Open Access

Maternal high fat intake affects the development and transcriptional profile of fetal intestine in late gestation using pig model

  • Lianqiang Che1, 2Email author,
  • Peilin Liu1, 2,
  • Zhengguo Yang1, 2,
  • Long Che1, 2,
  • Liang Hu1, 2,
  • Linlin Qin1, 2,
  • Ru Wang1, 2,
  • Zhengfeng Fang1, 2,
  • Yan Lin1, 2,
  • Shengyu Xu1, 2,
  • Bin Feng1, 2,
  • Jian Li1, 2 and
  • De Wu1, 2
Lipids in Health and Disease201615:90

https://doi.org/10.1186/s12944-016-0261-0

Received: 21 February 2016

Accepted: 3 May 2016

Published: 10 May 2016

Abstract

Background

The objective of this study was to investigate the effects of maternal high fat intake on intestinal development and transcriptional profile.

Methods

Eight gilts with similar age and body weight were randomly allocated into 2 groups receiving the control and high fat diets (HF diet) from d 30 to 90 of gestation, with 4 gilts each group and one gilt each pen. At d 90 of gestation, two fetuses each gilt were removed by cesarean section. Intestinal samples were collected for analysis of morphology, enzyme activities and transcriptional profile.

Results

The results showed that feeding HF diet markedly increased the fetal weight and lactase activity, also tended to increase intestinal morphology. Porcine Oligo Microarray analysis indicated that feeding HF diet inhibited 64 % of genes (39 genes down-regulated while 22 genes up-regulated),which were related to immune response, cancer and metabolism, also markedly modified 33 signal pathways such as antigen processing and presentation, intestinal immune network for IgA production, Jak-STAT and TGF-ß signaling transductions, pathways in colorectal cancer and glycerolipid metabolism.

Conclusion

Collectively, it could be concluded that maternal high fat intake was able to increase fetal weight and lactase activity, however, it altered the intestinal immune response, signal transduction and metabolism.

Keywords

Maternal nutrition Offspring Immune Cancer DNA microarray

Background

Gastrointestinal tract (GIT), as an internal organ to digest nutrients and resist exogenous antigens, starts to develop at early gestation and mature rapidly in late gestation for extra-uterine life [1]. The functional maturation of GIT occurs in both pre- and postnatal period, which is largely influenced by maternal nutrition [2]. Maternal diet has been shown to affect the fetal development and organ function in mammalian animals [3]. Our recent study also suggests that maternal nutrition levels could affect the intestinal development and function, in which maternal over-nutrition would improve intestinal morphology, enzyme activities and gene expressions of nutrient transporters in newborn pigs [4]. However, it has been reported that maternal high-fat intake or –related obesity could impair gut barrier, enhance gene expression of pro-inflammatory cytokines in offspring intestine, thus predisposes offspring to inflammatory bowel disease [5, 6]. However, the underlying mechanism for the effects of maternal high fat intake on the intestinal development and function are limited. The current study was designed to investigate the effects of maternal high fat intake on fetal intestinal development and function by measuring parameters on morphology, enzyme activities and transcriptional profiles. Oligo Microarray was used to analyze the genomic response of fetal intestine to maternal high fat intake. Pigs were chosen as the experimental animal, because it is generally accepted to be closer to humans than other laboratory or domestic animals in terms of gastrointestinal anatomy, physiology, nutrition and microbiota [2, 79].

Methods

The experimental procedure was approved by the University of Sichuan Agricultural Animal Care Advisory committee, and followed the current law of animal protection.

Animals and diets

A total of 8 Meishan (MS) gilts (aged at 266 ± 15 d, initial body weight at 73 ± 4 kg) were used in this study. After inseminated with MS semen, eight gilts were randomly allocated to receive control diet (CON diet with 14 % Protein, 34.7 % Starch and 2.8 % Fat) and high fat diet (HF diet with 14 % Protein, 34.7 % Starch and 7.3 % Fat), respectively. The 4.5 % of soy oil was added into CON diet to formulate HF diet, as a result, HF diet contained digestive energy (DE) at 3.0 Mcal/kg, while CON diet contained DE at 2.6 Mcal/kg. According to the fatty acids contents of feed ingredients by NRC (2012), the contents of saturated, mono- and polyunsaturated fatty acids were 0.25 %, 0.48 %, 0.83 % in CON diet and 0.84 %, 1.78 %, 3.44 % in HF diet, respectively. The other nutrient levels were similar between 2 diets, meeting or exceeding nutrient requirements recommended by NRC (2012). All gilts were housed individually in stall (2.5 m length × 1.6 m width), receiving the same amount of diets at 2.0 kg from d 1 to 30 of gestation and 2.5 kg from d 30 to 90 of gestation, with free access to water. Environmental temperature was maintained at approximately 24 °C during the experiment.

Sample collection

At d 90 of gestation, gilts were weighed (in average 128 kg at HF vs. 117 kg at CON group) and anaesthetized by intramuscularly injecting Zoletil 50 at the dose of 0.1 mg/kg (Virbac, France), then the uterus were removed from gilts. Two fetuses near the average fetal weight were collected each gilt. As the previous study, duodenal, jejunal and ileal samples (approximately 2 cm) were preserved in 4 % paraformaldehyde solution, then embedded in paraffin. Each tissue sample of duodenum, jejunum and ileum was used to prepare 5 slides, each slide had three sections (5 mm thickness), which were stained with eosin and haematoxylin, 20 well-oriented villi and crypts each section were measured for morphology (Optimus software version 6.5, Media Cybergenetics, North Reading, MA, USA), and villous height to crypt depth ratio (VCR) was calculated [10]. A section of duodenum, jejunum and ileum tissues were collected and snap-frozen in liquid nitrogen, then stored at −80 °C for analysis of enzyme activities, RNA microarray and gene expression.

