Abstract

Simple Summary

   Although the correlation between oxidative stress and liver metabolic
   dysfunction has been established in piglets with intrauterine growth
   restriction, detailed information concerning the molecular mechanisms
   that facilitate this relationship remains limited. In the present
   study, metabolomic and transcriptomic techniques were used to
   investigate differences in gene expression and metabolites in the liver
   of piglets in intrauterine growth restriction and normal birth weight
   groups to systematically analyze the potential mechanism of metabolic
   characteristics induced by oxidative stress in the liver of
   intrauterine growth restriction piglets. Our results revealed that the
   intrauterine growth restriction piglets were involved in a variety of
   metabolic abnormalities, including mitochondrial dysfunction, imbalance
   of fatty acid composition, disruption to sources of one-carbon unit
   supply, and abnormal galactose conversion, which may be responsible for
   oxidative stress in the liver.

Abstract

   The correlation between oxidative stress and liver metabolic
   dysfunction in piglets with intrauterine growth restriction (IUGR)
   remains limited. Therefore, the objective of the present study was to
   investigate potential mechanisms of metabolic characteristics induced
   by oxidative stress in the livers of IUGR piglets using metabolomic and
   transcriptomic analysis. Analysis of the phenotypic characteristics
   showed that the liver weight of the intrauterine growth restriction
   piglets was significantly lower than that of normal birth weight
   piglets. Intrauterine growth restriction piglets exhibited disordered
   hepatic cord arrangement and vacuolization as well as excessive lipid
   accumulation in hepatocytes. In addition, the activities of antioxidant
   enzymes were significantly decreased in the liver of the intrauterine
   growth restriction piglets, whereas the level of the lipid peroxidation
   marker MDA was significantly increased. Finally, our findings revealed
   that intrauterine growth restriction piglets were involved in a variety
   of metabolic abnormalities, including mitochondrial dysfunction,
   imbalance of fatty acid composition, disruption to sources of
   one-carbon unit supply, and abnormal galactose conversion, which may be
   responsible for oxidative stress in the liver. In summary, these data
   provided a detailed theoretical reference for revealing the hepatic
   metabolic characteristics of intrauterine growth restriction piglets.

   Keywords: intrauterine growth restriction (IUGR), piglet, liver,
   metabolomics, transcriptomics

1. Introduction

   Piglet mortality is an ongoing concern in global pork production. The
   first week after birth is the most critical for piglet survival due to
   the higher risk of death during this period [[38]1]. Low birth weight
   is likely to be an important determinant of the high mortality rate of
   neonatal piglets as low birth weight piglets usually die from crushing,
   weakness, starvation, and other congenital defects within the first few
   days of life [[39]2]. Most low birth weight piglets are recognized as
   intrauterine growth restriction (IUGR) piglets. They usually suffer
   from intrauterine crowding and placental inefficiency, which stunts
   fetal growth potential [[40]3]. IUGR in piglets not only shows higher
   mortality and morbidity in the short term but is also related to
   disturbances in metabolism, stunted growth, and poor carcass quality
   [[41]4]. Considerable evidence suggests that oxidative stress is a
   major contributor to metabolic disorders in various IUGR models. Some
   studies have shown that a suboptimal prenatal environment can trigger
   fetal oxidative stress, which may be the main cause of metabolic organ
   dysfunction, resulting in inflammation, apoptosis, and autophagy,
   ultimately impairing fetal growth [[42]5]. However, detailed
   information regarding oxidative stress leading to metabolic
   abnormalities in individuals with IUGR remains limited.

   The liver plays a central role in the management of systemic
   homeostasis by regulating energy metabolism; therefore, liver
   dysfunction may cause widespread metabolic disorders. Previous studies
   have shown that individuals with IUGR exhibit hepatic dysfunction
   [[43]6,[44]7]. In piglets, IUGR alters the expression of genes
   associated with hepatic energy production and mitochondrial biogenesis,
   which impairs hepatic mitochondrial oxidative phosphorylation and
   reduces hepatic mitochondrial DNA content [[45]8,[46]9,[47]10]. In
   addition, compromised utilization of circulating triglycerides and less
   optimized lipid metabolism have been reported in IUGR piglets [[48]11].
   Our previous study revealed dysregulation of glucocorticoids in the
   liver of IUGR piglets [[49]12]. We hypothesized that the liver
   metabolism of IUGR piglets would also be abnormal. It is well known
   that glucocorticoid secretion occurs mainly in response to cellular
   stress activities through the hypothalamic–pituitary–adrenal (HPA) axis
   [[50]13]. Although the above reports have directly or indirectly
   established a negative correlation between oxidative stress and liver
   metabolic dysfunction in IUGR piglets, the molecular mechanisms that
   facilitate this relationship require further investigation.

