Open Access

Maternal chromium restriction induces insulin resistance in adult mice offspring through miRNA

  • Authors:
    • Qian Zhang
    • Xiaofang Sun
    • Xinhua Xiao
    • Jia Zheng
    • Ming Li
    • Miao Yu
    • Fan Ping
    • Zhixin Wang
    • Cuijuan Qi
    • Tong Wang
    • Xiaojing Wang
  • View Affiliations

  • Published online on: December 18, 2017     https://doi.org/10.3892/ijmm.2017.3328
  • Pages: 1547-1559
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Increasing evidence suggests that undernutrition during the fetal period may lead to glucose intolerance, impair the insulin response and induce insulin resistance (IR). Considering the importance of chromium (Cr) in maintaining carbohydrate metabolism, the present study aimed to determine the effects of maternal low Cr (LC) on glucose metabolism in C57BL mice offspring, and the involved mechanisms. Weaned C57BL mice were born from mothers fed a control diet or LC diet, and were then fed a control or LC diet for 13 weeks. Subsequently, the liver microRNA (miRNA/miR) expression profile was analyzed by miRNA array analysis. A maternal LC diet increased fasting serum glucose (P<0.05) and insulin levels (P<0.05), homeostasis model assessment of IR index (P<0.01), and the area under curve for glucose concentration during oral glucose tolerance test (P<0.01). In addition, 8 upregulated and 6 downregulated miRNAs were identified in the maternal LC group (fold change ≥2, P<0.05). miRNA‑gene networks, Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially expressed miRNAs, and miRNA overexpression in HepG2 cells revealed the critical role of insulin signaling, via miR‑327, miR‑466f‑3p and miR‑223‑3p, in the effects of early life Cr restriction on glucose metabolism. In conclusion, maternal Cr restriction may irreversibly increase IR, which may involve a specific miRNA affecting the insulin signaling pathway.

Introduction

An increasing number of individuals are being diagnosed with diabetes, particularly type 2 diabetes (T2D) (1). In addition to genetic factors, environmental factors contribute to the occurrence of T2D. In human and animal experiments, a negative association between intrauterine nutrition and metabolic disease risk has been observed. Hales et al reported a U-shaped relationship between birth weight and glucose intolerance at 64 years of age (2). Furthermore, low-birth weight subjects exhibited increased plasma levels of proinsulin, which is an indication of islet β cell dysfunction. Based on these findings, Hales and Barker proposed the 'thrifty phenotype hypothesis', which addresses the important role of poor nutrition in early life on permanent changes in glucose-insulin metabolism, to explain this relationship (3).

Chromium (Cr) is an essential glucose regulator (4,5). The normal human range of blood Cr is 0.12–0.67 μg/l (6). However, total body Cr concentrations decrease by 25–40% with age. Subjects with Cr deficiency have increased blood glucose and insulin levels (7). In addition, the tissue Cr levels of patients with diabetes are lower compared with in normal control subjects, and a correlation exists between low serum Cr levels and the occurrence of T2D (8).

A previous study demonstrated that maternal Cr deficiency leads to insulin resistance (IR) and impaired glucose tolerance in WNIN rat offspring. The underlying mechanism involves increased oxidative stress (9). In addition, the molecular mechanisms underlying fetal programming may be partly associated with epigenetic regulation of the expression of key genes (10). Our recent research indicates that maternal chromium restriction leads the miRNA dysfunction involved with MAPK signaling pathway in the adipose of female offspring (11).

MicroRNAs (miRNA/miRs) are associated with important epigenetic mechanisms. miRNAs are short noncoding RNA molecules that bind to target genes to regulate various cellular processes. Increasing research has suggested that miRNAs are involved in the incidence and development of diabetes. miR-125a is upregulated in Goto-Kakizaki (GK) rats (T2D rat model) compared with in normal rats (12), and may lead to IR. In addition, increased miR-143 has been detected in the livers of diabetic rats (13); miR-143 downregulates oxysterol-binding protein-related protein 8 and subsequently impairs the ability of insulin to induce the activation of protein kinase B (AKT) signaling, a central node of insulin action to induce glucose metabolism. Upregulation of miR-181a in diabetic livers and hepatocytes decreases sirtuin 1 expression, thus inactivating insulin signaling and glucose metabolism (14). Furthermore, miR-96 and miR-126 directly target the insulin receptor substrate 1 (IRS1) 3′-untranslated region; a reduction in IRS1 is involved in IR under mitochondrial dysfunction in hepatocytes (15,16). Downregulation of miR-200 also impairs AKT/glycogen synthase kinase-mediated glycogenesis in the liver, resulting in hepatic IR (17).

The present study hypothesized that an association exists between maternal low Cr (LC) status and epigenetic reprogramming of the transcriptome by means of miRNA in offspring. To test this hypothesis, a whole miRNA expression array was performed in mice exposed to maternal Cr restriction.

Materials and methods

Animals and protocol

All procedures were performed in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the National Institute of Health (18). The present study was approved by the Animal Care Committee of the Peking Union Medical Hospital (Beijing, China; permit no. MC-07-6004), and all efforts were made to minimize suffering. Female (n=20) and male (n=10) C57BL mice (age, 7 weeks; weight, 18.7±1.8 g) were purchased from the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China; SCXK-2013-0107). All mice were maintained in ventilated individual cages at 24±1°C and had access to food and water ad libitum; lights were switched on between 6 a.m. and 6 p.m.. Mating was confirmed by the presence of sperm in a vaginal smear the following morning. Pregnant females (n=16) were randomly divided into two groups: Control group (CON, fed a standard rodent casein-based diet based on the American Institute of Nutrition AIN-93G diet) and LC diet group (fed a LC diet, which lacked only in Cr) (n=8/group; Table I). The diet protocol was maintained throughout the gestation and lactation periods. The concentration of Cr in the CON and LC diets was 1.19 and 0.14 mg Cr/kg−1 diet, respectively [assessed by atomic absorption spectrometer (TAS986; Beijing Persee General Corporation, Beijing, China)]. All diets were provided by Research Diets, Inc. (New Brunswick, NJ, USA). Following birth of the offspring, the litter size in each cage was randomly adjusted to 6 pups (3 males, 3 females, if possible) to ensure equal nutrition until the pups were weaned. Pups were kept under the same housing conditions as the adult mice. After weaning, pups were randomly subdivided into four groups: i) Pregnancy and lactation CON diet group fed a CON diet after weaning (CON/CON); ii) pregnancy and lactation CON diet group fed a LC diet after weaning (CON/LC); iii) pregnancy and lactation LC group fed a LC diet after weaning (LC/LC) and iv) pregnancy and lactation LC group fed a CON diet after weaning (LC/CON) (n=8/group; 1 male pup from each litter was randomly assigned to the experimental groups). Male offspring only were used for the present study to avoid sex differences on the effect of maternal nutrition on glucose metabolism (19). At the end of the experimental period (16 weeks of age), blood samples were collected from the intraorbital retrobulbar plexus in anesthetized mice (ketamine 100 mg/kg, i.p.; Pfizer, Inc., New York, NY, USA) following a 10-h fast. All male mice (n=8/group) from eight different litters (1 male offspring/litter) were then sacrificed. Whole livers of the offspring were quickly removed and stored at −80°C for further analysis. The experimental design is presented in Fig. 1.

Table I

Composition of dietsa.

Table I

Composition of dietsa.