Enzyme activities

According to the previous study, the thawing samples of jejunum and ileum were weighed (approximately 2 g), then 9 times volume of 50 mM Tris–HCl buffer (pH 7 · 0) than the sample weight were added and homogenized for 40 s by homogenate machine (Homogenizer Power Gen 125™, ThermoFisher Scientific, MA, USA) and centrifuged at 3000 g for 10 min, the supernatant was collected and stored at −20 °C [11]. Total protein was extracted from the supernatant and protein concentration was determined by bicinchoninic acid protein assay with bovine serum albumin as the standard (Solarbio, Inc., Beijing, China). Activities of disaccharidase including maltase, sucrase and lactase were measured using commercial kits (Nanjing Jiancheng Bioengineering, Nanjing, China). The absorbance at 450 nm was determined with spectrophotometer (Beckman Coulter DU-800; Beckman Coulter, Inc., CA, USA). Activities of disaccharidase were presented as U/mg protein. One unit (U) was defined by 1 nmol of maltose, sucrose and lactose as a substrate for the enzymatic reaction, respectively.

RNA extraction

The frozen ileum tissues were used for RNA extraction, 4 sections around luminal circle each tissue were collected and pooled for RNA extraction. Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA) and quantified using spectrophotometry based on absorbance at 260 nm, the RNA quality was monitored using Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). The equal amount of RNA from 2 fetus each gilt were pooled together.

Porcine oligo microarray

As in our previous study, Agilent Porcine Oligo Microarray (4 × 44 K) containing more than 40,000 probes were used [12]. Cyanine-3 (Cy3)-labeled cRNA was prepared from 0.5 μg RNA using the One-Color Low RNA Input Linear Amplification PLUS kit (Agilent Technologies,Palo Alto, CA, USA) according to the manufacturer’s instructions, and followed by the RNeasy column purification (Qiagen, Valencia, CA, USA). Dye incorporation and cRNA yield were checked with the NanoDrop ND-1000 Spectrophotometer. Microarrays were hybridized at 65 °C for 17 h and washed with a Gene Expression Washing Buffer Kit (Agilent Technologies, Palo Alto, CA, USA). Slides were scanned with an Agilent microarray scanner.

Microarray data collection and analysis

Microarray data were collected and analyzed using Agilent G2567AA Feature Extraction software, following Agilent’s direct labeling protocol. The quantile method was used to normalize the probe intensities across the whole set of arrays. Three criteria were used to determine statistically significant differential expression of intestinal genes between fetus from CON and HF gilts: 1) statistical significance: P value as determined by t-test < 0.05; 2) reliability: a spot quality flag P (“P,” a quality flag assigned by the software package); 3) relevance: a minimal fold change between the means of the 2 groups >1.5.

Real-time PCR

In order to verify the microarray data, RNA samples used for porcine oligo microarray were applied to the quantitative real-time PCR (qPCR), which was performed in duplicate to amplify the target and reference genes, using one step SYBR Prime-Script™ RT-PCR kit II (Catalog no. DRR086A, Takara, Japan) by Real-Time PCR (ABI 7900HT, Applied Biosystems, CA, USA). The sequences of primers and length of products were shown at Table 1. The reaction mixture (10.0 μL) contained 5.6 μL of freshly pre-mixed one step SYBR Green Real-Time PCR Master mix and Prime Script™ Enzyme Mix, 0.8 μmol/L of the primers, and 100 ng of RNA template. The qPCR program was designed with one cycle of 42 °C for 5 min, one cycle of 95 °C for 10 s, 40 cycles of 95 °C for 5 s and 60 °C for 34 s, followed by the dissociation step at 95 °C for 15 s, 60 °C for 60 s and 95 °C for 15 s. At the end of amplification, melting curve analysis was performed to identify amplification specificity. Amplification of ß-actin was used to normalize gene expression through the double standard curves method [11].
Table 1

Primer sequences of genes selected for analysis by real-time RT-PCR

Genes

GenBank accession

Primer sequence (5′ ~ 3′)

Product length (bp)

Tm (°C)