   Omics technology has accelerated the development of quantitative and
   high-throughput. Though integrating multiomics analysis, key molecular
   functions and metabolic pathways involved in complex biological
   processes can be explored in-depth [[51]14]. In our previous study, we
   successfully conducted transcriptome sequencing of the livers of IUGR
   piglets [[52]12]. Hence, in this study, we first applied metabolome to
   assess liver metabolic changes between IUGR and normal birth weight
   (NBW) groups. Second, we conducted an integrative analysis of
   metabolomics and transcriptomics to explore the potential mechanism of
   metabolic characteristics induced by oxidative stress in the liver of
   IUGR piglets.

2. Materials and Methods

2.1. Sample Collection

   All animals were treated humanely according to the criteria outlined in
   the “Guide for the Care and Use of Laboratory Animals” published by the
   Institute of Animal Sciences, Chinese Academy of Agricultural Sciences
   (Beijing, China). The experimental protocols used in this study were
   approved by the Animal Care and Use Committee (IAS2021-65).

   In total, 17 full-term neonatal piglets (Landrace sires × Large White
   dams) were recruited, including 9 IUGR piglets and 8 NBW controls.
   According to the criteria [[53]15]. The body weight of IUGR piglets
   should be chosen less than two standard deviations within the average
   weight in the litter, whereas the body weight of NBW piglets should be
   within one standard deviation of the average weight. In addition, IUGR
   piglets were categorized based on their head morphological
   characteristics using a previously described method [[54]16]: (1)
   steep-dolphin-like forehead, (2) bulging eyes, and (3) wrinkles
   perpendicular to the mouth. Piglets with at least one of the three
   characteristics were defined as IUGR.

   Both the body and liver weights of the piglets were measured after
   sacrifice. Liver samples were collected, washed, and frozen in liquid
   nitrogen for further use.

2.2. Widely Targeted Metabolomics and Analysis

   Metabolomics analyses were performed at MetWare Biotechnology Co., Ltd.
   (Wuhan, China) as previously described [[55]17], including liver tissue
   extraction, metabolite identification, and quantification.

   For each metabolite, Student’s t-test was used to assess the
   statistical significance of p values, and OPS-DA was used to determine
   the variable importance of projection (VIP) scores. Significantly
   differential metabolites between the groups were determined under the
   criteria: VIP ≥ 1, p < 0.05, and absolute Log[2]FC (fold change) ≥ 1.
   All analyses were performed using R statistical software V.3.6.1
   (Vienna, Austria).

2.3. Bioinformatic Analysis of Transcriptome

   RNA sequencing sources were collected from the Sequence Read Archive
   (Bio-Project: PRJNA597972;
   [56]https://www.ncbi.nlm.nih.gov/bioproject/PRJNA597972 (accessed on 1
   May 2022). Due to the update of the pig reference genome, the filtered
   sequence reads were realigned to the Sscrofa11.4 reference genome from
   the Ensembl annotation system. Bioinformatics analysis was performed as
   previously described [[57]12] and integrative analysis was performed by
   MetaboAnalyst V.5.0 tools (McGill University, Quebec, QC, Canada;
   [58]https://www.metaboanalyst.ca/ (accessed on 15 July 2022).

2.4. Detection of Oxidative Stress Biomarkers in Liver

   Commercial kits for detecting the total antioxidant capacity (T-AOC),
   superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase
   (CAT), glutathione (GSH), and malondialdehyde (MDA) were purchased from
   the Nanjing Jiancheng Institute of Biotechnology (Nanjing, China), and
   liver tissue was processed and biochemically analyzed following to the
   manufacturer’s instructions.

2.5. Transmission Electron Microscopy Analysis

   The procedure for liver hematoxylin eosin (H&E) and Oil Red O (ORO)
   staining was performed according to previous reports [[59]18]. Liver
   tissue specimens were dehydrated, fixed, and stained with H&E or ORO
   dye. The histopathology of liver tissues was evaluated through a light
   microscope (Nikon ECLIPSE 80i; Nikon Corporation, Tokyo, Japan). Two
   independent observers performed a double-blind assessment and each was
   observed in at least three different viewpoints.

