Results
General characteristics of experimental animals.
Four months after the beginning of the dietary regimen and prior to conception, dams fed the HFD exhibited increased body weight (Fig. 1A) and LDL-cholesterol (Fig. 1B) and showed a trend for a rise in blood triglycerides compared with RD (Fig. 1D). During pregnancy, dams fed the HFD lost nearly 0.2 kg in body weight, while RD dams gained 1.8 kg (Fig. 1C).
Fetal morphometrics at 165 days of gestation.
Table 1 presents morphometric measures on the placenta and fetuses of RD and HFD dams at cesarean section on day 165. Although there was no difference in placental weight or volume, placental efficiency, expressed as the fetal mass supported per unit placental mass, was reduced in HFD pregnancies compared with control RD (P < 0.02)(Table 1). Fetal body weight was reduced by 16% in HFD pregnancies. Brain and thymus weights were significantly increased in HFD fetuses compared with RD. No change in heart weight was detected (Table 1).
Table 1.
| Groups | RD (n = 22) | HFD (n = 5) | P Value |
|---|---|---|---|
| Fetal measures | mean ± SE | mean ± SE | |
| Body weight, g | 806 ± 24 | 675 ± 37 | < 0.02* |
| Fetal measures as percent fetal weight | |||
| Placenta | 26.0 ± 0.80 | 32.1 ± 2.50 | < 0.01* |
| Brain | 10.4 ± 0.30 | 11.9 ± 0.60 | < 0.02* |
| Heart | 0.61 ± 0.02 | 0.63 ± 0.04 | 0.6 |
| Kidneys | 0.56 ± 0.02 | 0.59 ± 0.03 | 0.5 |
| Liver | 2.97 ± 0.06 | 2.97 ± 0.10 | 1.0 |
| Lungs | 2.16 ± 0.07 | 2.43 ± 0.10 | < 0.09 |
| Pancreas | 0.07 ± 0.01 | 0.07 ± 0.01 | 0.9 |
| Thymus | 0.42 ± 0.02 | 0.52 ± 0.05 | 0.048* |
RD, regular diet; HFD, high-fat diet.
P < 0.05.
Heart histology.
Hematoxylin-and-eosin staining did not reveal any significant differences in myofiber orientation in fetal ventricular tissue from RD and HFD groups (not shown). However, Masson trichrome-stained cardiac sections (Fig. 2) showed increased myocardial fibrosis in HFD fetal hearts (22.05 ± 3.8%, n = 5) compared with RD hearts (2.1 ± 0.76%, n = 6; P = 0.003).
MiRNA sequencing in fetal baboon hearts.
We isolated total RNA from the hearts of baboon fetuses. The RNA samples were checked for RNA quality control and processed to generate a cDNA library on which deep sequencing was performed. Overall, 27,857,063 sequence reads were obtained (Table 2). A total of 2,369,584 unique sequences were annotated to annotated small RNA sequences. The small RNA libraries exhibited a diverse size distribution of sequence reads that aligned to the human genome (Fig. 3). miRNAs were the most abundantly expressed small RNAs with 22,611,923 or 81% of total sequences. Other small noncoding RNAs such as small interfering RNAs, small nucleolar RNAs, small nuclear RNAs, and transfer RNAs comprised only 1% (318,109) of the total sequences. On average 19,708,547 sequences (or 87.2%) were mapped to miRBase. We discovered 961 miRNAs in the hearts from baboon fetuses: 601 (68%) were identical to human miRNAs; 23 were mapped to other mammalian miRNAs. Of these mapped miRNAs, 157 showed new genome location, and 180 baboon miRNAs were novel; of these 86 were unmapped to known miRNAs (Table 3).
Table 2.
| SequSeq, n | Mappable SequSeq, % | Unique Seq, n | Mappable Unique Seq, % | |
|---|---|---|---|---|
| Raw | 27,857,063 | 2,369,584 | ||
| Mapped to mRNA | 749,824 | 14,425 | ||
| Mapped to other RNAs: rRNA, tRNA, snRNA, snoRNA, | 318,109 | 9,810 | ||
| Mapped to RepBase | 5,537 | 304 | ||
| Total mappable for miRNA | 22,611,923 | 100 | 118,674 | 100 |
| Mapped to miRBase | 19,274,534 | 85.2 | 18,955 | 16 |
| Unmapped to miRBase | 2,912,223 | 12.9 | 96,136 | 81.0 |
| Mapped total | 19,274,534 | 85.2 | 18,955 | 16.0 |
| No hit | 3,337,389 | 14.8 | 90,723 | 84 |
rRNA, ribosomal RNA; tRNA, transfer RNA; snRNA, small nuclear RNA, snoRNA, small nucleolar RNA. RepBase is a database of prototypic sequences representing repetitive DNA from different eukaryotic species. MirBase is a searchable database of published miRNA sequences and annotation.