HSPA1L

NM_001123128.1

F:CGCTTTGACCTGACTGGAAT

120

60

R:CTTGCCTGTGCTCTTGTCC

CD8A

NM_001001907.1

F:GCTGGACACCCGTTACATCT

100

60

R:CGAGCAGAAATAGTAGCCTTGG

CD40

NM_214194.1

F:GGTTCGTCTGCCTCTGAAGT

104

60

R:GGCTGTTTGTTGGGTATTGG

PSTPIP1

NM_001244186.1

F:CTCCTTTGACTCCCTGAAGC

114

60

R:TTCTGCCTCTCTCGGAACTC

SLA-DQA1

NM_001114062.2

F:TGGACCTGGAGAAGAAGGAG

132

60

R:TGGAGCGTTTAGTCACGATG

STAT2

NM_213889.1

F:TCCCAAATCACAAGGTTTCC

109

60

R:CAGATAGCCGAAGTCCCAAA

GK

NM_001143708.1

F:GCAGGTAGATGGAGGGATGA

107

60

R:CCAGGGCAGTTGTTTCAGG

BMP7

NM_001105290.1

F:TCCAGGGCAAGCACAACT

172

60

R:TCGGTGAGGAAGTGGCTATC

PIK3R5

NM_213851.1

F:CTGTCATTCCCTCCTTCCAA

117

60

R:GCCACCCTCCTCTTACTCTG

SLA-DRB1

NM_001113695.1

F:TCTGCTCTTTGTTGCTGTGG

120

60

R:GGATGCTTGCTTGGAGTGTC

THY1

XM_005667396.1

F:GGCATCGCTCTCTTGCTAAC

125

60

R:GGCAGGTTGGTGGTATTCTC

TGFB1

NM_214015.1

F:AAGCGGCAACCAAATCTATG

113

60

R:CCCGAGAGAGCAATACAGGT

SLA-1

NM_001097431.1

F:GTCAAGGAAACCGCACAGAT

113

60

R:CCCAAGTAGCAGCCAAACAT

CD74

NM_213774.1

F:ATGGACGGTGTGAACTGGA

100

60

R:GAACCTCAAAGGGTGTCTCCT

SOD2

XM_005659113.1

F:CCTTCACTTTGCCTCTTGGT

127

60

R:CACCGTTAGGGCTCAGATTT

ACTA2

XM_005671254.1

F:GTCCACCTTCCAGCAAATGT

105

60

R:AGACAGCGAGCAGGGTAAGT

SULT1E1

NM_213992.1

F:TGAAGTCTCATCTGCCACCT

101

60

R:AGAAACGACCACATCCTTGG

β-actin

DQ845171.1

F:GGCGCCCAGCACGAT

66

60

R:CCGATCCACACGGAGTACTTG

HSPA1L heat shock 70 kDa protein 1-like, CD8A CD8a molecule (CD8A), CD40 CD40 molecule, TNF receptor superfamily member 5; SLA-DQA1 MHC class II histocompatibility antigen SLA-DQA, PSTPIP1 proline-serine-threonine phosphatase interacting protein 1, STAT2 signal transducer and activator of transcription 2, GK glycerol kinase, BMP7 bone morphogenetic protein 7, PIK3R5 phosphoinositide-3-kinase, regulatory subunit 5, SLA-DRB1 MHC class II histocompatibility antigen SLA-DRB1, THY1 Thy-1 cell surface antigen, TGFB1 transforming growth factor, beta 1, SLA-1 MHC class I antigen 1, CD74 CD74 molecule, major histocompatibility complex, class II invariant chain, SOD2 superoxide dismutase 2, mitochondrial, ACTA2 actin, alpha 2, smooth muscle, aorta, SULT1E1 sulfotransferase family 1E, estrogen-preferring, member 1

Statistical analysis

The detected data by samples from two fetuses each gilt were averaged and taken as one independent data involving into statistical analysis model. In addition to Oligo Microarray and qPCR data, all other data on growth performance, intestinal morphology and enzyme activities were analyzed via the t Student’s t test for a completely randomized design using SAS (SAS, Cary, NC). Results were expressed as the mean ± SD. Differences were considered to be significant when P <0.05, while a tendency was considered when 0.05 < P < 0.10.

Results

Growth performance

Feeding HF diet markedly increased the fetal weight (in average 585 g vs.508 g, P < 0.05) at d 90 of gestation.

Morphology and enzyme activities

Feeding HF diet tended to increase intestinal villous height (P = 0.055), but decrease crypt depth (P = 0.098) of fetus (Fig. 1). Meanwhile, the lactase activity was markedly increased (+55 %, P < 0.05) by feeding HF diet relative to CON diet, whereas the maltase activity did not markedly differ between groups (Fig. 2), and sucrase activity could not be detected in fetal intestine. Gene expression of digestive enzymes were not markedly differ between two groups (Additional file 1).
Fig. 1

Effect of maternal high fat intake on the intestinal morphology of fetus (n = 4)

Fig. 2

Effects of maternal high fat intake on digestive enzyme activities of fetal intestine (n = 4). The symbol “*” in figure represents there was significant difference at 5 % level (P < 0.05)

Differentially expressed genes in fetal intestine

A total of 61 genes were differentially expressed (at least 1.5 fold change, P < 0.05), and 39 genes were down-regulated while 22 genes were up-regulated (Table 2, Fig. 3). The changes in mRNA expression detected by porcine oligo microarrays were further validated by qRT-PCR (Table 3). Given their participation in crucial biological process and modulating signal pathways on immune response, cancer and metabolism, these genes were chosen for Real-Time PCR analysis.
Table 2

Maternal high fat intake markedly regulated intestinal gene expressions related to immune response, signal transduction, cancer and metabolism

Gene Symbola

Gene name

Fold changeb

P value

CCR7

chemokine (C-C motif) receptor 7

−2.94

0.023

HSPA1L

heat shock 70 kDa protein 1-like

−2.50

0.016

CD8A

CD8a molecule (CD8A)

−2.44

0.035

CD3E

CD3e molecule, epsilon (CD3-TCR complex) (CD3E)