2.6. Liver Morphological Evaluation

   Liver samples for transmission electron microscopy (TEM) were prepared
   according to previously described [[60]19]. Briefly, the liver tissue
   was sequentially fixed in 2.5% glutaraldehyde and 1% osmium tetroxide,
   dehydrated, and finally embedded. Then the tissue was sliced into
   ultrathin sections (60–80 nm) and stained with 2% uranyl acetate and
   lead citrate, observed under TEM (Hitachi HT-7700, Tokyo, Japan).

2.7. RNA Isolation and Real-Time qPCR

   Total RNA of liver was extracted using Invitrogen^TM TRIzol (Thermo
   Fisher Scientific, Carlsbad, CA, USA). cDNA was synthesized using
   PrimeScript™ RT reagent Kit (Takara, kusatsu, shiga, Japan). Real-time
   qPCR was performed in Bio-Rad fluorescence detection system (CFX96
   RealTime PCR, Bio-Rad Laboratories, Hercules, CA, USA) as previously
   described [[61]20]. Real-time qPCR was repeated three times
   independently and the 2^−ΔΔCt method was used to measure the relative
   mRNA expression of target genes [[62]21]. GAPDH was a reference gene
   and the primers information are listed in [63]Supplementary file: Table
   S1.

2.8. Statistical Analysis

   Statistical analysis was performed using SPSS v.21.0 (IBM Headquarters,
   Chicago, IL, USA). The Student’s t-test was used to analyze the
   differences between the two groups. The data are presented as means ±
   standard deviation (SD), and p values are indicated by asterisks to
   indicate statistically significant differences (* p < 0.05; ** p <
   0.01).

3. Results

3.1. Phenotypic Traits of Piglets in IUGR and NBW Groups

   The body weight of all piglets in this study are shown in [64]Figure
   1a. As expected, the initial body weight of newborn IUGR piglets was
   approximately 44% lower than that of NBW piglets. At the same time,
   both the absolute and relative weights of the liver were significantly
   lower in IUGR piglets than in NBW piglets.

Figure 1.

   [65]Figure 1
   [66]Open in a new tab

   Phenotypic traits of piglets between IUGR and NBW group. (a) Body
   weight, liver weight, as well as the relative liver weight of the
   piglets. The data are presented as the means ± SD, and p values are
   indicated by asterisks to indicate statistically significant
   differences (* p < 0.05; ** p < 0.01). (b) Histopathological
   characteristics of liver in IUGR and NBW piglets, light microscopy of
   hematoxylin eosin (H&E) and Oil Red O (ORO) staining, as well as
   transmission electron micrographs (TEM). Abbreviations: N, nucleus; M:
   mitochondria; RER: rough endoplasmic reticulum; LD: lipid droplets.

   Histopathological examinations of hepatic sections from IUGR and NBW
   piglets are presented in [67]Figure 1b. The NBW group exhibited normal
   histomorphological structures. Conversely, apparent vacuolization and
   disordered arrangement of the hepatic cords were observed in the
   hepatocytes of IUGR piglets. Notably, ORO staining revealed increased
   lipid accumulation in the IUGR group. Furthermore, TEM showed
   significant pathological changes in the hepatocyte ultrastructure of
   IUGR piglets, consistent with our previous results that IUGR piglets
   have shorter, globular, and smaller mitochondria compared to the NBW
   group; in addition, a greater volume and number of lipid drops were
   observed in the cytoplasm.

3.2. Liver Cellular Stress in the Liver of Piglets

   To explore hepatic oxidative stress in piglets, we examined the key
   antioxidant activities, including SOD, CAT, GSH-Px, T-AOC, as well as
   the level of GSH in liver tissues from IUGR and NBW groups. The
   antioxidative enzymes activities of SOD, CAT, T-AOC, GSH-Px, and the
   level of GSH in the liver of IUGR piglets were noticeably decreased
   compared to those in the NBW group. Meanwhile, the level of the lipid
   peroxidation marker MDA was significantly increased in the IUGR group
   compared with that in NBW group ([68]Figure 2).

Figure 2.