Ingredient (g or mg/kg diet)CONLC
Cornstarch, g397.5397.5
Casein, g200.0200.0
Dextrinized cornstarch, g132.0132.0
Sucrose, g100.0100.0
Soybean oil, g70.070.0
Fiber, g50.050.0
Mineral mix, g35.035.0
Vitamin mix AIN-93-VX, g10.010.0
L-Cystine, g3.03.0
Choline bitartrate, g2.62.6
Tert-butylhydroquinone, mg14.014.0
Chromium potassium sulfate 12H2O (10.42% Cr), mg275.00.0

a Reeves (50); CON, control; LC, low chromium.

Measurement of serum Cr levels

The blood samples from the dams at weaning and the 16-week-old offspring were centrifuged and the resulting serum was collected for Cr analysis. Serum Cr concentrations were measured using an atomic absorption spectrometer (Atomic Absorption Spectrophotometer; Hitachi, Ltd., Tokyo, Japan).

Measurement of body weight and blood glucose levels

Body weight and blood glucose levels of dams were measured at weaning, and were measured in pups at birth, weaning and at 16 weeks of age. Tail blood glucose measurements were recorded using a blood glucose analyzer (Bayer Contour TS Glucometer; Bayer AG, Leverkusen, Germany).

Oral glucose tolerance test (OGTT)

An OGTT was performed on dams at weaning and on pups at 16 weeks, after 12 h of food deprivation. The OGTT was performed in the morning. Glucose was introduced into the mice via oral gavage at a final dose of 2 g/kg body weight. Blood samples were obtained from the tail vein 30, 60 and 120 min after glucose load in order to analyze glucose concentration using a glucometer (Bayer Contour TS Glucometer; Bayer AG). Glucose responses during the OGTT were evaluated by estimating the total area under the curve (AUC) using the trapezoidal method.

Serum insulin levels and homeostasis model assessment of IR (HOMA-IR) index

After 12 h of food deprivation, tail blood samples were obtained and centrifuged. Serum insulin concentration was quantified using an ELISA method (mouse insulin kit; EZRMI-13K; EMD Millipore, Billerica, MA, USA) according to the manufacturer's protocol. Serum glucose was measured by an enzyme end-point method (Roche Diagnostics, GmnH, Mannheim, Germany). HOMA-IR was calculated as follows: Fasting serum glucose × fasting serum insulin/22.5.

RNA extraction, cDNA synthesis and miRNA array hybridization

Total liver RNA was isolated using the mirVana™ RNA Isolation kit (Ambion; Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer's protocol. All RNA samples were examined for the absence of DNA and RNA degradation by denaturing agarose gel electrophoresis. Total RNA was then reverse transcribed using the PrimeScript Reverse Transcription (RT) kit (Takara Bio, Inc., Shiga, Japan) according to the manufacturer's protocol. The expression of miRNAs contained in miRBase version 20.0 (released in June, 2013; http://www.mirbase.org) was analyzed by microarray using the Affymetrix Multispecies miRNA 4.0 array (Affymetrix; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Raw data were normalized, processed, and analyzed for statistical significance using Student's t-test and post hoc Tukey's test was applied for a multiple testing correction.

miRNA quantitative polymerase chain reaction (qPCR) validation

Total RNA obained using the mirVana™ RNA Isolation kit mentioned above was quantified using a NanoDrop 1000, and then reverse transcribed using TaqMan MicroRNA RT kit and RT primers from the respective TaqMan MicroRNA assay kit. qPCR was performed on an ABI 7900 thermocycler using the TaqMan Universal PCR Master Mix and TaqMan probes from the TaqMan MicroRNA assay kit (all from Applied Biosystems; Thermo Fisher Scientific, Inc.). Taqman probes were synthesized according to miRNA mature sequneces (Table III). Amplification was performed at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 60 sec. The reaction conditions were 16°C for 30 min, 42°C for 30 min and 85°C for 5 min. Tissue miRNA levels were normalized to endogenous U6.

Table III

Significantly differentially expressed miRNAs in the livers of offspring from dams fed a low chromium diet.

Table III

Significantly differentially expressed miRNAs in the livers of offspring from dams fed a low chromium diet.

MicroRNAFold changeRegulationP-valueMature sequences
mmu-miR-30f0.4549Down0.000241 GUAAACAUCCGACUGAAAGCUC
mmu-miR-344b-5p0.4134Down 3.56×10−5 AGUCAGGCUCCUGGCUAAAGUUC
mmu-miR-28b0.2847Down 3.45×10−6 AGGAGCUCACAAUCUAUUUAG
mmu-miR-296-3p0.3765Down0.000718 GAGGGUUGGGUGGAGGCUCUCC
mmu-miR-7062-3p0.4833Down0.001047 ACUAACUUCUCCUGGCCCCACAG
mmu-miR-3090-5p0.4811Down0.000773 GUCUGGGUGGGGCCUGAGAUC
mmu-miR-122-3p3.0022Up0.000983 AAACGCCAUUAUCACACUAA
mmu-miR-62382.7639Up0.001775 UUAUUAGUCAGUGGAGGAAAUG
mmu-miR-669a-3p2.1272Up0.007318 ACAUAACAUACACACACACGUAU
mmu-miR-6912.4515Up 8.43×10−5 AUUCCUGAAGAGAGGCAGAAAA
mmu-miR-223-3p2.5277Up0.000156 UGUCAGUUUGUCAAAUACCCCA
mmu-miR-3272.9025Up0.000762 ACUUGAGGGGCAUGAGGAU
mmu-miR-2072.2873Up0.000728 GCUUCUCCUGGCUCUCCUCCCUC
mmu-miR-466f-3p2.1942Up 1.06×10−5 CAUACACACACACAUACACAC

[i] miR/miRNA, microRNA.

miRNA target gene bioinformatics analysis

miRTarBase database version 6.0 (released September, 2015; http://mirtarbase.mbc.nctu.edu.tw/) was used to perform bioinformatics analysis of the differentially expressed miRNAs and their validated target sequences (20).

miRNA target genes qPCR analysis

Total RNA extracted from the liver samples using the mirVana™ RNA Isolation kit mentioned above was reverse-transcibed by Superscript II (Invitrogen; Thermo Fisher Scientific, Inc.). qPCR analysis of gene expression was performed using an ABI Prism 7500 system (Applied Biosystems; Thermo Fisher Scientific, Inc.). The primers used were obtained from Applied Biosystems; Thermo Fisher Scientific, Inc. (Table II). Each PCR sample contained 20 ng reverse-transcribed RNA, which was analyzed using SYBR-Green PCR Master Mix (Applied Biosystems; Thermo Fisher Scientific, Inc.). The following reaction conditions were used: 48°C for 30 min, 95°C for 15 min, 40 cycles of 95°C for 15 sec, 55°C for 1 min. Target mRNA expression was normalized to β-actin expression and are expressed as a relative value using the cycle quantification (Cq) method (2−ΔΔCq) (21).

Table II

Quantitative polymerase chain reaction primers.

Table II

Quantitative polymerase chain reaction primers.

