.ijo.0800483.View ArticlePubMedGoogle Scholar
  45. Da Costa TH, Williamson DH, Ward A, Bates P, Fisher R, Richardson L, Hill DJ, Robinson IC, Graham CF: High plasma insulin-like growth factor-II and low lipid content in transgenic mice: measurements of lipid metabolism. J Endocrinol. 1994, 143 (3): 433-439. 10.1677/joe.0.1430433.View ArticlePubMedGoogle Scholar
  46. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G: The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000, 403 (6772): 901-906. 10.1038/35002607.View ArticlePubMedGoogle Scholar
  47. Slack FJ, Basson M, Liu Z, Ambros V, Horvitz HR, Ruvkun G: The lin-41 RBCC gene acts in the C. elegans heterochronic pathway between the let-7 regulatory RNA and the LIN-29 transcription factor. Mol Cell. 2000, 5 (4): 659-669. 10.1016/S1097-2765(00)80245-2.View ArticlePubMedGoogle Scholar
  48. Liu CG, Calin GA, Volinia S, Croce CM: MicroRNA expression profiling using microarrays. Nat Protoc. 2008, 3 (4): 563-578. 10.1038/nprot.2008.14.View ArticlePubMedGoogle Scholar
  49. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al: Real-time quantification of microRNAs by stem-loop RT-PCR. Nuc Aci Res. 2005, 33 (20): e179-10.1093/nar/gni178.View ArticleGoogle Scholar
  50. Baskerville S, Bartel DP: Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA. 2005, 11 (3): 241-247. 10.1261/rna.7240905.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A: Identification of Mammalian microRNA Host Genes and Transcription Units. Genome Res. 2004, 14 (10a): 1902-1910. 10.1101/gr.2722704.PubMed CentralView ArticlePubMedGoogle Scholar
  52. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129 (7): 1401-1414. 10.1016/j.cell.2007.04.040.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Shi R, Chiang VL: Facile means for quantifying microRNA expression by real-time PCR. BioTechniques. 2005, 39 (4): 519-525. 10.2144/000112010.View ArticlePubMedGoogle Scholar
  54. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human MicroRNA targets. PLoS Biol. 2004, 2 (11): e363-10.1371/journal.pbio.0020363.PubMed CentralView ArticlePubMedGoogle Scholar
  55. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, et al: Combinatorial microRNA target predictions. Nat Genet. 2005, 37 (5): 495-500. 10.1038/ng1536.View ArticlePubMedGoogle Scholar
  56. Gerhard DS, Wagner L, Feingold EA, Shenmen CM, Grouse LH, Schuler G, Klein SL, Old S, Rasooly R, Good P, et al: The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome Res. 2004, 14 (10B): 2121-2127. 10.1101/gr.2596504.View ArticlePubMedGoogle Scholar
  57. Esau C, Davis S, Murray SF, Yu XX, Pandey SK, Pear M, Watts L, Booten SL, Graham M, McKay R, et al: miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell Metab. 2006, 3 (2): 87-98. 10.1016/j.cmet.2006.01.005.View ArticlePubMedGoogle Scholar
  58. Constancia M, Hemberger M, Hughes J, Dean W, Ferguson-Smith A, Fundele R, Stewart F, Kelsey G, Fowden A, Sibley C, et al: Placental-specific IGF-II is a major modulator of placental and fetal growth. Nature. 2002, 417 (6892): 945-948. 10.1038/nature00819.View ArticlePubMedGoogle Scholar
  59. Kwong WY, Miller DJ, Ursell E, Wild AE, Wilkins AP, Osmond C, Anthony FW, Fleming TP: Imprinted gene expression in the rat embryo-fetal axis is altered in response to periconceptional maternal low protein diet. Reproduction (Cambridge, England). 2006, 132 (2): 265-277.View ArticleGoogle Scholar