   [69]Figure 2
   [70]Open in a new tab

   The activity of SOD, CAT, GSH-Px, T-AOC, MDA, and the concentration of
   GSH in liver between IUGR and NBW piglets. Differences were assessed by
   t-test (n = 8) and asterisks represent the p values (* p < 0.05; ** p <
   0.01).

3.3. Liver Metabolic Profiles in IUGR and NBW Piglets

   To better explore the underlying mechanisms of liver metabolism in IUGR
   piglets, we employed a targeted UPLC−MS/MS approach for comprehensive
   metabolic profiling of the livers from the IUGR and NBW piglets. Based
   on metabolomics and multivariate statistical analyses, 866 known
   metabolites were detected in the IUGR and NBW groups. PCA score plots
   showed a clear separation trend ([71]Figure 3a). Subsequently, an
   OPLS-DA model was constructed with one predictive and two orthogonal
   components (R^2X = 0.434, R^2Y = 0.998, Q^2 = 0.894), based on the
   detected metabolites ([72]Figure 3b). These results indicated that the
   liver metabolic profile of IUGR piglets is distinct from that of NBW
   controls, demonstrating satisfactory modeling and predictive abilities.

Figure 3.

   [73]Figure 3
   [74]Open in a new tab

   Metabolomics analysis of liver in the IUGR and NBW piglets. (a)
   Principal component analysis (PCA) scores plot derived from liver
   metabolomics between the NBW and IUGR group. (b) The permutation tests
   between NBW and IUGR group. (c) Volcano plot of DMs between NBW and
   IUGR piglets. Red, green, and gray dots indicate significantly
   upregulated, significantly downregulated, and non-significant
   metabolites, respectively. (d) The top 20 enriched KEGG pathways in the
   IUGR piglets.

   Significantly different liver metabolites (DMs) between IUGR and NBW
   piglets were selected under the criteria: VIP ≥ 1, p < 0.05, and
   absolute Log[2]FC ≥ 1. Ultimately, 94 DMs, including 65 downregulated
   and 29 upregulated DMs, were identified ([75]Figure 3c;
   [76]Supplementary file: Table S2). Among them, urobilin showed the
   highest decrease among the identified metabolites in IUGR piglets with
   Log[2]FC = −6.79, and L-theanine was the most increased metabolite with
   Log[2]FC = 2.35. KEGG analysis revealed that the DMs were significantly
   enriched in linoleic acid metabolism, galactose metabolism, oxytocin
   signaling pathway, aldosterone synthesis and secretion, and
   biosynthesis of unsaturated fatty acids (p ˂ 0.05), as shown in
   [77]Figure 3d. Among the DMs involved in the above pathways, the levels
   of gamma-linolenic acid (GLA), arachidonic acid (AA, 20:4n-6), hydroxy
   octadecaenoic acids (HODEs; 9-HODE and 13-HODEs), eicosapentaenoic acid
   (EPA, 20:5n-3), and N-acetyl-D-galactosamine were significantly lower
   in the IUGR group than those in the NBW group. Conversely, the relative
   abundance of galactitol, inositol, sorbitol, cyclic ADP ribose (cADPR),
   and nicotinic acid adenine dinucleotide (NAD+) was higher in the IUGR
   group than in the NBW piglets.

3.4. Liver Transcriptomic Analysis of IUGR and NBW Piglets

   We selected five IUGR and four NBW piglet samples from the previously
   published RNA sequencing data of neonatal piglets for transcriptomic
   analysis, and screened out 1828 differentially expressed genes (DEGs)
   (padj ≤ 0.05; Fold Change ≥ 2 or ≤ 0.5), of which 643 were upregulated
   and 1185 were downregulated in the IUGR group ([78]Figure 4a–c,
   [79]Supplementary file: Table S3). Subsequently, we performed KEGG
   pathway enrichment analysis to explore the potential functions of these
   DEGs and displayed the top 20 enriched pathways in the IUGR group
   compared to the NBW group ([80]Figure 4d). The top enriched pathways,
   such as glycine, serine, and threonine metabolism, glutathione
   metabolism, p53 signaling pathway, and cellular senescence, were all
   closely related to oxidative stress.

Figure 4.

   [81]Figure 4
   [82]Open in a new tab

   Bioinformatics analysis of liver transcriptome between the IUGR and NBW
   piglets. (a) The number of DEGs between the IUGR and NBW piglets. (b)
   Volcano plot of DEGs between the IUGR and NBW piglets. (c) The cluster
   heatmap between the IUGR and NBW piglets. (d) The top 20 enriched KEGG
   pathway terms were significantly altered in the IUGR piglets.