Gene symbolIDForwardReverseProduct length (bp)
Akt1NM_009652 5′-CAGTTTGAGACCACACAT-3′ 5′-GCGTCAGTCCTTAATAGTT-3′75
Pdpk1NM_011062 5′-GGAATTGAATAGTGAGGTT-3′ 5′-AATGATGAATGTTGTATGTG-3′114
Pik3caNM_008839 5′-TGTGGCATCTGAGTATCT-3′ 5′-TGTGGCATCTGAGTATCT-3′132
Pik3r1NM_001077495 5′-ATTAAGGTTCTGTGGATTC-3′ 5′-GCTATGCTGTATCTATCTG-3′80
Pik3r3NM_181585 5′-ATTGGAATACCTGTGACT-3′ 5′-TGACTTACTTGGAAGAGAT-3′148
Slc2a4NM_009204 5′-TATGTTGCGGATGCTATG-3′ 5′-TTAGGAAGGTGAAGATGAAG-3′82

[i] Akt1, thymoma viral proto-oncogene 1; Pdpk1, 3-phosphoinositide dependent protein kinase 1; Pik3ca, phosphatidylinositol 3-kinase, catalytic, α polypeptide; Pik3r1, phosphatidylinositol 3-kinase, regulatory subunit, polypeptide (p85 α); Pik3r3, phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 3 (p55); Slc2a4, solute carrier family 2 member 4.

miRNA target genes pathway analysis

To fully clarify the biological functions of the target genes of differentially expressed miRNAs, Gene Ontology (GO) classifications and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined, using Database for Annotation, Visualization and Integrated Discovery software (http://david.abcc.ncifcrf. gov/) (22). Interactions between miRNAs and mRNAs were analyzed by Cytoscape (http://www.cytoscape.org) (23).

Cell culture and treatments

HepG2 human liver cancer cells (American Type Culture Collection, Manassas, VA, USA) were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 1% pencillin-strepomycin, 2 mM glutamine (all from Lonza Group, Ltd., Basel, Switzerland) and 25 mM HEPES (Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) in a humidified incubator containing 5% CO2 at 37°C. HepG2 cells (2×105 cells/ml) were transfected with miR-327, miR-223-3p, miR-446f-3p (mature sequences in Table III; 10 μM; Ambion; Thermo Fisher Scientific, Inc.) or negative control oligo-duplex (Ambion) using transfection reagent (Lipofectamine® RNAiMAX; Invitrogen; Thermo Fisher Scientific, Inc.) for 12 h at 37°C. After 24 h, cells were harvested for RNA preparation, and to measure the relative expression levels of miR-327, miR-223-3p, miR-446f-3p, thymoma viral proto-oncogene 1 (Akt1), 3-phosphoinositide dependent protein kinase 1 (Pdpk1), phosphatidylinositol 3-kinase, catalytic, α polypeptide (Pik3ca), phosphatidylinositol 3-kinase, regulatory subunit, polypeptide (Pik3r1), phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 3 (Pik3r3) and solute carrier family 2 member 4 [Slc2a4, also known as glucose transporter 4 (Glut4)].

Data statistical analysis

All numerical results are expressed as the means ± standard deviation of the indicated number of experiments. Student's t-test was used to compare unpaired samples, and One-way analysis of variance followed by post hoc Tukey test was used for multiple comparisons. GraphPad Prism software version 5.0 (GraphPad Software, Inc., La Jolla, CA, USA) was used to analyze the data. P<0.05 was considered to indicate a statistically significant difference.

Results

Dams

Serum Cr concentrations were lower in the LC group compared with in the CON group (0.36±0.03 vs. 0.74±0.14 ng/ml, P<0.01). Body weight, fasting blood glucose, blood glucose levels, AUC of glucose concentration during OGTT, serum insulin levels and HOMA-IR index in LC dams were similar to CON dams at the end of lactation (data not shown).

Offspring
Effect of maternal LC diet on serum Cr in offspring

At 16 weeks of age, serum Cr in the LC/LC (0.31±0.02 ng/ml) and CON/LC groups (0.27±0.01 ng/ml) was significantly reduced compared with in the CON/CON (0.69 ±0.03 ng/ml) and LC/CON groups (0.72±0.09 ng/ml, P<0.01).

Effects of maternal LC diet on body weight of offspring

At birth, 3 and 16 weeks of age, body weight of male pups was not markedly different among the various groups.

Effects of maternal LC diet on fasting blood glucose in offspring

At week 3, no significant differences in fasting blood glucose were noted among all groups. At week 16, fasting blood glucose in the CON/LC (8.32±0.72 mmol/l), LC/CON (8.19±0.92 mmol/l) and LC/LC groups (8.52±0.73 mmol/l) was significantly higher compared with in the CON/CON group (5.83±0.07 mmol/l, P<0.05).

Effects of maternal LC diet on glucose tolerance in offspring

To determine whether maternal LC diet disturbs glucose tolerance in offspring, an OGTT was conducted at 16 weeks of age. After a bolus of oral glucose, blood glucose levels were significantly increased in the offspring of the CON/LC, LC/LC and LC/CON groups compared with in the CON/CON group at 30, 60 and 120 min (P<0.05 or P<0.01). AUC of blood glucose concentration was increased in the CON/LC (51.43±4.38 mmol/l/h), LC/LC (53.18±4.83 mmol/l/h) and LC/CON groups (48.85±5.28 mmol/l/h) compared with in the CON/CON group (26.23±3.14 mmol/l/h, P<0.01).

Effects of maternal LC diet on serum insulin and HOMA-IR index in offspring

Serum fasting insulin levels were significantly increased in the CON/LC (11.8±1.84 μIU/ml) and LC/LC groups (13.2±1.8 μIU/ml) compared with in the CON/CON group (6.4±0.53 μIU/ml, P<0.05). HOMA-IR index in the LC/CON (3.8±0.37), CON/LC (3.1±0.24) and LC/LC groups (3.8±0.26) was higher than that in the CON/CON group (2.1±0.13, P<0.01) (data not shown).

miRNA array

Affymetrix miRNA 4.0 array contains all known mouse and rat miRNA annotated in the miRBase 20.0 sequence database. A volcano plot was generated to identify differential expression between the LC/CON and CON/CON groups, as shown in Fig. 2. The cluster and heat maps of all detected differentially expressed miRNAs were plotted to better demonstrate differential miRNA expression between the two groups (Fig. 3). The miRNA array yielded 14 significantly differentially expressed miRNAs (Table III). Among the 14 significantly differentially expressed miRNAs, 8 miRNAs were upregulated (mmu-miR-122-3p, mmu-miR-6238, mmu-miR-669a-3p, mmu-miR-691, mmu-miR-223-3p, mmu-miR-327, mmu-miR-207 and mmu-miR-466f-3p), and 6 miRNAs were downregulated (mmu-miR-30f, mmu-miR-344b-5p, mmu-miR-28b, mmu-miR-296-3p, mmu-miR-7062-3p and mmu-miR-3090-5p). Differentially expressed miRNAs were defined by a fold change ≥2, up- or downregulated and P<0.05.

qPCR

qPCR was used to confirm differential miRNA expression and to detect the target genes of differentially expressed miRNAs (Fig. 4). The differential expression of miRNAs was confirmed by RT-qPCR; the results were consistent with the array analysis (Fig. 4A).

Bioinformatics analysis

Based on the data from miRTar-Base databases, 8 of the 14 dysregulated miRNAs have 732 validated target genes (Table IV). The significantly over-represented biological process, cellular component and molecular function GO terms (P<0.001) in the differentially expressed miRNA targets are presented in Table V. In biological process, the GO terms were involved with transcription, regulation of transcription, negative regulation of gene expression, protein localization, regulation of neuron apoptosis, phosphate metabolic process and phosphorus metabolic process (Fig. 5). A pathway analysis was conducted using the 8 differentially expressed miRNA targets; pathway title and the P-value of the significant pathways (P<0.0001), are presented in Table VI. KEGG pathway analysis showed that the target genes of differential expression miRNAs in LC/CON mice were mainly focused on insulin signaling pathway (Fig. 6). miRNAs and their target interactions are presented in Fig. 7.

Table IV

Validated target genes of 8 differentially expressed miRNAsa.