−2.27

0.033

STK17B

serine/threonine kinase 17b

−2.00

0.026

CD40

CD40 molecule, TNF receptor superfamily member 5

−2.00

0.011

CD2

CD2 molecule

−1.89

0.026

SLA-DQA1

MHC class II histocompatibility antigen SLA-DQA

−1.85

0.002

PSTPIP1

proline-serine-threonine phosphatase interacting protein 1

−1.85

0.007

SLAMF6

SLAM family member 6

−1.82

0.046

TP53INP1

tumor protein p53 inducible nuclear protein 1

−1.82

0.000

FAM78A

family with sequence similarity 78, member A

−1.79

0.015

BCL2A1

BCL2-related protein A1

−1.79

0.023

ARHGAP25

Rho GTPase activating protein 25

−1.75

0.011

CD1.1

CD1 antigen

−1.72

0.007

STAT2

signal transducer and activator of transcription 2

−1.69

0.042

ARHGAP30

Rho GTPase activating protein 30

−1.69

0.036

BCL2A1

BCL2-related protein A1

−1.69

0.040

IL10RB

interleukin 10 receptor, beta

−1.67

0.013

GK

glycerol kinase

−1.64

0.041

LTB

mRNA, clone:MLN010057G03, expressed in mesenteric lymph nodes

−1.64

0.031

LCP1

lymphocyte cytosolic protein 1 (L-plastin)

−1.61

0.014

PGM1

phosphoglucomutase 1

−1.61

0.045

NRROS

negative regulator of reactive oxygen species

−1.59

0.049

CYTH4

cytohesin 4

−1.59

0.039

BMP7

bone morphogenetic protein 7

−1.59

0.024

PIK3R5

phosphoinositide-3-kinase, regulatory subunit 5

−1.56

0.009

RGS14

regulator of G-protein signaling 14

−1.54

0.049

GLRX

glutaredoxin (thioltransferase)

−1.54

0.025

SLA-DRB1

MHC class II histocompatibility antigen SLA-DRB1

−1.52

0.028

LPAR2

lysophosphatidic acid receptor 2

−1.52

0.016

THY1

Thy-1 cell surface antigen

−1.52

0.028

TGFB1

transforming growth factor, beta 1

−1.52

0.022

BAZ1A

bromodomain adjacent to zinc finger domain, 1A

−1.52

0.024

CCDC69

coiled-coil domain containing 69

−1.49

0.048

LRRK2

leucine-rich repeat kinase 2

−1.49

0.022

SLA-1

MHC class I antigen 1

−1.49

0.018

CD74

CD74 molecule, major histocompatibility complex, class II invariant chain

−1.49

0.038

SOD2

superoxide dismutase 2, mitochondrial

1.51

0.004

ILF2

interleukin enhancer binding factor 2

1.51

0.021

CYP39A1

cytochrome P450, family 39, subfamily A, polypeptide 1

1.52

0.043

JPH4

junctophilin 4

1.52

0.026

ATCAY

ataxia, cerebellar, Cayman type

1.53

0.008

MATN2

mRNA, clone:OVR010041A03, expressed in ovary

1.53

0.016

CRMP1

Uncharacterized protein

1.54

0.039

RTDR1

mRNA, clone:UTR010010H08, expressed in uterus.

1.55

0.001

SPARCL1

SPARC-like 1 (hevin)

1.56

0.035

MATN2

mRNA, clone:OVR010041A03, expressed in ovary

1.56

0.019

CCN2

connective tissue growth factor

1.57

0.042

TUSC3

mRNA, clone: HTMT10103A12, expressed in hypothalamus

1.58

0.009

ID4

inhibitor of DNA binding 4, dominant negative helix-loop-helix protein

1.58

0.024

SPARC

secreted protein, acidic, cysteine-rich (osteonectin)

1.60

0.035

MEP1A

meprin A, alpha (PABA peptide hydrolase)

1.63

0.018

ARL10

ADP-ribosylation factor-like 10

1.64

0.036

STMN2

stathmin-like 2

1.64

0.039

ACTA2

actin, alpha 2, smooth muscle, aorta

1.66

0.016

SHISA2

shisa family member 2

1.76

0.029

UCHL1

ubiquitin carboxyl-terminal esterase L1 (ubiquitin thiolesterase)

1.79

0.014

OCRL

oculocerebrorenal syndrome of Lowe

2.01

0.008

SULT1E1

sulfotransferase family 1E, estrogen-preferring, member 1

2.59

0.013

aGenes were selected from the Kyoto Encyclopedia of Genes and Genomes pathways related to intestinal immune response, signal transduction, cancer and metabolism (http://www.genome.jp/kegg/pathway.html)

bThe fold change was based on the ratio of HF group to CON group, n = 4 subpools/group

Fig. 3

Heatmap of the 61 differentially expressed genes. The HF diet: s1_NS, s3-NS, s5-NS, s7-NS; The CON diet: s9_NS, s11-NS, s13-NS, s15-NS

Table 3

Differentially expressed genes in fetal intestine by maternal high fat intake and validated by qPCR

  

Fold changeb

 

Gene symbola

cDNA Microarray

qPCR

P value

ACTA2

1.66

1.10

0.246

SULT1E1

2.59

1.88

0.002

SOD2

1.51

1.75

0.036

BMP7

−1.59

−1.09

0.722

CD40

−2.00

−1.74

0.116

CD74

−1.49

−1.36

0.029

CD8A

−2.44

−1.85

0.049

GK

−1.64

−1.33

0.041

HSPAIL

−2.50

−1.60

0.027

PIK3R5

−1.56

−1.07

0.644

PSTPIP1

−1.85

−1.48

0.083

SLA-1

−1.49

−1.45

0.097

SLA-DQA1

−1.85

−1.79

0.003

SLA-DRB1

−1.52

−1.33

0.136

STAT2

−1.69

−1.18

0.125

TGF-β

−1.52

−1.19

0.296

THY1

−1.52

−1.29

0.118

aGenes were selected on the basis of their crucial role on regulating intestinal immune response (i.e.SLA-DRB1,SLA-DQA,HSPA1L,CD74,CD40), colorectal cancer (i.e.TGF-β,PIK3R5), signal transduction (i.e. PSTPIP1,BMP7,STAT2) and metabolism (i.e.GK, SULT1E1). These genes by DNA microarray were all significantly regulated (P < 0.05, at least 1.5 fold change)

bThe fold change was based on the ratio of HF group to CON group, n = 4 subpools/group