  60. Brameld JM, Mostyn A, Dandrea J, Stephenson TJ, Dawson JM, Buttery PJ, Symonds ME: Maternal nutrition alters the expression of insulin-like growth factors in fetal sheep liver and skeletal muscle. J Endocrinol. 2000, 167 (3): 429-437. 10.1677/joe.0.1670429.View ArticlePubMedGoogle Scholar
  61. Rappolee DA, Sturm KS, Behrendtsen O, Schultz GA, Pedersen RA, Werb Z: Insulin-like growth factor II acts through an endogenous growth pathway regulated by imprinting in early mouse embryos. Gen Dev. 1992, 6 (6): 939-952. 10.1101/gad.6.6.939.View ArticleGoogle Scholar
  62. Ludwig T, Eggenschwiler J, Fisher P, D'Ercole AJ, Davenport ML, Efstratiadis A: Mouse mutants lacking the type 2 IGF receptor (IGF2R) are rescued from perinatal lethality in Igf2 and Igf1r null backgrounds. Devl Biol. 1996, 177 (2): 517-535. 10.1006/dbio.1996.0182.View ArticleGoogle Scholar
  63. Adams SO, Nissley SP, Handwerger S, Rechler MM: Developmental patterns of insulin-like growth factor-I and -II synthesis and regulation in rat fibroblasts. Nature. 1983, 302 (5904): 150-153. 10.1038/302150a0.View ArticlePubMedGoogle Scholar
  64. Holly JM: The IGF-II enigma. Growth Horm IGF Res. 1998, 8 (3): 183-184. 10.1016/S1096-6374(98)80109-3.View ArticlePubMedGoogle Scholar
  65. Gonzalez FJ, Shah YM: PPARalpha: mechanism of species differences and hepatocarcinogenesis of peroxisome proliferators. Toxicology. 2008, 246 (1): 2-8.View ArticlePubMedGoogle Scholar
  66. Shah YM, Morimura K, Yang Q, Tanabe T, Takagi M, Gonzalez FJ: Peroxisome proliferator-activated receptor alpha regulates a microRNA-mediated signaling cascade responsible for hepatocellular proliferation. Mol Cell Biol. 2007, 27 (12): 4238-4247. 10.1128/MCB.00317-07.PubMed CentralView ArticlePubMedGoogle Scholar
  67. Grosshans H, Johnson T, Reinert KL, Gerstein M, Slack FJ: The temporal patterning microRNA let-7 regulates several transcription factors at the larval to adult transition in C. elegans. Dev Cell. 2005, 8 (3): 321-330. 10.1016/j.devcel.2004.12.019.View ArticlePubMedGoogle Scholar
  68. Tugwood JD, Issemann I, Anderson RG, Bundell KR, McPheat WL, Green S: The mouse peroxisome proliferator activated receptor recognizes a response element in the 5' flanking sequence of the rat acyl CoA oxidase gene. Embo J. 1992, 11: 433-439.PubMed CentralPubMedGoogle Scholar
  69. Zhang B, Marcus S, Sajjadi F, Alvares K, Reddy J, Subramani S, Rachubinski R, Capone J: Identification of a Peroxisome Proliferator-Responsive Element Upstream of the Gene Encoding Rat Peroxisomal Enoyl-CoA Hydratase/3-Hydroxyacyl-CoA Dehydrogenase. PNAS. 1992, 89 (16): 7541-7545. 10.1073/pnas.89.16.7541.PubMed CentralView ArticlePubMedGoogle Scholar
  70. Gulick T, Cresci S, Caira T, Moore D, Kelly D: The Peroxisome Proliferator-Activated Receptor Regulates Mitochondrial Fatty Acid Oxidative Enzyme Gene Expression. PNAS. 1994, 91 (23): 11012-11016. 10.1073/pnas.91.23.11012.PubMed CentralView ArticlePubMedGoogle Scholar
  71. Rodriguez JC, Gil Gomez G, Hegardt FG, Haro D: Peroxisome proliferator-activated receptor mediates induction of the mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase gene by fatty acids. J Biol Chem. 1994, 269 (29): 18767-18772.PubMedGoogle Scholar
  72. Muerhoff A, Griffin K, Johnson E: The peroxisome proliferator-activated receptor mediates the induction of CYP4A6, a cytochrome P450 fatty acid omega-hydroxylase, by clofibric acid. J Biol Chem. 1992, 267 (27): 19051-19053.PubMedGoogle Scholar