Table IV

Validated target genes of 8 differentially expressed miRNAsa.

miRNARegulationTarget genes
mmu-miR-28bDownSlc7a2, Nr3c1, Timm17b, Timm17b, 2010109I03Rik, Tars2, Sl×1b, Tns4, Prss43, Pld5, Ccdc78, Tmppe
mmu-miR-296-3pDownCtla2a, Gna14, Igf1r, Dusp8, Tfam, Tnf, Cxcl11, Car5b, Nsa2, Clvs1, Phf14, Phf14, Braf, Mcmdc2, Prss43
mmu-miR-669a-3pUpCry2, Cd47, Kras, Shh, Zfp37, Rnf11, Ccdc82, 2610002M06Rik, Wdfy3, 1700019G17Rik, Fam159b, Cml2, Arrdc3, Sipa1l1, Csde1, Gm4944, Gm7616
mmu-miR-691UpCbln2, Cdk7, Inpp5d, Ndrg1, Sp4, Pcgf2, Scd3, Arl6ip1, Wtap, Atp2b1, 1700025G04Rik, Sik3, Dapl1, Adamtsl1, Utp23, Cnnm1, Hlcs
mmu-miR-223-3pUpAbca1, Slc33a1, Macf1, Ank3, Prdx6, Aplp2, Atp9a, Atrn, Pcdh15, Casp9, Cbx5, Cckbr, Cdkn1b, Celf1, Dag1, Ddn, Enc1, Eps15, Ercc3, F2r, F3, Fmn1, Srgap2, Fut9, Gabra6, Gabrg1, Gria2, Grin2b, Trip12, Gzmb, Sdc2, Igf1r, Itgb1, F11r, Kcnj10, Kcnj3, Kif1b, Klf12, Kpna1, Kras, Ktn1, Ldlr, Zbtb7a, Smad5, Mecp2, Mef2c, Mt2, Map1b, Myo1c, Napb, Nf1, Nfib, Ntrk2, Dusp8, Pdgfra, Enpp1, Pdpk1, Pea15a, Peg3, Pgr, Phka1, Pik3r3, Prkcb, Pkia, Plod1, Plxna2, Prkar2b, Prnp, Ptpru, Pvt1, Rest, Rgs2, Xpr1, Rtn3, Scd1, Scn8a, Sdpr, Sept8, Serf1, Sox6, Stat3, Syn1, Syp, Syt4, Tnfaip1, Utrn, Vim, Xpc, Ywhaz, Zfp26, Akt3, Apc2, Ets1, Gm16515, Gnpda1, Mapk10, B3galt1, B3galnt1, Syn3, Trp53bp1, Amot, Nrep, Ubfd1, Mga, Rnf11, Fbxw7, Tmod2, Mfhas1, Tmx4, Sgsm1, Pgrmc1, Stk39, Vti1a, Gria3, Rragc, Ptbp2, Ak3, Ptpn9, Dbn1, Socs5, Foxo3, Rnf14, Mbnl1, Jph3, Extl2, Fam13a, Usp14, Gprc5b, Txndc12, Tmem57, Cyb5b, Rnf220, Asf1b, Ddx17, Slc25a46, Polr3g, Gpatch2, Tmem33, Camsap2, Atp2b1, Tmem178, Ndnf, Ankra2, Pdrg1, 1190002N15Rik, Abhd13, Zfp467, Sec62, Ttc9, Zfp619, Afap1, Gcc2, Draxin, Spag9, Fmnl2, Ap2b1, Klhl5, March8, Pdia6, Antxr2, Zcchc24, Dusp11, Slc37a3, Fbxl20, Usp36, Ttc4, Mvb12b, Dnajc6, Tppp, Ccnt2, 3110043O21Rik, Gpbp1, 1700012B15Rik, Lonrf3, Arl5a, Rap2a, Car12, Paqr4, Clasp2, Usp42, Golga1, Atg16l1, Kidins220, Myh10, Yipf6, Trappc6b, Mvp, Acap2, Ankrd17, Cacng7, Smarcd2, Pard6g, Vangl2, Trim9, Gopc, Dgcr8, Cadm3, Pds5b, Tbc1d14, Akap9, Inpp5f, Bbs4, Dnm3, Arrdc3, Epdr1, Ypel1, Tm9sf3, Grm7, Foxp1, Dnajc9, Jmjd1c, Fam73b, Ank2, Zfp91, Shroom2, Hlcs, Derl2, Fam57a, Ube3b, Hpcal4, Mtmr4, Zfp704, Glcci1, Mlc1, Cd99l2, Prpf8, Phf21a, Mical3, C2cd2, Rsrc2, Cblb, Daam1, Cpeb3, Prrt3, Kbtbd2, Dennd6a, Kmt2a, Gnptg, L3mbtl2, Rhbdl1, Vezt, Tom1l2, Hlf, 4933426M11Rik, Pacs2, Rasa1, Srek1, Spata13, Xkr6, Pcdh17, Golgb1, Cep120, Fam78b, Cdc42bpa, Ino80d, Camk1d, Sestd1, Zmynd8, 4932438A13Rik, Tnrc18, Vopp1, Eva1a, Mob1a, Ppp4r2, Luzp2, Arglu1, Psd3, Atg4d, Ago1, Nxt2, Eml6, Usp32, Pnma2, Phf20l1, Mb21d2, Gpr158, Rbm45, Lrrc55, Tspyl3, Podn, Wasf2, Stard13, Kat6a, Mllt6, Lgi2, Lsamp, Robo2, Caskin1, Luzp1, Nceh1, Zbtb39, Mdga2, Zhx3, Pitpnm3, Fnip2, Tmem67, Slc5a6, Dock1, Ksr2, Pcdhac1, Nefh, Lonrf2, Rbm33, Znrf3, Adcy1, Bdp1, Tnik
mmu-miR-327UpClk4, Mef2c, Ptprb, Slc2a4, Cntnap2, Fam168b, Cep68, Evi2b, Zmym5, Rasal2, Rab39, Pld5
mmu-miR-207UpAdcy6, Cplx2, Emp2, Gda, Itgam, Lpl, Nab1, Nrl, Pigr, Prlr, Slc22a12, Ubtf, Txnl4a, Preb, Sh3bgrl, Rab3c, Ascl4, Prr5l, Steap2, Crebrf, BC021785, Plcl1, Ntm, Gm4944, Prkag3, Dzank1, Oacyl
mmu-miR-466f-3pUpAdam9, Adcyap1r1, Akap2, Ap1g1, Xiap, Arf3, Zfhx3, Atf2, Atp7a, Pcdh15, Chic1, Bsn, Chek1, Cnr1, Cpd, Cry2, Drp2, E2f1, Efnb2, Eif2s1, Dmtn, Epha4, Ereg, Eya1, Fech, Gnb4, Grb2, Grik3, Hist1h1d, Foxq1, Hiat1, Hk2, Id2, Ids, Il17ra, Inpp5d, Jag2, Kcna4, Klra2, Hivep3, Loxl3, Mef2a, Mmp12, Mmp11, Mtf1, Nck2, Nfatc2, Nodal, Ints6, Npy1r, Slc11a2, Nrf1, Nrg3, Nrp2, Pappa, Piga, Pik3ca, Pik3r1, Prkcb, Plau, Prrx1, Ppargc1a, Pter, Ptp4a2, Ptprk, Rai1, Rgl1, Msr1, Cxcl5, Sin3a, Slc12a2, Smarca4, Spta1, Tcf4, Tcf7l2, Tcte1, Tll1, Tlr4, Tnfrsf11a, Trhr, Unc5c, Uty, Vegfa, Wfs1, Xk, Pcgf2, Zbtb7b, Ikzf2, Gtf2h2, Dnajb6, Rnasel, Sept3, Taf7, Ldb3, Cops7b, Azi2, Sytl4, Csnk1e, Prl5a1, Vps26a, Fbxl17, Sept11, Hif3a, B3gnt2, Diap2, Usp27×, Pitpnb, Pias1, Gsk3b, Akap10, Plag1, Gmeb1, Tbx20, Piwil2, Usp29, Cd200r1, Pard6b, Fign, Ms4a4b, Wtap, Ms4a4c, Gpr85, Mrps24, Dimt1, Haus2, Ccdc82, Atg10, Slc16a9, Slc25a46, Klhl13, Zfp169, Nacc2, Pdf, Golt1a, Rraga, Fndc1, Anln, Synpo2l, Phtf2, Casz1, Sash1, Mllt3, Tbc1d13, Rufy2, Prss22, Pgm2l1, Foxn3, Tmem25, Smug1, Mtfr2, Nup93, Cyb5r1, Ccpg1, Slc16a10, Ints8, Zfp444, Tmem161b, Zfp839, Appl1, Insig2, Larp1, Ddit4l, Rap2b, Ehhadh, Ubap2l, Ifitm7, Mdga1, Uhrf1bp1l, Dusp18, Arhgap12, Fam227a, Dcaf17, Sl×1b, Ppp1r14c, Abca6, Rtf1, Fam101b, B3gat1, Bcas1, Crebrf, Csrnp3, Cdk19, Clca2, Tbl1xr1, Tfcp2l1, Tdrd1, Pde4dip, Tas1r3, Mcam, Spock2, Arid4b, Hecw1, Ubac1, Lrrc19, Zfand2a, Zfp518b, Fbxl14, Adamts9, Dennd4a, Slc6a8, Dnm3, Unc5a, Ssr1, Mylk, Ston2, Trmt10a, Fam212b, Prr5, Lmx1a, Scn2a1, Erc1, Slc28a3, Rasa2, Pofut1, Asb13, Bbc3, Edaradd, Rwdd4a, Ash1l, Wfdc12, Ell2, Bzrap1, Prrg3, Ido2, Mkx, Alkbh1, Fam46a, Gabpb2, Armc2, Arhgef11, Tnrc6b, Tet2, Fam168b, BC003965, Tmprss13, Phactr2, Kctd11, Spag7, Evi2b, Rnf157, Nploc4, Stxbp6, Cep170b, Gxylt1, Tbc1d24, Cep76, Plcl1, Rabgap1, Cytip, Atp5g3, Mavs, Hnrnpa3, Bcl2l15, Slc6a17, Strip1, Cdc14a, Tmem201, Lin54, Oas1e, BC030336, Fus, Tpte, Psd3, Ankle1, Fhod1, Stard8, Ofd1, Zc3h12d, C2cd4c, Ric8b, Btnl9, Pfas, Fam160b2, Apol8, Rtp1, 9930021J03Rik, Zbtb34, Zbtb6, Lzts3, Dzank1, Zfp462, Klhl21, Rsbn1l, Ugt2b35, Ppm1k, Lonrf1, Cwf19l2, Zscan4c, Tbx22, Klf8, Atxn7, Oas3, Srgap3, Alkbh5, Greb1, Fam107a, Ctif, Nbeal1, Ss18l1, Lhfpl4, Fat3, Bpifc, Trim12c, Fndc3a, Hif1an, Itpripl2, A630001G21Rik, Zbtb39, Adrbk2, Hook3, Rnf152, Hist4h4, Ubn2, Fam169a, Sycp2, Ccdc169, Arl4c, Xpnpep3, Slfn5, Gfod1, Mast4, Skint7, Zfp516, Acot11, Taok3, Opcml, Gpd1l, Jhdm1d, Lin28b, Gm1587, Tbc1d22b, Arl14epl, Gse1, Mtx3, Zfp874b, Gm13476, Ociad2, Tmem178b, Fam169b, Scrt2, Zscan4d, Mast3, Zfp872, Zfp963, Zfp827, I830012O16Rik, AK010878