Analysis of gene ontology and signal pathway

The differentially expressed genes were clustered according to their biological process ontology by Gene Ontology (GO) analysis from the SBS analysis system (http://www.shanghaibiotech.com/). A large number of these genes were associated with antigen processing and presentation [i.e. D74, CD8A, SLA-DOB, SLA-DRB1, SLA-DQA, HSPA1L], intestinal immune network for IgA production [i.e. CD40, IL6, TGFβ1], Jak-STAT signaling pathway [i.e. IL6, STAT2 and PIK3R5], TGF-ß signaling pathway [i.e. TGF-β and PIK3R5], pathways in cancer [i.e. LEF1, PIK3R5, NOS2] and glycerolipid metabolism [i.e. GK, PNLIPRP1] et al. (Table 2, Fig. 4).
Fig. 4

Signal pathway enrichment analysis of fetal intestine by HF diet relative to CON diet (n = 4 subpools/group). The pathway terms were according to the down-regulated genes for certain biological processes, enriched categories are those identified as significantly enriched after multiple testing. * P < 0.05, ** P < 0.01. The value by horizontal axis resulted from negative value of Log (enrichment test P value, base 10)

Consequently, maternal HF intake markedly modified 33 signal pathways (P < 0.01) (Table 4), which were mainly involved in immune response (i.e. antigen processing and presentation, intestinal immune network for IgA production, primary immunodeficiency), signaling transduction (i.e. TGF-ß signaling pathway, chemokine signaling pathway), cancer (i.e. colorectal cancer, pathways in cancer), metabolism (i.e. glycerolipid metabolism, nitrogen metabolism), signaling molecules and interaction (i.e. cytokine-cytokine receptor interaction, cell adhesion molecules, neuroactive ligand-receptor interaction).
Table 4

The markedly modified signal pathways in fetal intestine of gilts fed HF diet

Name

Hits a

Total b

Percent

Enrichment test

p value

Allograft rejection

6

34

17.65 %

0.000

Antigen processing and presentation

8

64

12.50 %

0.000

Autoimmune thyroid disease

6

45

13.33 %

0.000

Cell adhesion molecules

8

71

11.27 %

0.000

Cytokine-cytokine receptor interaction

10

142

7.04 %

0.000

Hematopoietic cell lineage

7

63

11.11 %

0.000

Intestinal immune network for IgA production

7

48

14.58 %

0.000

Leishmania infection

8

63

12.70 %

0.000

Viral myocarditis

9

46

19.57 %

0.000

Graft-versus-host disease

6

57

10.53 %

1E-04

Neuroactive ligand-receptor interaction

10

174

5.75 %

1E-04

Type I diabetes mellitus

5

40

12.50 %

2E-04

Asthma

5

50

10.00 %

5E-04

Jak-STAT signaling pathway

6

82

7.32 %

6E-04

Primary immunodeficiency

4

37

10.81 %

0.0013

Pathways in cancer

7

140

5.00 %

0.0017

Hypertrophic cardiomyopathy

4

43

9.30 %

0.0022

Systemic lupus erythematosus

5

86

5.81 %

0.0043

Adipocytokine signaling pathway

4

55

7.27 %

0.005

Chemokine signaling pathway

5

90

5.56 %

0.0052

Colorectal cancer

3

31

9.68 %

0.0072

Fc gamma R-mediated phagocytosis

3

32

9.38 %

0.0078

T cell receptor signaling pathway

4

63

6.35 %

0.0078

Leukocyte transendothelial migration

4

71

5.63 %

0.0115

Acute myeloid leukemia

3

39

7.69 %

0.0129

Dilated cardiomyopathy

3

40

7.50 %

0.0137

Glycerolipid metabolism

3

41

7.32 %

0.0146

Arrhythmogenic right ventricular cardiomyopathy

3

47

6.38 %

0.0205

Nitrogen metabolism

2

19

10.53 %

0.0246

TGF-beta signaling pathway

3

55

5.45 %

0.0302

Endometrial cancer

2

22

9.09 %

0.0316

Aldosterone-regulated sodium reabsorption

2

24

8.33 %

0.0366

Type II diabetes mellitus

2

24

8.33 %

0.0366

aHits mean the number of differential expressed genes within the particular GO term

bTotal: the total number of genes within the particular GO term

Discussion

Some studies have indicated that maternal nutrition would affect the intestinal development and function of offspring [4, 1315].