Table 3.
| Known miRNAs | Group | Unique miRs, n |
|---|---|---|
| Of Homo sapiens | group 1a | 601 |
| Of mammals but novel to Homo sapiens | group 1b | 23 |
| Of Homo sapiens and mammals, but with new genome locations | group 1c | 157 |
| Predicted miRs | ||
| Mapped to known mammal miRs and genome; within hairpins | group 2 | 17 |
| Mapped to known mammal miRs but unmapped to genome | group 3 | 77 |
| Unmapped to known miRs but mapped to genome and within hairpin | group 4 | 86 |
| Total (Unique miRs) | 961 |
microRNA: miRNA or miR.
Dysregulation of fetal cardiac miRNA expression in response to maternal obesity.
MiRNA expression profiling using microarrays is a powerful high-throughput tool capable of monitoring the regulatory networks of the entire genome. To identify regulatory networks involved into the fetal response to maternal obesity, miRNA microarray analysis was performed by LC Sciences. Comprehensive miRNA profiles were generated for 11 fetal hearts from baboons born to RD (n = 3 males and 3 females) or HFD (n = 3 females and 2 males)-fed mothers. The expression pattern of differentially expressed miRNAs is presented as a clustered heat map (Fig. 4). Overall, 80 miRNAs were altered in response to maternal obesity (P < 0.05). Of those, 55 miRNAs were upregulated and 25 downregulated. In total, 22 miRNAs (27.5%) mapped exclusively to human, nine miRNAs mapped to other mammalian species: three miRNAs to Bos taurus (bta-miRs-2436-3p, -1296, -2889), three to Canis familiaris (cfa-miRs-143, -135a-2, -127), one miRNA to Monodelphis domestica (mdo-miR-139), one to Mus musculus (mmu-miR-326), and one to Pongo pygmaeus (ppy-miR-638). The remaining 49 miRNAs were unmapped at the time of analysis. The greatest expression change, >16-fold upregulation, was found for hsa-miR-1296 in HFD hearts compared with RD. Other miRNAs demonstrating notable (>4-fold) nutrient sensitivity included overexpression of hsa-mir-30a, hsa-mir-1-2, hsa-mir-223, hsa-mir-197, hsa-mir-145, hsa-mir-21, and hsa-mir-133a-1. Of the 80 significantly differentially expressed miRNAs, a group of 14 (10 upregulated and 4 downregulated) has previously been reported to be dysregulated in experimental or human cardiovascular diseases. This group includes miR-133a, -197, miR-30a, miR-499, and miR-451 (Table 4). A second group includes differentially expressed miRNA that have not been previously linked to cardiac development and function but have been shown to be involved in other human diseases (Table 5), such as cancer (miR-326, miR-181a, miR-377, miR-584, miR-593, miR-101-1, miR-193, miR-342, miR-99b), diabetes (miR-223, miR-181a), multiple sclerosis (miR-326), and Alzheimer's disease (miR-146).
Table 4.
| miRNA | Role in CVD |
|---|---|
| Upregulated | |
| hsa-miR-30a | regulates myocardial matrix remodeling (31) and atrial fibrillation (66) |
| hsa-miR-30c | myocardial infarction (31) |
| hsa-miR-145 | regulates smooth muscle cell plasticity (22) |
| hsa-miR-451 | myocardial infarction (11) |
| hsa-miR-499 | regulates mitochondrial dynamics (105) and myosin isoforms (29) |
| hsa-miR-223 | regulates glut4 expression and cardiomyocyte glucose metabolism (69) |
| hsa-miR-133a | involved in cardiac hypertrophy (35) |
| hsa-miR-21 | involved in heart failure (100), myocardial infarction (28), and cardiac hypertrophy (97) |
| hsa-miR-197 | involved in cardiac hypertrophy (17) |
| hsa-miR-139 | involved in myocardial infarction (86) |
| Downregulated | |
| hsa-miR-1-2 | involved in cardiomyopathy (113) |
| hsa-miR-27b | involved in cardiac hypertrophy (113) |
| hsa-mir-378 | targets IGF1R (59), inhibits apoptosis in cardiomyocytes (112) |
| hsa-miR-18a | regulates myocardial matrix remodeling in age related heart failure (102) |
CVD, cardiovascular disease; IGFR1, insulin-like growth factor 1 receptor. The numbers in parentheses are reference list numbers.