a Validated target genes in miRTarBase database version 6.0. miR/miRNA, microRNA.

Table V

Gene Ontology terms significantly over-represented in the differentially expressed miRNA target genes in three separate domains (P<0.001).

Table V

Gene Ontology terms significantly over-represented in the differentially expressed miRNA target genes in three separate domains (P<0.001).

TermDE CountFold enrichmentP-valueOntology
Transcription921.4456 2.06×10−4Biological Process
Regulation of transcription1101.3753 2.80×10−4Biological Process
Negative regulation of gene expression302.0373 3.87×10−4Biological Process
Protein localization461.7009 5.17×10−4Biological Process
Regulation of neuron apoptosis113.8285 5.53×10−4Biological Process
Phosphate metabolic process511.6397 5.66×10−4Biological Process
Phosphorus metabolic process511.6397 5.66×10−4Biological Process
Transcription921.4456 2.06×10−4Biological Process
Plasma membrane1401.3912 9.85×10−6Cellular Component
Cell fraction411.9865 4.33×10−5Cellular Component
Plasma membrane part861.5208 5.55×10−5Cellular Component
Insoluble fraction372.0236 7.68×10−5Cellular Component
Membrane fraction362.0384 8.46×10−5Cellular Component
Synapse242.1726 7.18×10−4Cellular Component
Neuron projection202.3573 8.54×10−4Cellular Component
Cell junction311.9046 9.00×10−4Cellular Component
Clathrin-coated vesicle123.3320 9.14×10−4Cellular Component
GTPase regulator activity302.3798 2.57×10−5Molecular Function
Nucleoside-triphosphatase regulator activity302.3409 3.49×10−5Molecular Function
Transition metal ion binding1261.3835 5.71×10−5Molecular Function
Metal ion binding1731.2868 6.86×10−5Molecular Function
Ion binding1761.2812 7.24×10−5Molecular Function
Cation binding1731.2752 1.15×10−4Molecular Function
GTPase activator activity192.8339 1.33×10−4Molecular Function
Protein tyrosine phosphatase activity133.6860 1.99×10−4Molecular Function
Zinc ion binding1031.4012 2.31×10−4Molecular Function
Cytoskeletal protein binding302.0752 2.87×10−4Molecular Function
DNA binding891.4310 3.63×10−4Molecular Function

[i] DE count, differential expression count.

Table VI

Significant pathways of the dysregulated miRNA target genes (P<0.0001).

Table VI

Significant pathways of the dysregulated miRNA target genes (P<0.0001).

PathwayCountFold enrichmentP-value
Endometrial cancer137.5104 7.94×10−8
Colorectal cancer155.2398 6.60×10−7
ErbB signaling pathway155.1796 7.64×10−7
Non-small cell lung cancer126.6759 1.08×10−6
Prostate cancer155.0069 1.17×10−6
Insulin signaling pathway183.9185 2.41×10−6
Glioma115.1634 3.99×10−5
miRNA target genes qPCR

Akt1, Pdpk1, Pik3ca, Pik3r1, Pik3r3 and Slc2a4 (Glut4) were downregulated in the LC/CON group compared with in the CON/CON group, as demonstrated by RT-qPCR (Fig. 4B).

miR-327, miR-223-3p and miR-466f-3p target the insulin signaling pathway in HepG2 cells

To determine whether miR-327, miR-223-3p and miR-466f-3p negatively regulate the insulin signaling pathway in vitro, HepG2 cells were transfected with miR-327, miR-223-3p and miR-466f-3p mimics. Slc2a4 expression was significantly downregulated in miR-327-transfected HepG2 cells (P<0.01; Fig. 8A). In miR-466f-3p-transfected HepG2 cells, Pik3ca and Pik3r1 were significantly downregulated (P<0.01; Fig. 8B). In miR-223-3p-transfected HepG2 cells, Akt1, Pdpk1 and Pik3r3 were significantly downregulated (P<0.01; Fig. 8C).