  73. Aldridge TC, Tugwood JD, Green S: Identification and characterization of DNA elements implicated in the regulation of CYP4A1 transcription. Biochem J. 1995, 306 (Pt 2): 473-479.PubMed CentralView ArticlePubMedGoogle Scholar
  74. Hu JF, Nguyen PH, Pham NV, Vu TH, Hoffman AR: Modulation of Igf2 genomic imprinting in mice induced by 5-azacytidine, an inhibitor of DNA methylation. Mol Endocrinol. 1997, 11 (13): 1891-1898. 10.1210/me.11.13.1891.View ArticlePubMedGoogle Scholar
  75. Kovacheva VP, Mellott TJ, Davison JM, Wagner N, Lopez-Coviella I, Schnitzler AC, Blusztajn JK: Gestational choline deficiency causes global and Igf2 gene DNA hypermethylation by up-regulation of Dnmt1 expression. J Biol Chem. 2007, 282 (43): 31777-31788. 10.1074/jbc.M705539200.View ArticlePubMedGoogle Scholar
  76. 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 (3): 435-439. 10.1017/S0007114507352392.PubMed CentralView ArticlePubMedGoogle Scholar
  77. 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 (6): 1382-1386.PubMedGoogle Scholar
  78. Brueckner B, Stresemann C, Kuner R, Mund C, Musch T, Meister M, Sultmann H, Lyko F: The human let-7a-3 locus contains an epigenetically regulated microRNA gene with oncogenic function. Cancer Res. 2007, 67 (4): 1419-1423. 10.1158/0008-5472.CAN-06-4074.View ArticlePubMedGoogle Scholar
  79. Lewis JD, Meehan RR, Henzel WJ, Maurer-Fogy I, Jeppesen P, Klein F, Bird A: Purification, sequence, and cellular localization of a novel chromosomal protein that binds to methylated DNA. Cell. 1992, 69 (6): 905-914. 10.1016/0092-8674(92)90610-O.View ArticlePubMedGoogle Scholar
  80. Kimura H, Shiota K: Methyl-CpG-binding protein, MeCP2, is a target molecule for maintenance DNA methyltransferase, Dnmt1. J Biol Chem. 2003, 278 (7): 4806-4812. 10.1074/jbc.M209923200.View ArticlePubMedGoogle Scholar
  81. Ke X, Lei Q, James SJ, Kelleher SL, Melnyk S, Jernigan S, Yu X, Wang L, Callaway CW, Gill G, et al: Uteroplacental insufficiency affects epigenetic determinants of chromatin structure in brains of neonatal and juvenile IUGR rats. Physiol Genom. 2006, 25 (1): 16-28. 10.1152/physiolgenomics.00093.2005.View ArticleGoogle Scholar
  82. Gardner DS, Jackson AA, Langley-Evans SC: Maintenance of Maternal Diet-Induced Hypertension in the Rat Is Dependent on Glucocorticoids. Hypertension. 1997, 30 (6): 1525-1530.View ArticlePubMedGoogle Scholar
  83. Zhang J, Byrne CD: Differential hepatic lobar gene expression in offspring exposed to altered maternal dietary protein intake. Am J Physiol Gastrointest Liver Physiol. 2000, 278 (1): G128-136.PubMedGoogle Scholar
  84. Christofori G, Naik P, Hanahan D: Deregulation of both imprinted and expressed alleles of the insulin-like growth factor 2 gene during beta-cell tumorigenesis. Nat Genet. 1995, 10 (2): 196-201. 10.1038/ng0695-196.View ArticlePubMedGoogle Scholar
  85. Griffiths-Jones S: The microRNA Registry. Nucl Acids Res. 2004, 32 (90001): D109-111. 10.1093/nar/gkh023.PubMed CentralView ArticlePubMedGoogle Scholar
  86. Bai S, Nasser MW, Wang B, Hsu SH, Datta J, Kutay H, Yadav A, Nuovo G, Kumar P, Ghoshal K: MicroRNA-122 inhibits tumorigenic properties of hepatocellular carcinoma cells and sensitizes these cells to Sorafenib. J Biol Chem. 2009,Google Scholar

Copyright

© Zhang et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement