Introduction

Intrauterine growth restriction (IUGR), stemming from maternal, placental, fetal, or genetic factors, is defined as a rate of fetal growth that is below the expected norm for a specific child per race and gender of the fetus. An estimated 30 million newborns (23.8%) suffer from IUGR per year (Sharma et al., 2016). IUGR manifests as morphological and functional changes alongside low birth weight. These changes increase the susceptibility to metabolic diseases like type-2 diabetes, obesity, hypertension, and cardiovascular diseases (Fowden et al., 2006; Godfrey et al., 2010; Oke et al., 2021) notwithstanding achieving catch-up growth. Intrauterine programming delineates the process by which early insults during crucial developmental stages yield long-lasting morphological and functional alterations (Fowden et al., 2006). The programming of adult diseases may originate from inadequate intrauterine conditions due to alterations in the development or progression of critical endocrine and metabolic pathways. This concept is defined as the ‘fetal origins hypothesis’ (Barker 2007).

Animal models of IUGR are invaluable tools to question the genetic, molecular and cellular events that determine fetal growth and development. The findings obtained from animal models bear similarities to those recorded in humans born small for gestational age and are noteworthy due to established correlation between poor fetal growth and emergence of adult diseases (Vuguin 2007). Bilateral uterine artery ligation is one of the various interventions used for creating inadequate intrauterine condition to induce growth retardation (Wigglesworth 1964). Caloric restriction, low protein diet, iron deficiency, utero-placental embolization, genetic interventions are among the other IUGR methods (Vuguin 2007). Similar to humans, fetal rats in this model suffer from transient hypoglycemia, hypoxia, and acidosis and demonstrate the long-term effects of the altered intrauterine milieu (Simmons et al., 1992; Ogata et al.1986).

Fetal tissues are extremely vulnerable to internal or external stressors. Several genes from the mother, the fetus, and the placenta are implicated in IUGR (Sharma et al., 2017). Apoptosis, autophagy, endoplasmic reticulum (ER) stress, oxidative stress, inflammation, and mitochondrial dysfunction are suggested to contribute to the metabolic alterations observed in IUGR (Oke et al., 2021; Gurugubelli Krishna et al., 2018; Rashid et al., 2018). Due to cellular stress and programmed cell death induced by genetic or epigenetic factors, individuals born with IUGR are predisposed to metabolic disorders. The liver, pancreas, adipose tissue, and muscle tissue exhibit high susceptibility to oxidative stress and mitochondrial dysfunction in individuals with a history of IUGR (Oke et al., 2021; Pendleton et al., 2021). Emerging evidence suggests the liver may represent one of the candidate organs undergoing structural, functional, and epigenetic changes in response to unfavorable intrauterine conditions (Cianfarani et al., 2012). Hepatic as well as pancreatic oxidative damage potentially contribute to the development of insulin resistance and diabetes (Oke et al., 2021).

Limited studies investigating the IUGR-induced changes in the hepatic metabolomics and related long-term metabolic effects have been performed (Liu et al., 2022, Ponsuksili et al., 2022). Currently data on the hepatic involvement in the developmental origins of metabolic diseases are limited leaving numerous unanswered questions regarding the primary and secondary pathways implicated. Although some molecular pathways have been hypothesized on the emergence of diseases linked to IUGR, significant gaps in understanding persist. Through an examination of liver programming, which raises metabolic risk in IUGR patients even when catch-up growth is present, this research attempts to shed light on the molecular mechanisms underlying this condition. With a wide range of techniques applied, such as immunohistochemistry, biochemistry, Western blotting, and metabolomics analyses, our goals are to identify important metabolic pathways implicated in the pathophysiology of IUGR, provide insights into diseases presumed to be linked to IUGR, and clarify putative metabolite markers of IUGR- related metabolic disorders. We anticipate that the results of this study will provide a solid platform for future research into the mechanisms behind IUGR-related disorders.

Results

Body weights

The mean body weights of the pups from the IUGR and Sham groups were similar in the 2^nd week. However, the mean body weights of the pups from IUGR in the 4^th (p<0.05) and 8^th weeks were higher than those of the Sham group in the corresponding weeks. Notably, the catch-up growth was evident as early as the 2nd postnatal week (Fig. 1).

Fig. 1. Mean body weights of the groups at the end of the 2^nd, 4^th and 8^th weeks.

* p<0.05 vs Sham group.

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Fig. 1. Mean body weights of the groups at the end of the 2^nd, 4^th and 8^th weeks.

* p<0.05 vs Sham group.