Discussion

In the present study, a 13-week LC diet led to impaired fasting blood glucose and glucose tolerance, and IR in male mice. Previous studies revealed the key role of Cr in regulating blood glucose (24,25). In addition, Cr supplementation has been reported to improve glucose metabolism in diabetic rats and patients (2628).

Notably, the present study demonstrated that male offspring from dams fed a LC diet had comparable birth weight to the CON group up to 16 weeks. A maternal LC diet did not change fasting blood glucose levels in offspring at 3 weeks of age; however, offspring from dams fed a LC diet exhibited impaired fasting blood glucose and glucose tolerance at 16 weeks of age. Mice in the LC/CON group had normal serum Cr levels; however, this could not correct the glucose intolerance. Padmavathi et al indicated that maternal Cr restriction increased fasting plasma glucose (from 9 months) and the AUC of glucose concentration during OGTT (from 15 months) in the offspring (9). In addition, growth-restricted rats exhibited glucose intolerance from 6 months of age (29). Pups from magnesium-restricted dams, which were shifted to a control diet, exhibited glucose intolerance at 180 days of age (30). A low-protein diet increased fasting blood glucose in offspring at 3 months (31). The results of the present study detected glucose intolerance in offspring earlier than Padmavathi et al (9), this may be partly because the Cr-restricted diet used in the present study was 88% Cr restriction compared with 67% Cr restriction.

The present study also demonstrated that serum insulin levels and HOMA-IR index were markedly increased in the offspring of dams fed a LC diet at 16 weeks of age. Padmavathi et al reported that maternal Cr restriction increased fasting insulin (from 15 months) and HOMA-IR index (from 9 months), whereas rehabilitation could not correct this effect (9). In a previous study, pups from pregnant rats fed a calcium-deficient diet exhibited increased insulin levels and HOMA-IR index at day 200 (32). Furthermore, at 12 months of age, offspring from vitamin B12-restricted dams had higher fasting blood insulin levels and HOMA-IR index (31).

Using a miRNA array and qPCR, the present study indicated that a maternal LC diet downregulated miR-334b-5p expression in the liver of male offspring. Zeng et al revealed that miR-344-3p was downregulated in islets from diabetic GK rats (33). Furthermore, in the present study, a maternal LC diet downregulated miR-296-3p expression in the liver of male offspring. In the pancreatic β-cell line, MIN6, miR-296 was significantly downregulated by high glucose treatment (34). In addition, a maternal LC diet upregulated miR-122-3p expression in the liver of male offspring; miR-122 is highly expressed in the liver and is highly conserved in sequence (35). Serum miR-122 levels are markedly increased in newly diagnosed diabetic cats (36). A maternal LC diet also upregulated miR-327 expression in the liver of male offspring. miR-327 has been revealed to be upregulated in myocardial microvascular endothelial cells from GK rats compared with Wistar rats (36).

In the present study, further bioinformatics analysis aided in the interpretation of the biological function of the differentially expressed miRNAs in glucose metabolism regulation. According to the KEGG pathway enrichment analysis, the significantly differentially expressed miRNAs (miR-327, miR-223-3p and miR-466f-3p) in the LC/CON group served important roles in the insulin signaling pathway.

In the insulin signaling pathway, Pik3r1 and Pik3ca are target genes of miR-466f-3p (3739). In addition, Akt1 and Pik3r3 are target genes of miR-223-3p (40). One target of miR-327 is Slc2a4 (Glut4) (39). In a previous study, Cr malate improved Glut4 and Akt levels in the liver tissues of T2D rats (26). In C2C12 skeletal muscle cells, oligomannuronate Cr complexes may enhance GLUT4 and the phosphatidylinositol 3-kinase (PI3K)/AKT signaling pathway (25). Therefore, it may be hypothesized that a maternal LC diet upregulates miR-327, miR-466f-3p and miR-223-3p expression, thus inhibiting insulin signaling (Fig. 9).

The PI3K/AKT signaling pathway is a classical pathway that regulates insulin in glucose metabolism. This pathway serves a role in glucose uptake by the liver, skeletal muscles and adipose tissues (41,42). The insulin receptor signaling pathway contributes to the development of diabetes. The IRS-1/PI3K/Akt axis has an important role in the insulin receptor signaling pathway through cascade phosphorylation (43,44). Inhibiting or blocking this pathway may reduce the physiological effects of insulin, which lead to IR. Ye et al demonstrated that hepatocytes in intrauterine growth retardation rats with catch-up growth exhibited decreased IRS1 and PI3K expression (45). In addition, the PI3K p110β subunit, Akt1 proteins and phosphorylated-Akt Ser473 are reduced in the offspring of low protein-fed dams (46). A previous study also reported that maternal protein restriction leads a decrease in Akt1 (Ser473) phosphorylation in the livers of offspring at postnatal day 130 (47). Catch-up growth following intrauterine growth restriction reduces the expression of the PI3K p110β subunit at 22 days old, and Akt phosphorylation and Akt-2 protein levels at 3 months in adipose tissue (48). In addition, maternal diabetes has been revealed to decrease GLUT4 in the adipose and muscle tissues of 10-week-old male offspring (49).

In conclusion, the present study confirmed that a maternal LC diet caused glucose intolerance and IR in male offspring. The present study is the first, to the best of our knowledge, to demonstrate that a maternal LC diet induces miRNA dysfunction in the liver samples of offspring. In particular, a maternal LC diet may upregulate the expression of miR-327, miR-466f-3p and miR-223-3p, thus inhibiting the insulin signaling pathway in male offspring. In the future, scientists should aim to identify interventions for maternal Cr restriction to affect glucose metabolism in offspring. This treatment will benefit mothers and the next generation.

Glossary

Abbreviations

Abbreviations:

Akt1

thymoma viral proto-oncogene 1

AUC

area under the curve

CON

control

Cr

chromium

GK

Goto-Kakizaki

Glut4

glucose transporter 4

GO

Gene Ontology

HOMA-IR

homeostasis model assessment of insulin resistance

IR

insulin resistance

IRS1

insulin receptor substrate 1

KEGG

Kyoto Encyclopedia of Genes and Genomes

LC

low chromium

miRNA

microRNA

NC

negative control

OGTT

oral glucose tolerance test

Pdpk1

3-phosphoinositide dependent protein kinase 1

Pik3ca

phosphatidylinositol 3-kinase, catalytic, α polypeptide

Pik3r1

phosphatidylinositol 3-kinase, regulatory subunit, polypeptide

Pik3r3

phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 3

PKB

protein kinase B

Slc2a4

solute carrier family 2 member 4

T2D

type 2 diabetes

Acknowledgments

The present study was supported by the National Natural Science Foundation of China (grant nos. 81170736 and 81570715), the National Natural Science Foundation for Young Scholars of China (grant no. 81300649), the China Scholarship Council foundation (grant no. 201308110443), the PUMC Youth Fund (grant no. 33320140022), the Fundamental Research Funds for the Central Universities, and the Scientific Activities Foundation for Selected Returned Overseas Professionals of Human Resources and Social Security Ministry. The authors would like to thank Beijing Compass Biotechnology Company for technical assistance with the miRNA microarray experiments.