In this study, maternal high fat intake increased intestinal villous height and lactase activity, which is similar as our recent study that maternal over-nutrition markedly increased birth weight, accordingly intestinal morphology as well as lactase activity [4]. It may be rational that the heavier birth weight needs higher lactase activity in preparation for better degradation of lactose, which is a crucial energy source in neonatal period [16]. However, a recent study indicated that maternal high fat intake would induce intestinal inflammation and poor gut barrier function in the offspring of mice [5]. In this study, porcine oligo miacro array analysis was used to determine the genomic response of intestine to maternal high fat intake, in an attempt to reveal the potential mechanism. According to the strict selection criteria, we found a total of 61 genes were differentially regulated and 64 % of them (39 genes) was down-regulated by HF diet. With the bioinformatics analysis, these down-regulated genes were mainly involved in process of immune response, signaling transduction, pathways in cancer and metabolism, suggesting the inhibitory effects of maternal high fat intake on certain biological events. The maternal diet fat composition could change the maternal-to-fetal fatty acid transfer and intestinal membrane n-6 and n-3 fatty acids composition of newborns, thus altering intestinal function [13]. In this study, therefore, it is rational that the addition of soy oil in maternal diet would induce alterations in intestinal physiology of fetus. Obviously, antigen processing and presentation in intestine could be inhibited by feeding HF diet, as indicated by the markedly decreasing gene expressions (i.e. SLA-1, SLA -DRB1, SLA-DQA1, CD74 and CD8, 1.5 ~ 2.5 fold reduction). Particularly, SLA-1, SLA -DRB1 and SLA-DQA1 are belonged to the highly polymorphic swine leucocyte antigen genes, which determine the immune response to disease and vaccine [17]. Among them, SLA-1 could interact with natural killer cells to prevent cytotoxicity [18], while SLA-DRB1 and -DQA1 mainly present exogenous peptides for T cells [18, 19]. Previous studies have shown that maternal high fat intake impaired intestinal barrier and immune system through altering immune cell homeostasis, such as the number of T cells and macrophages [13]. Furthermore, intestinal immune network for IgA production may be impaired by HF diet, as shown by the decreasing gene expression of CD40, IL-6 and TGF-ß. These genes are required for B cells proliferation and differentiation in Peyer’s patches, their down-regulation would reduce the homing of T cells and IgA+ plasma cells to the intestine, thus impair the immune homeostasis of intestine [20, 21].

Several signal transduction pathways related to inflammatory and immune response were affected by maternal high fat intake. For example, the TGF-β signaling pathway was affected by HF diet, as indicated by the decreasing gene expression of TGF-β and Bmp7 (approximately 1.6 fold reduction). TGF-β is a multifunctional factor regulating cell growth, adhesion and differentiation [22, 23], also exerting anti-inflammatory effects by inhibiting NF-κB expression in the intestinal epithelium [24]. The oral administration of TGF-β has been shown to decrease severity and incidence of necrotizing enterocolitis in neonatal rat necrotizing enterocolitis model [24]. In addition, feeding HF diet affected intestinal Jak-STAT signaling pathway, as shown by the decreasing gene expression of IL6, STAT2 and PIK3R5. The Jak-STAT signaling pathway is required for T cell differentiation, B cell maturation and secretion of sIgA [25], these down-regulated genes by HF diet may induce the abnormal intestinal innate immune response. Similarly, previous study demonstrated that maternal high protein diet would decrease liver mass, associated with altering gene expressions mapping to Jak-STAT signaling pathway in mouse offspring [26].

Furthermore, the lower expressions of TGF-β and PIK3R5 genes by HF diet may affect the progression of colorectal cancer. TGF-β1/Smads signaling pathway was demonstrated to mediate epithelial-to-mesenchymal transition, associated with the progression of colorectal cancer [27]. Mutation of PIK3R5 and other genes (i.e. PRKCZ, PTEN, RHEB and RPS6KB1) have altered PI3K signaling pathway, which is the central pathway for both colorectal and breast cancers [28]. Recent studies also indicated that maternal high fat diet would modify the susceptibility to breast cancer [29, 30], meanwhile it is dependent on fat or oil sources [3133].

In this study, moreover, the markedly reduced glycerol kinase by feeding HF diet suggests the intestinal metabolism was altered. Glycerol kinase is required to release glycerol from glycerol-3-phosphate and dihydroxyacetone, and intestinal glycerol could produce 20 ~ 25 % of total endogenous glucose under insulinopenia, suggesting the important role of glycerol in intestinal metabolism [34]. Although most of genes were markedly down-regulated by HF diet, some of genes (SOD2, CYP39A1, CCN2, SPARC et al.) were up-regulated. Particularly, SOD2, as an anti-oxidative enzyme in living cells, was highly expressed (1.75 fold change, P = 0.04). Likewise, a recent study demonstrated that maternal high energy intake increased the expression of SOD in offspring ileum [5]. Previous study indicated that the increasing SOD gene is not necessarily associated with a better antioxidant capability, for example, the inflamed intestinal mucosa has been shown to contain higher SOD protein compared with normal tissues [35]. In addition, we found that feeding HF diet markedly increased gene expression of SULT1E by both DNA Microarray and RT-PCR analysis. SULT1E, as an estrogen-preferring drug metabolizing enzyme, its highly expression may be an compensatory response to high circulating estrogen, which occurs in dams fed high fat diet [36]. It has been shown that the estrogen deletion by SULT1E over-expression is associated with the risk of developing different types of cancers [28, 37].

Conclusion

In summary, maternal high fat intake was able to increase fetal and intestinal weights as well as lactase activity, however, it altered the intestinal immune response, signal transduction and metabolism.

Declarations

Acknowledgements

We would like to thank the staff at our laboratory for their ongoing assistance.

Funding

The present study was supported by the National Natural Science Foundation of China (31101727) and the National Basic Research Program (973 Program) of China (No. 2013CB127306). 