3.5. Integrative Analysis of Differential Transcriptomic and Metabolomic
Profiles between IUGR and NBW Piglets

   Given that the molecular mechanisms and metabolic pathways in the
   livers of IUGR piglets have not been fully elucidated, we conducted a
   comprehensive metabolome and transcriptome analysis to understand the
   regulatory network of hepatic metabolism in IUGR piglets. By mapping
   all DEGs and DMs to the KEGG pathway database, we obtained their common
   pathway in which both the DMs and DEGs were involved by joint pathway
   analysis. The interrelations of the integrated pathways were further
   obtained globally diagram using visual network analysis ([83]Figure 5,
   [84]Supplementary file: Table S4). Based on the integrated analysis, we
   further focused on the DEGs and DMs involved in these pathways, which
   were closely related to oxidative stress. These pathways were
   classified into four categories based on physiological processes,
   including mitochondrial dysfunction (nicotinate and nicotinamide
   metabolism, oxytocin signaling pathway, aldosterone synthesis and
   secretion, and calcium signaling pathways), imbalance of fatty acid
   composition (biosynthesis of unsaturated fatty acids, linoleic acid
   metabolism, alpha-linolenic acid metabolism, and arachidonic acid
   metabolism), disruption to sources of one-carbon unit supply (glycine,
   serine and threonine metabolism, and cysteine and methionine
   metabolism), as well as abnormal galactose conversion (galactose
   metabolism, fructose and mannose metabolism, and inositol phosphate
   metabolism) ([85]Table 1).

Figure 5.

   [86]Figure 5
   [87]Open in a new tab

   A network of interrelationships that integrate pathways in metabolomics
   and transcriptomics.

Table 1.

   Major DEGs and DMs included in the integrated pathway related to
   oxidative stress.
   Physiological
   Process Pathway Term DEGs DMs
   Mitochondrial dysfunction Nicotinate and nicotinamide metabolism
   (ko00760);
   Oxytocin signaling pathway (ko04921);
   Aldosterone synthesis and secretion (ko04925);
   Calcium signaling pathway (ko04020) NT5E, CD38, SIRT4, NNMT, NT5C3A,
   ENPP3, BST1, NMRK2, LIPE, HSD3B, CDKN1A, CAMK1D, CAMK1, ADCY1, CACNB1,
   EGFR, CALML4, CACNB3, KCNJ2, PLCE1, PHKA2, ERBB2, GNAL, PLCD1, P2RX3,
   CXCR4 NAD^+, cADPR, AA,
   N’-Methyl-2-pyridone-5-carboxamide,
   6-hydroxy-3-succinylpyridine
   Imbalance of fatty acid composition Biosynthesis of unsaturated fatty
   acids (ko01040);
   Linoleic acid metabolism (ko00591);
   alpha-Linolenic acid metabolism (ko00592);
   Arachidonic acid metabolism (ko00590) CYP2C, FADS2, CYP4A24, PTGS1,
   GGT5, ALOX5, CBR3, AKR1C2, ELOVL2, ACOT HSD17B12 AA, GLA, EPA,
   9,10-DiHOME, 12,13-DiHOME, FFA(18:4)
   Disruption to sources of one-carbon unit supply Glycine, serine and
   threonine metabolism (ko00260);
   Cysteine and methionine metabolism (ko00270) GATM, PSAT1, PHGDH, PSPH,
   DAO, GLYCTK, ALAS2, BHMT, DMGDH, AGXT2, PIPOX, GCAT, GNMT, GCLC,
   DNMT3B, LDHA, DNMT1, SRM, BCAT1 N,N-Dimethylglycine,
   5′-Deoxy-5′-(Methylthio) Adenosine
   Abnormal galactose conversion Galactose metabolism (ko00052);
   Inositol phosphate metabolism (ko00562);
   Fructose and mannose metabolism (ko00051) ISYNA1, PIK3C2G, GANC, GALM,
   MGAM2, LCT, MGAM, PFKFB4, TKFC, PI4KA, PLCE1, PLCD1, MTMR7 galactitol,
   inositol, sorbitol,
   3-hydroxyhippuric acid,
   mannitol,
   N-acetyl-D-galactosamine,
   GDP-fucose
   [88]Open in a new tab