The normalized data and raw 'cel' files from the miRNA array are available at the NCBI GEO website under the series record GSE82026 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=upsxikamhpgvbmn&acc=GSE82026; private website only for reviewers).

References

1 

Diabetes Atlas IDF. 7th edition. International Diabetes Federation; 2015

2 

Hales CN, Barker DJ, Clark PM, Cox LJ, Fall C, Osmond C and Winter PD: Fetal and infant growth and impaired glucose tolerance at age 64. BMJ. 303:1019–1022. 1991. View Article : Google Scholar : PubMed/NCBI

3 

Hales CN and Barker DJ: Type 2 (non-insulin-dependent) diabetes mellitus: The thrifty phenotype hypothesis. Diabetologia. 35:595–601. 1992. View Article : Google Scholar : PubMed/NCBI

4 

de Rooij SR, Painter RC, Phillips DI, Osmond C, Michels RP, Godsland IF, Bossuyt PM, Bleker OP and Roseboom TJ: Impaired insulin secretion after prenatal exposure to the Dutch famine. Diabetes Care. 29:1897–1901. 2006. View Article : Google Scholar : PubMed/NCBI

5 

Food and Nutrition Board Recommended Dietary Allowances. National Research Council, National Academy of Sciences; Washington DC: 2000

6 

Jeejeebhoy KN, Chu RC, Marliss EB, Greenberg GR and Bruce-Robertson A: Chromium deficiency, glucose intolerance, and neuropathy reversed by chromium supplementation, in a patient receiving long-term total parenteral nutrition. Am J Clin Nutr. 30:531–538. 1977. View Article : Google Scholar : PubMed/NCBI

7 

Anderson RA: Chromium, glucose intolerance and diabetes. J Am Coll Nutr. 17:548–555. 1998. View Article : Google Scholar : PubMed/NCBI

8 

Freund H, Atamian S and Fischer JE: Chromium deficiency during total parenteral nutrition. JAMA. 241:496–498. 1979. View Article : Google Scholar : PubMed/NCBI

9 

Padmavathi IJ, Rao KR and Raghunath M: Impact of maternal chromium restriction on glucose tolerance, plasma insulin and oxidative stress in WNIN rat offspring. J Mol Endocrinol. 47:261–271. 2011. View Article : Google Scholar : PubMed/NCBI

10 

Waterland RA and Jirtle RL: Early nutrition, epigenetic changes at transposons and imprinted genes, and enhanced susceptibility to adult chronic diseases. Nutrition. 20:63–68. 2004. View Article : Google Scholar

11 

Zhang Q, Xiao XH, Zheng J, Li M, Yu M, Ping F, Wang Z, Qi C, Wang T and Wang X: Maternal chromium restriction modulates miRNA profiles related to lipid metabolism disorder in mice offspring. Exp Biol Med (Maywood). 242:1444–1452. 2017. View Article : Google Scholar

12 

Herrera BM, Lockstone HE, Taylor JM, Wills QF, Kaisaki PJ, Barrett A, Camps C, Fernandez C, Ragoussis J, Gauguier D, et al: MicroRNA-125a is over-expressed in insulin target tissues in a spontaneous rat model of Type 2 Diabetes. BMC Med Genomics. 2:542009. View Article : Google Scholar : PubMed/NCBI

13 

Jordan SD, Krüger M, Willmes DM, Redemann N, Wunderlich FT, Brönneke HS, Merkwirth C, Kashkar H, Olkkonen VM, Böttger T, et al: Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nat Cell Biol. 13:434–446. 2011. View Article : Google Scholar : PubMed/NCBI

14 

Zhou B, Li C, Qi W, Zhang Y, Zhang F, Wu JX, Hu YN, Wu DM, Liu Y, Yan TT, et al: Downregulation of miR-181a upregulates sirtuin-1 (SIRT1) and improves hepatic insulin sensitivity. Diabetologia. 55:2032–2043. 2012. View Article : Google Scholar : PubMed/NCBI

15 

Ryu HS, Park SY, Ma D, Zhang J and Lee W: The induction of microRNA targeting IRS-1 is involved in the development of insulin resistance under conditions of mitochondrial dysfunction in hepatocytes. PLoS One. 6:e173432011. View Article : Google Scholar : PubMed/NCBI

16 

Jeong HJ, Park SY, Yang WM and Lee W: The induction of miR-96 by mitochondrial dysfunction causes impaired glycogen synthesis through translational repression of IRS-1 in SK-Hep1 cells. Biochem Biophys Res Commun. 434:503–508. 2013. View Article : Google Scholar : PubMed/NCBI

17 

Dou L, Zhao T, Wang L, Huang X, Jiao J, Gao D, Zhang H, Shen T, Man Y, Wang S, et al: miR-200s contribute to interleukin-6 (IL-6)-induced insulin resistance in hepatocytes. J Biol Chem. 288:22596–22606. 2013. View Article : Google Scholar : PubMed/NCBI

18 

National Research Council Committee for the Update of the Guide for the C and Use of Laboratory A: The National Academies Collection: Reports funded by National Institutes of Health Guide for the Care and Use of Laboratory Animals. 8th (ed). National Academies Press (US) National Academy of Sciences; Washington (DC): 2011

19 

Yokomizo H, Inoguchi T, Sonoda N, Sakaki Y, Maeda Y, Inoue T, Hirata E, Takei R, Ikeda N, Fujii M, et al: Maternal high-fat diet induces insulin resistance and deterioration of pancreatic β-cell function in adult offspring with sex differences in mice. Am J Physiol Endocrinol Metab. 306:E1163–E1175. 2014. View Article : Google Scholar : PubMed/NCBI

20 

Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, et al: miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res. 44:D239–D247. 2016. View Article : Google Scholar :

21 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 25:402–408. 2001. View Article : Google Scholar

22 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:32003. View Article : Google Scholar

23 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

24 

Król E and Krejpcio Z: Chromium(III) propionate complex supplementation improves carbohydrate metabolism in insulin-resistance rat model. Food Chem Toxicol. 48:2791–2796. 2010. View Article : Google Scholar : PubMed/NCBI

25 

Hao C, Hao J, Wang W, Han Z, Li G, Zhang L, Zhao X and Yu G: Insulin sensitizing effects of oligomannuronate-chromium (III) complexes in C2C12 skeletal muscle cells. PLoS One. 6:e245982011. View Article : Google Scholar : PubMed/NCBI

26 

Feng W, Zhao T, Mao G, Wang W, Feng Y, Li F, Zheng D, Wu H, Jin D, Yang L, et al: Type 2 diabetic rats on diet supplemented with chromium malate show improved glycometabolism, glycometabolism-related enzyme levels and lipid metabolism. PLoS One. 10:e01259522015. View Article : Google Scholar : PubMed/NCBI

27 

Feng W, Mao G, Li Q, Wang W, Chen Y, Zhao T, Li F, Zou Y, Wu H, Yang L, et al: Effects of chromium malate on glycometabolism, glycometabolism-related enzyme levels and lipid metabolism in type 2 diabetic rats: A dose-response and curative effects study. J Diabetes Investig. 6:396–407. 2015. View Article : Google Scholar : PubMed/NCBI

28 

Racek J, Sindberg CD, Moesgaard S, Mainz J, Fabry J, Müller L and Rácová K: Effect of chromium-enriched yeast on fasting plasma glucose, glycated haemoglobin and serum lipid levels in patients with type 2 diabetes mellitus treated with insulin. Biol Trace Elem Res. 155:1–4. 2013. View Article : Google Scholar : PubMed/NCBI