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Histopathological analyses

The histopathological analyses revealed some differences between the IUGR and Sham groups. In the sections from the 2-week-old rats in the Sham group, classical hepatic lobules were not distinctly discernible, with central veins and portal areas appearing randomly arranged compared to healthy adult livers. Moreover, the portal areas lacked conspicuous connective tissue, hindering the recognition (Fig. 2 A,B). Evidence of ongoing hemopoiesis was observed, characterized by dispersed hematopoietic cell groups (Fig. 2A). Similarly, sections from 2-week-old rats in the IUGR group exhibited indistinct classical hepatic lobules with some areas devoid of central veins and the elements of the portal triad including portal vein, portal artery, and bile duct. These areas were occupied by rows of hepatocytes (Fig. 2C). Prominent vacuolization and perinuclear edema were detected in these sections. Additionally, larger hematopoietic cell groups were dispersed compared to the Sham group (Fig. 2D).

Fig. 2. Legend title.

Sections obtained from 2-week-old Sham group (A,B) and 2-week-old IUGR group (C,D). (A,B) Classical hepatic lobule organization is absent (asterisks). Central veins (CV) and hardly recognized portal areas (white circle) are organized randomly. Small hematopoietic cell groups are observed (yellow circle). (C,D) Classical hepatic lobules are not obvious (asterisks). Portal areas generally are barely recognizable since connective tissue is absent or very thin (C). Hematopoietic cell groups at the portal area are circled (yellow circle) (D). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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Fig. 2. Legend title.

Sections obtained from 2-week-old Sham group (A,B) and 2-week-old IUGR group (C,D). (A,B) Classical hepatic lobule organization is absent (asterisks). Central veins (CV) and hardly recognized portal areas (white circle) are organized randomly. Small hematopoietic cell groups are observed (yellow circle). (C,D) Classical hepatic lobules are not obvious (asterisks). Portal areas generally are barely recognizable since connective tissue is absent or very thin (C). Hematopoietic cell groups at the portal area are circled (yellow circle) (D). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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In the sections from 4-week-old rats in the Sham group, liver histology exhibited greater similarity to that of healthy adult liver. Classical hepatic lobules were discernible, and the connective tissue elements of portal areas, although not abundant, were detectable (Figs. 3 A,B). Hemopoiesis was still ongoing within small hematopoietic cell groups (Fig. 3A). Similarly, sections from 4-week-old rats in the IUGR group displayed discernible classical hepatic lobules (Figs. 3 C,D), although some areas were still solely occupied by rows of hepatocytes without central veins or portal spaces. Notably, perinuclear edema was obvious in the cytoplasm of the hepatocytes located in these areas (Fig. 3C). Hemopoiesis persisted, with larger hematopoietic cell groups compared to Sham (Fig. 3D).

Fig. 3. Liver histology of 4-week-old groups.

Sections obtained from 4-week-old Sham group (A,B) and of 4-week-old IUGR group (C,D). (A,B) Classical hepatic lobules containing central vein (CV) at the center and portal area (white circle) at the periphery are predictable. Small hematopoietic cell groups are observed (yellow circles). (C,D) Classical lobule organization is not fully developed. Although classical hepatic lobules containing a central vein (CV) at the center and portal area (white circle) at the periphery are predictable, some areas are occupied by only randomly distributed hepatocytes (asterisks). Especially in the cytoplasm of the hepatocytes located in those areas, perinuclear edema is obvious (arrows). Larger hematopoietic cell groups than those of the Sham group are observed (yellow circle). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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Fig. 3. Liver histology of 4-week-old groups.

Sections obtained from 4-week-old Sham group (A,B) and of 4-week-old IUGR group (C,D). (A,B) Classical hepatic lobules containing central vein (CV) at the center and portal area (white circle) at the periphery are predictable. Small hematopoietic cell groups are observed (yellow circles). (C,D) Classical lobule organization is not fully developed. Although classical hepatic lobules containing a central vein (CV) at the center and portal area (white circle) at the periphery are predictable, some areas are occupied by only randomly distributed hepatocytes (asterisks). Especially in the cytoplasm of the hepatocytes located in those areas, perinuclear edema is obvious (arrows). Larger hematopoietic cell groups than those of the Sham group are observed (yellow circle). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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In the sections of 8-week-old rats in the Sham group, liver histology closely resembled that of a healthy adult liver. Classical hepatic lobules were easily identifiable, and the connective tissue connective tissue of portal areas was detectable (Figs. 4 A,B). Similarly, sections from 8-week-old rats in the IUGR group displayed predictable classical hepatic lobules, yet some areas remained occupied by only rows of hepatocytes with no sign of central vein or portal elements (Figs. 4 C,D). The distribution of central veins and the portal areas appeared more random than typical lobule organization. Perinuclear edema was rarely detected (Fig. 4D).