3.6. The Validation of DEGs Expression Involved in the Integrated Pathways
between IUGR and NBW Piglets

   We selected several key DEGs involved in the integrated pathways that
   may directly or indirectly regulate oxidative stress in the liver of
   IUGR piglets and to validate their expression patterns. The results
   showed that the mRNA expressions of NNMT, CAMK1D, CACNB1, GNMT, and
   ISYNA1 were significantly increased in IUGR piglets, whereas the mRNA
   expressions of CD38, BST1, CYP2C, FADS2, PTGS1, ELOVL2, ACOT7, GALM,
   PHGDH, PSAT1, PSPH, BHMT, DMGDH, and DNMT1 were significantly increased
   in the NBW group ([89]Figure 6a). Moreover, the similar pattern for the
   expression of all genes was found between the real-time qPCR and our
   previous RNA-seq data ([90]Figure 6b).

Figure 6.

   [91]Figure 6
   [92]Open in a new tab

   Verification of DEGs expression involved in the integrated pathways
   between IUGR and NBW piglets. (a) The relative expression of mRNA was
   determined by real-time qPCR. Results were presented as means ± SD.
   Differences were assessed by t-test (n = 8) and asterisks represent the
   p values (** p < 0.01). (b) log[2]Foldchange of selected genes between
   real-time qPCR and RNA-seq analysis.

4. Discussion

   According to Barker’s ‘thrifty phenotype’ hypothesis, liver growth is
   impaired in IUGR individuals, as indicated by a decrease in abdominal
   circumference at birth [[93]22]. Consistent with this, we observed
   similar results in our previous and current study [[94]12,[95]23], as
   both the absolute and relative weight of the liver were significantly
   lower in IUGR piglets. Combined with our previous report, IUGR piglets
   undergo rapid catch-up growth in the early postnatal period; this may
   be due to the malnourished liver adapting to the abundant postnatal
   environment, and gaining excessive growth relative to other organs,
   which may in turn be detrimental to hepatic function, further
   exacerbating risk of metabolic syndrome [[96]24].

   Previous studies have revealed that mitochondrial dysfunction in the
   liver often causes increased levels of superoxide radical metabolites,
   which may lead to oxidative damage. Mitochondria are abundant in the
   liver, and when mitochondria are damaged or dysfunctional, the liver is
   bound to suffer from oxidative stress [[97]25]. Similarly, many studies
   have revealed mitochondrial stress in the liver of IUGR individuals
   [[98]8,[99]9,[100]10,[101]26]. In aerobic respiration, mitochondria use
   oxygen as the terminal electron acceptor; however, this process
   produces damaging reactive oxygen species (ROS). Oxidative stress
   occurs when an imbalance appears between ROS and antioxidant enzymes,
   which leads to the transformation of ROS into less harmful molecules
   [[102]27]. NAD^+ is an enzyme cofactor, and its metabolites are key
   regulators of mitochondrial oxidative metabolism and stress resistance
   [[103]28]. Recent studies have shown that cADPR, an important
   metabolite of NAD^+, is decomposed by the NAD^+-dependent enzyme CD38,
   which has been proposed as a second messenger that regulates
   intracellular calcium levels in various cellular processes [[104]29].
   In this study, compared with the NBW group, abnormal expression of
   NAD^ +-consuming enzymes (CD38, SIRT4, BST1, and NNMT) and calcium
   signaling-related genes (CAMK1D, CAMK1, CALML4, CACNB1, and CACNB3), as
   well as altered concentrations of NAD+,
   N-methyl-2-pyridone-5-carboxamide, and cADPR were observed in the IUGR
   group. Meanwhile, the above DEGs and DMs were involved in nicotinate
   and nicotinamide metabolism, aldosterone synthesis and secretion, as
   well as the oxytocin signaling pathway, which were significantly
   enriched in the IUGR group. Recent evidence suggests that oxytocin
   signaling and the renin–angiotensin–aldosterone system (RAAS) are
   closely related to glucose uptake and lipid utilization, whereas
   dysfunction of oxytocin and the RAAS may lead to ROS imbalance and
   contribute to the pathogenesis of insulin resistance and dyslipidemia
   [[105]30,[106]31]. Therefore, the molecular mechanism of the
   dysregulated intracellular NAD^+ levels observed in the livers of IUGR
   piglets may involve the disruption of calcium homeostasis, leading to
   the excess production of ROS.