Table 5.
| miRNA | Experimentally Observed Function |
|---|---|
| Upregulated | |
| hsa-miR-326 | cancer (26); multiple sclerosis (30) |
| hsa-miR-483 | pancreatitis (9) |
| hsa-miR-181a | cancer (80); diabetes (58) |
| hsa-miR-377 | nephropathy (106); cancer (72) |
| hsa-miR-99b | myopathy (32), endometriosis (79) |
| hsa-miR-584 | cancer (49); multiple sclerosis (57) |
| hsa-miR-146 | Alzheimer disease (67) |
| hsa-miR-593 | cancer (54) |
| hsa-miR-101-1 | cancer (53) |
| Downregulated | |
| hsa-miR-582-3p | osteoarthritis (25) |
| hsa-miR-505 | tumor suppression (110) |
| hsa-miR-193a | cancer (60) |
| hsa-miR-342 | cancer (48) |
The numbers in parentheses refer to reference list numbers.
Quantitative RT-PCR analysis of selected miRNAs.
To confirm the accuracy of the results in the microarray study, we performed real-time PCR on 11 differentially expressed miRNAs. Four criteria were used to select candidate miRNAs: 1) the miRNA must be highly expressed in the heart; 2) the miRNA has to be previously linked to cardiac disease; 3) only one representative from a given miRNA family should be considered; and 4) the miRNA must be a target of a commercially available RT-PCR assay at the time of the work. Table 6 summarizes the data and illustrates the differences in expression between the HFD and RD RNA populations found by RT-PCR. U18 (Applied Biosystems) was used to normalize the RT-PCR data set. The normalized RT-PCR data yielded a correlation of 0.68 (P < 0.02) with microarrays. We found that compared with microarray: 1) the changes in expression of seven miRNAs (63%) were consistent with those determined by microarrays: hsa-mir-30a, hsa-mir-1-2, hsa-mir-223, hsa-mir-197, hsa-mir-18a, hsa-mir-584, hsa-mir-499; 2) changes in the opposite direction to those shown by microarrays were found for two miRNAs: hsa-mir-451, hsa-mir-30c-1, and 3) two miRNAs remained unchanged in contrast to our microarray data: hsa-mir-145, hsa-mir-21.
Table 6.
| miRNAs | Fold Change by Microarray | Fold Change by RT-PCR |
|---|---|---|
| Validated | ||
| hsa-mir-30a | 7.0 | 3.9* |
| hsa-mir-1-2 | 7.0 | 3.4* |
| hsa-mir-223 | 6.0 | 1.4* |
| hsa-mir-197 | 6.0 | 1.5* |
| hsa-mir-18a | 0.8 | 0.76* |
| hsa-mir-584 | 4 | 1.4* |
| hsa-mir-499 | 1.7 | 1.3* |
| Validated with opposite direction | ||
| hsa-mir-451 | 2 | 0.7* |
| hsa-mir-30c-1 | 2 | 0.8* |
| Not validated | ||
| hsa-mir-145 | 6.0 | 1 |
| hsa-mir-21 | 4.8 | 1 |
The microarray data were converted to fold change to directly compare with RT-PCR values, n = 6 RD and 5 HFD,
P < 0.05.
Identification of miRNA predicted targets.
miRNAs can regulate a large number of target genes and several databases based on various algorithms are available for predicting the targets of selected miRNAs. Target Scan 5.0, PicTar, and DIANA LAB were used to predict gene targets of the dysregulated miRNAs identified in this study. Overall >1,700 predicted and experimentally observed targets were identified. Using Ingenuity Pathways Analysis IPA), we utilized an miRNA target filter to limit the search to targets expressed in the heart. As a result, >1,500 target genes were identified, 133 of which were experimentally observed and the others classified as “highly predicted.”
A subset of validated and predicted target genes was selected to confirm the expression changes by Western blot analysis. Two extracellular matrix proteins and mediators of cardiac fibrosis in humans and rodents (95, 96), CTGF, and TSP-1 were among the validated targets. TSP-1 appeared to be a target of upregulated miR-1(4) and two downregulated miRNAs: miR-27b (115) and miR-18a (102). We found significantly increased cardiac protein levels of TSP-1 (P < 0.05) in HFD hearts compared with RD hearts (Fig. 5, A and C). Similarly, the protein level of CTGF, a target gene of three upregulated miRNAs, miR-145 (63), miR-133a, and miR-30c (31) and one downregulated miR-18a (102), was significantly higher in HFD vs. RD hearts (P = 0.01) (Fig. 5, A and D). Among the predicted targets, Claudin1 (CLDN1), a tight junction component, was identified as the potential target for four upregulated miRNAs: miR-139, miR-145, miR-584, miR-30a, and Western blot analysis showed 50% reduction in CLDN1 levels in HFD hearts compared with RD (Fig. 5, A and B). Novel miRNAs differentially expressed in HFD hearts compared with RD also had a great number of predicted target genes. Table 7 summarizes the information regarding cardiac-related potential target genes of five upregulated and five downregulated novel miRNAs.