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Animal Nutrition, Sichuan Agricultural University
(2)
Key Laboratory for Animal Disease-Resistance Nutrition, Ministry of Education

References

  1. Wu G, Bazer FW, Wallace JM, Spencer TE. Board-invited review: intrauterine growth retardation: implications for the animal sciences. J Anim Sci. 2006;84:2316–37.View ArticlePubMedGoogle Scholar
  2. Sangild PT. Gut responses to enteral nutrition in preterm infants and animals. Exp Biol Med (Maywood). 2006;231:1695–711.Google Scholar
  3. Pinheiro DF, Pinheiro PF, Buratini Jr J, Castilho AC, Lima PF, Trinca LA, Vicentini-Paulino Mde L. Maternal protein restriction during pregnancy affects gene expression and immunolocalization of intestinal nutrient transporters in rats. Clin Sci (Lond). 2013;125:281–9.View ArticleGoogle Scholar
  4. Cao M, Che L, Wang J, Yang M, Su G, Fang Z, Lin Y, Xu S, Wu D. Effects of maternal over- and undernutrition on intestinal morphology, enzyme activity, and gene expression of nutrient transporters in newborn and weaned pigs. Nutrition. 2014;30:1442–7.View ArticlePubMedGoogle Scholar
  5. Xue Y, Wang H, Du M, Zhu MJ. Maternal obesity induces gut inflammation and impairs gut epithelial barrier function in nonobese diabetic mice. J Nutr Biochem. 2014;25:758–64.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Yan X, Huang Y, Wang H, Du M, Hess BW, Ford SP, Nathanielsz PW, Zhu MJ. Maternal obesity induces sustained inflammation in both fetal and offspring large intestine of sheep. Inflamm Bowel Dis. 2011;17:1513–22.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Gonzalez-Bulnes A, Astiz S, Ovilo C, Lopez-Bote CJ, Sanchez-Sanchez R, Perez-Solana ML, Torres-Rovira L, Ayuso M, Gonzalez J. Early-postnatal changes in adiposity and lipids profile by transgenerational developmental programming in swine with obesity/leptin resistance. J Endocrinol. 2014;223:M17–29.View ArticlePubMedGoogle Scholar
  8. Zhang Q, Widmer G, Tzipori S. A pig model of the human gastrointestinal tract. Gut Microbes. 2013;4:193–200.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Heinritz SN, Mosenthin R, Weiss E. Use of pigs as a potential model for research into dietary modulation of the human gut microbiota. Nutr Res Rev. 2013;26:191–209.View ArticlePubMedGoogle Scholar
  10. Han F, Hu L, Xuan Y, Ding X, Luo Y, Bai S, He S, Zhang K, Che L. Effects of high nutrient intake on the growth performance, intestinal morphology and immune function of neonatal intra-uterine growth-retarded pigs. Br J Nutr. 2013;110:1819–27.View ArticlePubMedGoogle Scholar
  11. Hu L, Liu Y, Yan C, Peng X, Xu Q, Xuan Y, Han F, Tian G, Fang Z, Lin Y, et al. Postnatal nutritional restriction affects growth and immune function of piglets with intra-uterine growth restriction. Br J Nutr. 2015;114:53–62.View ArticlePubMedGoogle Scholar
  12. Che L, Chen H, Yu B, He J, Zheng P, Mao X, Yu J, Huang Z, Chen D. Long-term intake of pea fiber affects colonic barrier function, bacterial and transcriptional profile in pig model. Nutr Cancer. 2014;66:388–99.View ArticlePubMedGoogle Scholar
  13. Innis SM, Dai C, Wu X, Buchan AM, Jacobson K. Perinatal lipid nutrition alters early intestinal development and programs the response to experimental colitis in young adult rats. Am J Physiol Gastrointest Liver Physiol. 2010;299:G1376–1385.View ArticlePubMedGoogle Scholar
  14. Boudry G, Douard V, Mourot J, Lalles JP, Le Huerou-Luron I. Linseed oil in the maternal diet during gestation and lactation modifies fatty acid composition, mucosal architecture, and mast cell regulation of the ileal barrier in piglets. J Nutr. 2009;139:1110–7.View ArticlePubMedGoogle Scholar
  15. Desaldeleer C, Ferret-Bernard S, de Quelen F, Le Normand L, Perrier C, Savary G, Rome V, Michel C, Mourot J, Le Huerou-Luron I, Boudry G. Maternal 18:3n-3 favors piglet intestinal passage of LPS and promotes intestinal anti-inflammatory response to this bacterial ligand. J Nutr Biochem. 2014;25:1090–8.View ArticlePubMedGoogle Scholar
  16. Heyman MB, Committee on N. Lactose intolerance in infants, children, and adolescents. Pediatrics. 2006;118:1279–86.View ArticlePubMedGoogle Scholar
  17. Ho CS, Lunney JK, Lee JH, Franzo-Romain MH, Martens GW, Rowland RR, Smith DM. Molecular characterization of swine leucocyte antigen class II genes in outbred pig populations. Anim Genet. 2010;41:428–32.PubMedGoogle Scholar
  18. Lunney JK, Ho CS, Wysocki M, Smith DM. Molecular genetics of the swine major histocompatibility complex, the SLA complex. Dev Comp Immunol. 2009;33:362–74.View ArticlePubMedGoogle Scholar