   Furthermore, in this study, abnormal regulation of fatty acids was
   observed in IUGR piglets. Linoleic acid (LA) metabolism and unsaturated
   fatty acid biosynthesis pathways were significantly enriched in the
   IUGR group, coupled with a decreased concentration of long-chain (≥C20)
   polyunsaturated fatty acids (LC-PUFA), such as EPA and AA. Long-chain
   PUFA is an important component of all cell membranes, especially AA and
   EPA, which are important precursors for the synthesis of first
   messengers and eicosapanoic acid compounds, and an imbalance in the
   n-3/n-6 ratio in LC-PUFAs is associated with metabolic syndrome
   [[107]32]. Moreover, AA and its primary metabolites play an important
   role in the pro-inflammatory process and increase the ROS generation
   [[108]33]. EPA is involved in reducing lipid accumulation and
   alleviating pathology in the liver [[109]34]. In addition, LA and
   α-linolenic acid (ALA) are precursors for the synthesis of LC-PUFA. The
   synthesis process from PUFA to LC-PUFA needs the synergistic activities
   of fatty acid desaturase (FADS) and elongase (ELOVL) [[110]35,[111]36].
   Moreover, alterations involving the LC-PUFA synthesis pathway causes an
   imbalance in the n-3/n-6 ratio and lipid peroxidation, which also leads
   to the production of ROS, subsequently damaging cellular functions
   [[112]37]. Previous studies have found that abnormal expression of
   ELOVL2 and FADS2 in liver may lead to membrane fatty acid remodeling
   and impaired membrane lipid homeostasis [[113]38]. In this result, the
   mRNA expression levels of ELOVL2, FADS2, ACOT7, CYP2C, CYP4A, PTGS1,
   ALOX5, and GGT5, which are involved in EPA and AA metabolism, were also
   altered in the livers of IUGR piglets. In addition, we also found
   increased hepatocyte lipid droplets in IUGR piglets, combined with av
   increased triglyceride content as reported in previous studies
   [[114]12], which may be due to the imbalance of the n-3/n-6 ratio.
   Moreover, as the final products of lipid peroxidation, MDA is the main
   marker of superoxide free radicals [[115]39]. High concentration of MDA
   was observed in the livers of IUGR piglets, suggesting that fatty acid
   metabolism disorder contributes to excessive oxidized stress in the
   liver of IUGR piglets.

   Compared with those in the NBW group, the levels of SOD, CAT, GSH-Px,
   T-AOC, and GSH, all of which are antioxidant enzymes that eliminate
   ROS, were significantly decreased in the IUGR group, suggesting that
   the antioxidative abilities of the liver in IUGR piglets were impaired.
   In particular, GSH is an important scavenging molecule synthesized from
   glutamate, glycine, and cysteine, which protects cells from oxidation
   and maintains redox homeostasis [[116]40]. From our results, the DEGs
   and DMs involved in glycine, serine, and threonine metabolism, as well
   as cysteine and methionine metabolism, were significantly enriched in
   the IUGR group. These metabolic pathways are involved in the supply of
   carbon sources in one-carbon metabolism and GSH production. In these
   pathways, the gene expression levels involved in de novo serine
   synthesis (PHGDH, PSAT1, and PSPH), and the glycine cleavage system
   (GNMT, ALAS2, DMGDH, BHMT, GATM, GCAT, and AGXT2), as well as
   dimethylglycine (DMG), were dysregulated in the livers of IUGR piglets.
   In addition, serine metabolism fuels the methionine salvage pathway,
   which is the main mechanism of synthesizing of the methyl donor
   S-adenosyl methionine (SAM). Subsequently, SAM provides methyl groups
   for epigenetic methylation and is metabolized to homocysteine (Hcy),
   which can enter the trans-sulfuration pathway to produce cysteine and
   finally GSH [[117]41]. According to our results, the DNA
   methyltransferases DNMT1 and DNMT3b were also downregulated in the
   livers of IUGR piglets. Therefore, all the dysregulation of DGEs and
   DMs involved in the one-carbon cycle in IUGR piglets may be one
   mechanism by which IUGR influences hepatic GSH production.