Table 7.
| PC# | Fold Change | Seed Region | Targets, n | Sample Target Genes |
|---|---|---|---|---|
| Upregulated | ||||
| 32270T4 | 6.3 | ACGGGGA | 4 | PAX2, paired box gene 2; RAP2B, member of RAS oncogene family; PAX2, paired box gene2 |
| 2048T4 | 4.5 | AUGAGAG | 83 | caspase-3; SLC5A6, solute carrier family 5 (sodium-dependent vitamin transporter), member 6; MRPL43, mitochondrial ribosomal protein L43 |
| 11444T3 | 4.3 | GGUCCCC | 242 | CALM1, calmodulin 1; MYH9, myosin, heavy chain 9; ACTN4, actinin, alpha 4, HSPB8, heat shock protein 8 |
| 24524T4 | 2.6 | CAGAAUU | 289 | TP53INP2, tumor protein p53 inducible nuclear protein 2; MAP2, microtubule-associated protein 2, CTGF, connective tissue growth factor |
| 18991T4 | 2.3 | AAGGAUU | 127 | TXNDC13, thioredoxin domain containing 13; ANK2; ankyrin 2; TNPO1, transportin 1 |
| Downregulated | ||||
| 9971T4 | 0.8 | AAUACAU | 656 | KLF3, Kruppel-like factor 3; ADAM10, ADAM metallopeptidase domain 10; APP, amyloid beta (A4) precursor protein |
| 2949T3 | 0.5 | AUCCACG | 48 | ARNTL, aryl hydrocarbon receptor nuclear translocator-like; IGF2BP1, insulin-like growth factor 2 mRNA binding protein 1; HAND2, heart and neural crest derivatives expressed 2 |
| 4398T3 | 0.5 | AGCAGCC | 432 | EIF4E, eukaryotic translation initiation factor 4E; TRAK1, trafficking protein, kinesin binding 1; USP32, ubiquitin specific peptidase 32; CLDN1, claudin1 |
| 5663T3 | 0.4 | CAGTCGG | 6 | AHDC1, AT hook, DNA binding motif, containing 1; FOXJ3, forkhead box J3; GTPBP5, GTP binding protein 5 |
| 6274T3 | 0.4 | AACUGGU | 145 | TPM4, tropomyosin 4; CREB5 cAMP responsive element binding protein 5; GJA1, gap junction protein, alpha 1 |
The fold change, the 2–8 mer seed region, numbers of predicted target genes according to Targetscan and sample target genes, for each novel miRNA are shown. PC, potential candidate.
IPA of predicted targets.
Using the entire list of identified predicted targets as a starting point, we utilized IPA to reveal potential diseases, molecular functions, physiological systems, and canonical pathways associated with differentially expressed miRNAs. Not surprisingly, the analysis identified developmental disorder and cardiovascular disease as the main diseases associated with maternal overnutrition (324 and 251 molecules, respectively; P < 0.05). Cellular death and survival, growth, and proliferation and cellular development were the most affected molecular and cellular functions in response to maternal overnutrition. We next evaluated changes in cell death and proliferation in the RD and HFD hearts. TUNEL assay showed extremely low levels of cardiomyocyte cell death in ventricular tissue of both RD and HFD fetuses (not shown). In contrast, the proliferation rates measured by Ki-67 staining were significantly higher in HFD hearts compared with RD (P < 0.05, Fig. 6).
IPA also identified 383 potential transcriptional regulators (not shown). The transcription factors with the highest degree of probability and target molecules were: 1) tumor protein p53 (TP53), 2) peroxisome proliferator-activated receptor gamma (PPAR-γ), and 3) hypoxia-inducible factor 1 alpha (HIF-1α). Western blot analysis showed fourfold decrease in the levels of HIF-1α in HFD hearts compared with RD hearts (P < 0.05, Fig. 7, A and B). The expression of p53 showed a trend toward a decrease, which, however, did not reach a statistical significance (P = 0.1). No differences in PPAR-γ were detected (not shown).
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