  19. Summerfield A, Guzylack-Piriou L, Schaub A, Carrasco CP, Tache V, Charley B, McCullough KC. Porcine peripheral blood dendritic cells and natural interferon-producing cells. Immunology. 2003;110:440–9.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Wang D, Dubois RN, Richmond A. The role of chemokines in intestinal inflammation and cancer. Curr Opin Pharmacol. 2009;9:688–96.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Chu VT, Beller A, Rausch S, Strandmark J, Zanker M, Arbach O, Kruglov A, Berek C. Eosinophils promote generation and maintenance of immunoglobulin-A-expressing plasma cells and contribute to gut immune homeostasis. Immunity. 2014;40:582–93.View ArticlePubMedGoogle Scholar
  22. Massagué J. TGFβ in cancer. Cell. 2008;134:215–30.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Van’t Land B, Meijer H, Frerichs J, Koetsier M, Jager D, Smeets R, M'Rabet L, Hoijer M. Transforming Growth Factor-β2 protects the small intestine during methotrexate treatment in rats possibly by reducing stem cell cycling. Br J Cancer. 2002;87:113–8.Google Scholar
  24. Shiou SR, Yu Y, Guo Y, Westerhoff M, Lu L, Petrof EO, Sun J, Claud EC. Oral administration of transforming growth factor-beta1 (TGF-beta1) protects the immature gut from injury via Smad protein-dependent suppression of epithelial nuclear factor kappaB (NF-kappaB) signaling and proinflammatory cytokine production. J Biol Chem. 2013;288:34757–66.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Heneghan AF, Pierre JF, Kudsk KA. JAK-STAT and intestinal mucosal immunology. JAKSTAT. 2013;2:e25530.PubMedPubMed CentralGoogle Scholar
  26. Vanselow J, Kucia M, Langhammer M, Koczan D, Rehfeldt C, Metges CC. Hepatic expression of the GH/JAK/STAT/IGF pathway, acute-phase response signalling and complement system are affected in mouse offspring by prenatal and early postnatal exposure to maternal high-protein diet. Eur J Nutr. 2011;50:611–23.View ArticlePubMedGoogle Scholar
  27. Ji Q, Liu X, Han Z, Zhou L, Sui H, Yan L, Jiang H, Ren J, Cai J, Li Q. Resveratrol suppresses epithelial-to-mesenchymal transition in colorectal cancer through TGF-β1/Smads signaling pathway mediated Snail/E-cadherin expression. BMC Cancer. 2015;15:1.View ArticleGoogle Scholar
  28. Wood LD, Parsons DW, Jones S, Lin J, Sjöblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–13.View ArticlePubMedGoogle Scholar
  29. Montales MT, Melnyk SB, Simmen FA, Simmen RC. Maternal metabolic perturbations elicited by high-fat diet promote Wnt-1-induced mammary tumor risk in adult female offspring via long-term effects on mammary and systemic phenotypes. Carcinogenesis. 2014;35:2102–12.View ArticlePubMedPubMed CentralGoogle Scholar
  30. de Oliveira AF, Fontelles CC, Rosim MP, de Oliveira TF, de Melo Loureiro AP, Mancini-Filho J, Rogero MM, Moreno FS, de Assis S, Barbisan LF, et al. Exposure to lard-based high-fat diet during fetal and lactation periods modifies breast cancer susceptibility in adulthood in rats. J Nutr Biochem. 2014;25:613–22.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Mabasa L, Cho K, Walters MW, Bae S, Park CS. Maternal dietary canola oil suppresses growth of mammary carcinogenesis in female rat offspring. Nutr Cancer. 2013;65:695–701.View ArticlePubMedGoogle Scholar
  32. Czuba AK, Mazurkiewicz M, Stanimirova-Daszykowska I. Enrichment of maternal diet with conjugated linoleic acids influences desaturases activity and fatty acids profile in livers and hepatic microsomes of the offspring with 7, 12-dimethylbenz [a] anthracene-induced mammary tumors. Acta Pol Pharm. 2014;71:747.PubMedGoogle Scholar
  33. Su H-M, Hsieh P-H, Chen H-F. A maternal high n-6 fat diet with fish oil supplementation during pregnancy and lactation in rats decreases breast cancer risk in the female offspring. J Nutr Biochem. 2010;21:1033–7.View ArticlePubMedGoogle Scholar
  34. Croset M, Rajas F, Zitoun C, Hurot J-M, Montano S, Mithieux G. Rat small intestine is an insulin-sensitive gluconeogenic organ. Diabetes. 2001;50:740–6.View ArticlePubMedGoogle Scholar
  35. Kruidenier L, Kuiper I, van Duijn W, Marklund SL, van Hogezand RA, Lamers CB, Verspaget HW. Differential mucosal expression of three superoxide dismutase isoforms in inflammatory bowel disease. J Pathol. 2003;201:7–16.View ArticlePubMedGoogle Scholar
  36. Hilakivi-Clarke L, Clarke R, Onojafe I, Raygada M, Cho E, Lippman M. A maternal diet high in n − 6 polyunsaturated fats alters mammary gland development, puberty onset, and breast cancer risk among female rat offspring. Proc Natl Acad Sci. 1997;94:9372–7.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Sharda DR, Miller-Lee JL, Kanski GM, Hunter JC, Lang CH, Kennett MJ, Korzick DH. Comparison of the agar block and Lieber–DeCarli diets to study chronic alcohol consumption in an aging model of Fischer 344 female rats. J Pharmacol Toxicol Methods. 2012;66:257–63.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Che et al. 2016

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