   Moreover, excessive accumulation of sugar alcohol derivatives such as
   galactitol, sorbitol, and inositol was observed in the liver of IUGR
   piglets, all of which are involved in galactose metabolism. Galactose
   is normally converted into glucose through the Leloir pathway and then
   enters the glycolysis pathway or is stored as glycogen in the liver
   [[118]42]. The key enzyme GALM-catalyzed galactose isomerization is the
   first step in this process [[119]43]. Abnormal regulation of GALM in
   this metabolic process affects galactose conversion, resulting in the
   accumulation of galactose and its metabolic derivatives [[120]44]. In
   our results, lower expression of GLAM, as well as higher levels of
   galactitol, sorbitol, and inositol, revealed that the galactose
   conversion pathway is blocked in the liver of IUGR piglets. It has been
   reported that accumulated galactitol may lead to free radical build-up,
   disrupting normal osmotic pressure and leading to redox imbalance in
   the liver [[121]45]. In addition, inositol as a structural basis for
   secondary messengers in eukaryotic cells, is important in a variety of
   physiological processes such as intracellular growth, membrane
   biogenesis, and signal transmission [[122]46]. Although inositol can be
   obtained from dietary supply, the amount of endogenous inositol
   synthesized in the liver is far higher than that ingested through diet.
   The synthesis of inositol begins with the isomerization of
   glucose-6-phosphate (glucose-6P) to inositol 3-phosphate (IN3P) by
   ISYNA1, followed by dephosphorylation to free inositol [[123]47].
   Therefore, in this study, the higher expression of ISYNA1 mRNA may be
   the reason for the higher levels of inositol in livers of IUGR piglets.
   Clinically, altered concentration of inositol in plasma or urine is
   related to insulin resistance and diabetes, and a higher concentration
   of inositol has been proposed as a marker of IUGR in both humans
   [[124]48] and pigs [[125]49]. This is because inositol responds to
   insulin stimulation in the form of inositol phosphoglycans (IPGs) in
   liver tissue; decreased inositol availability or IPGs may lead to
   increased inositol circulation in insulin-resistant animals or humans
   [[126]46,[127]50]. Furthermore, it has been reported that increased
   inositol in fetuses with IUGR may reflect decreased glucose metabolic
   efficiency, which ultimately affects insulin secretion and fetal growth
   [[128]51]. These results may suggest that galactose and its metabolites
   deteriorate liver energy homeostasis and cause oxidative stress in IUGR
   piglets ([129]Figure 7).

Figure 7.

   [130]Figure 7
   [131]Open in a new tab

   Summary of the DEGs and DMs were involved in a variety of metabolic
   abnormalities induced by oxidative stress in the liver of IUGR piglets.
   Abbreviations of DEGs identified by the transcriptomics analysis are
   shown in italics with red (high expression in IUGR group) or green
   (high expression in NBW group). DMs identified by metabolomics analysis
   are indicated either in red (highly expressed in the IUGR group) or
   green (highly expressed in the NBW group) color. Abbreviations: SAM,
   S-adenosylmethionine; SAH, S-adenosylhomocysteine; Hcy, homocysteine;
   LA, linolenic acid; ALA, alpha-linolenic acid; GLA, gamma-linolenic
   acid; EPA, eicosapentaenoic acid; NAD^+, nicotinamide adenine
   dinucleotide; 3PG, 3-phospho glycerate; cADPR, cyclic ADP ribose; ROS,
   reactive oxygen species; SAM, S-adenosyl methionine; Hcy, homocysteine;
   IN3P, inositol 3-phosphate; Met, methionine; SAH,
   s-adenosylhomocysteine.

5. Conclusions

   In summary, the current study provides evidence that differences in
   gene expression and metabolites between the livers of IUGR and NBW
   piglets are involved in a variety of metabolic abnormalities, including
   mitochondrial dysfunction, imbalance of fatty acid composition,
   disruption to sources of one-carbon unit supply, and abnormal galactose
   conversion, thereby enhancing hepatic oxidative stress. Furthermore,
   oxidative stress status and hepatocyte damage were confirmed in the
   liver of IUGR piglets through the detection of antioxidant indices
   combined with morphological characteristics of the liver of IUGR
   piglets. Taken together, these data provide a detailed reference for
   understanding the metabolic characteristics of the liver in IUGR
   piglets and indicate that the mechanism of disordered metabolism in
   IUGR piglets might be associated with increased hepatic oxidative
   stress which ultimately affects the phenotypic characteristics of IUGR
   animals.

Acknowledgments