29 

Intapad S, Dasinger JH, Brown AD, Fahling JM, Esters J and Alexander BT: Glucose intolerance develops prior to increased adiposity and accelerated cessation of estrous cyclicity in female growth-restricted rats. Pediatr Res. 79:962–970. 2016. View Article : Google Scholar : PubMed/NCBI

30 

Venu L, Kishore YD and Raghunath M: Maternal and perinatal magnesium restriction predisposes rat pups to insulin resistance and glucose intolerance. J Nutr. 135:1353–1358. 2005. View Article : Google Scholar : PubMed/NCBI

31 

Bringhenti I, Schultz A, Rachid T, Bomfim MA, Mandarim- de-Lacerda CA and Aguila MB: An early fish oil-enriched diet reverses biochemical, liver and adipose tissue alterations in male offspring from maternal protein restriction in mice. J Nutr Biochem. 22:1009–1014. 2011. View Article : Google Scholar

32 

Takaya J, Yamanouchi S and Kaneko K: A calcium-deficient diet in rat dams during gestation and nursing affects hepatic 11β-hydroxysteroid dehydrogenase-1 expression in the offspring. PLoS One. 9:e841252014. View Article : Google Scholar

33 

Zeng LQ, Wei SB, Sun YM, Qin WY, Cheng J, Mitchelson K and Xie L: Systematic profiling of mRNA and miRNA expression in the pancreatic islets of spontaneously diabetic Goto-Kakizaki rats. Mol Med Rep. 11:67–74. 2015. View Article : Google Scholar

34 

Tang X, Muniappan L, Tang G and Ozcan S: Identification of glucose-regulated miRNAs from pancreatic {beta} cells reveals a role for miR-30d in insulin transcription. RNA. 15:287–293. 2009. View Article : Google Scholar :

35 

Tsai WC, Hsu SD, Hsu CS, Lai TC, Chen SJ, Shen R, Huang Y, Chen HC, Lee CH, Tsai TF, et al: MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis. J Clin Invest. 122:2884–2897. 2012. View Article : Google Scholar : PubMed/NCBI

36 

Fleischhacker SN, Bauersachs S, Wehner A, Hartmann K and Weber K: Differential expression of circulating microRNAs in diabetic and healthy lean cats. Vet J. 197:688–693. 2013. View Article : Google Scholar : PubMed/NCBI

37 

Zhang X, Zuo X, Yang B, Li Z, Xue Y, Zhou Y, Huang J, Zhao X, Zhou J, Yan Y, et al: MicroRNA directly enhances mitochondrial translation during muscle differentiation. Cell. 158:607–619. 2014. View Article : Google Scholar : PubMed/NCBI

38 

Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS and Rudensky AY: Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol Cell. 48:760–770. 2012. View Article : Google Scholar : PubMed/NCBI

39 

Leung AK, Young AG, Bhutkar A, Zheng GX, Bosson AD, Nielsen CB and Sharp PA: Genome-wide identification of Ago2 binding sites from mouse embryonic stem cells with and without mature microRNAs. Nat Struct Mol Biol. 18:237–244. 2011. View Article : Google Scholar : PubMed/NCBI

40 

Chi SW, Zang JB, Mele A and Darnell RB: Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 460:479–486. 2009. View Article : Google Scholar : PubMed/NCBI

41 

Cantley LC: The phosphoinositide 3-kinase pathway. Science. 296:1655–1657. 2002. View Article : Google Scholar : PubMed/NCBI

42 

Cusi K, Maezono K, Osman A, Pendergrass M, Patti ME, Pratipanawatr T, DeFronzo RA, Kahn CR and Mandarino LJ: Insulin resistance differentially affects the PI 3-kinase- and MAP kinase-mediated signaling in human muscle. J Clin Invest. 105:311–320. 2000. View Article : Google Scholar : PubMed/NCBI

43 

Aguirre V, Werner ED, Giraud J, Lee YH, Shoelson SE and White MF: Phosphorylation of Ser307 in insulin receptor substrate-1 blocks interactions with the insulin receptor and inhibits insulin action. J Biol Chem. 277:1531–1537. 2002. View Article : Google Scholar

44 

Yamauchi T, Kaburagi Y, Ueki K, Tsuji Y, Stark GR, Kerr IM, Tsushima T, Akanuma Y, Komuro I, Tobe K, et al: Growth hormone and prolactin stimulate tyrosine phosphorylation of insulin receptor substrate-1, -2, and -3, their association with p85 phosphatidylinositol 3-kinase (PI3-kinase), and concomitantly PI3-kinase activation via JAK2 kinase. J Biol Chem. 273:15719–15726. 1998. View Article : Google Scholar : PubMed/NCBI

45 

Ye J, Zheng R, Wang Q, Liao L, Ying Y, Lu H, Cianflone K, Ning Q and Luo X: Downregulating SOCS3 with siRNA ameliorates insulin signaling and glucose metabolism in hepatocytes of IUGR rats with catch-up growth. Pediatr Res. 72:550–559. 2012. View Article : Google Scholar : PubMed/NCBI

46 

Martin-Gronert MS, Fernandez-Twinn DS, Bushell M, Siddle K and Ozanne SE: Cell-autonomous programming of rat adipose tissue insulin signalling proteins by maternal nutrition. Diabetologia. 59:1266–1275. 2016. View Article : Google Scholar : PubMed/NCBI

47 

Sohi G, Revesz A and Hardy DB: Nutritional mismatch in postnatal life of low birth weight rat offspring leads to increased phosphorylation of hepatic eukaryotic initiation factor 2α in adulthood. Metabolism. 62:1367–1374. 2013. View Article : Google Scholar : PubMed/NCBI

48 

Berends LM, Fernandez-Twinn DS, Martin-Gronert MS, Cripps RL and Ozanne SE: Catch-up growth following intra-uterine growth-restriction programmes an insulin-resistant phenotype in adipose tissue. Int J Obes. 37:1051–1057. 2013. View Article : Google Scholar

49 

Kamel MA, Helmy MH, Hanafi MY, Mahmoud SA and Abo Elfetooh H: Impaired peripheral glucose sensing in F1 offspring of diabetic pregnancy. J Physiol Biochem. 70:685–699. 2014. View Article : Google Scholar : PubMed/NCBI

50 

Reeves PG: Components of the AIN-93 Diets as improvements in the AIN-96A diet. J Nutr. 127:838S–841S. 1997.

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March 2018
Volume 41 Issue 3

Print ISSN: 1107-3756
Online ISSN:1791-244X

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APA
Zhang, Q., Sun, X., Xiao, X., Zheng, J., Li, M., Yu, M. ... Wang, X. (2018). Maternal chromium restriction induces insulin resistance in adult mice offspring through miRNA. International Journal of Molecular Medicine, 41, 1547-1559. https://doi.org/10.3892/ijmm.2017.3328
MLA
Zhang, Q., Sun, X., Xiao, X., Zheng, J., Li, M., Yu, M., Ping, F., Wang, Z., Qi, C., Wang, T., Wang, X."Maternal chromium restriction induces insulin resistance in adult mice offspring through miRNA". International Journal of Molecular Medicine 41.3 (2018): 1547-1559.
Chicago
Zhang, Q., Sun, X., Xiao, X., Zheng, J., Li, M., Yu, M., Ping, F., Wang, Z., Qi, C., Wang, T., Wang, X."Maternal chromium restriction induces insulin resistance in adult mice offspring through miRNA". International Journal of Molecular Medicine 41, no. 3 (2018): 1547-1559. https://doi.org/10.3892/ijmm.2017.3328