Fig. 4. Liver histology of 8-week-old groups.

Sections obtained from 8-week-old Sham group (A,B) and the 8-week-old IUGR group (C,D). (A,B) Classical hepatic lobules containing a central vein (CV) at the center and portal area (white circle) at the periphery are obvious. (C,D) Classical hepatic lobules are not well organized. Central veins are generally randomly distributed. Some areas are occupied by only hepatocyte rows but not central vein or portal elements (asterisks). Especially in the cytoplasm of the hepatocytes located in those areas, perinuclear edema is obvious (arrows). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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Fig. 4. Liver histology of 8-week-old groups.

Sections obtained from 8-week-old Sham group (A,B) and the 8-week-old IUGR group (C,D). (A,B) Classical hepatic lobules containing a central vein (CV) at the center and portal area (white circle) at the periphery are obvious. (C,D) Classical hepatic lobules are not well organized. Central veins are generally randomly distributed. Some areas are occupied by only hepatocyte rows but not central vein or portal elements (asterisks). Especially in the cytoplasm of the hepatocytes located in those areas, perinuclear edema is obvious (arrows). Hematoxylin and eosin staining technique. Scale bars, 100 µm.

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The mean numbers of central veins and portal areas were non-uniform between the Sham and IUGR groups indicating disruption of classical hepatic lobules. IUGR altered the mean numbers of central veins and portal areas. Specifically, the mean number of the central veins of the 4- and 8-week- old IUGR group was higher than that of the 4- and 8-week-old Sham group (p<0.001, p<0.05; respectively). Additionally, the mean number of portal areas in the 2-week-old IUGR group was higher than that in the 2-week-old Sham group (p<0.001) (Fig. 5).

Fig. 5. Mean numbers of central veins (CV) and portal areas of the groups.

* p<0.001, ** p<0.05.

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Fig. 5. Mean numbers of central veins (CV) and portal areas of the groups.

* p<0.001, ** p<0.05.

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Western blotting analyses

The tissue RIP3/GAPDH level in the 2-week-old IUGR group was found to be lower than that in the 2-week-old Sham group (p<0.005). Conversely, the tissue NF-kB level of the 2- and 8-week-old IUGR groups was lower than the corresponding Sham groups (p<0.005, p<0.001; respectively). However, the NF-kB level in the 4-week-old IUGR group was higher than that in the 4-week-old Sham group (p<0.05). These findings suggest alterations in necroptosis and inflammation patterns between the equivalent weeks of the Sham and IUGR groups, underscoring potential differences in underlying molecular mechanisms associated with IUGR (Fig. 6).

Fig. 6. Tissue RIP3/GAPDH and NF-kB levels of the groups.

* p<0.005, ** p<0.05, *** p<0.001.

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Fig. 6. Tissue RIP3/GAPDH and NF-kB levels of the groups.

* p<0.005, ** p<0.05, *** p<0.001.

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Metabolomics analyses

Upon examining the results of the metabolomics in the 2-week-old IUGR and Sham groups, significant alterations were uncovered. Illustrated in the volcano plot graph (Fig. 7A), the IUGR group exhibited elevated levels of succinate, acetoacetate, isoleucine, cytidine, and formate, while displaying decreased levels of citrate, isocitrate, glucose, glucose-1-phosphate, maltose, and homocysteine to the Sham group. Utilizing Principal Component Analysis (PCA), which is adept at discerning patterns within high-dimensional data, the complexity of the data was effectively mitigated. The PCA score plots derived from NMR spectroscopy -analyzed tissue samples convincingly demonstrated group segregation and intra-group data clustering, accounting for approximately 63.15% of the variables. As depicted in Fig. 7B, the first principal component (PC1) accounted for 47.3% of the variance, while the second principal component (PC2) accounted for 15.8% of the variance. Further insights were gleaned through orthogonal partial least square data analysis (OPLS-DA), a method facilitating detailed examination of multivariate data. The OPLS-DA graph (Fig. 7C) affirmed successful differentiation between the IUGR and Sham groups. Additionally, the VIP score graph (Fig. 7D) showcased metabolites such as succinate, glucose, and acetoacetate as pivotal in distinguishing between groups. These findings underscore the utility of metabolomics in unraveling metabolic distinctions associated with IUGR.

Fig. 7. The metabolomics results of the 2-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated and depleted metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of NMR dataset. The IUGR group is depicted as red, and the Sham group is represented by green data points. Additionally, T2 Hotelling's ellipses were employed to define a 95% confidence level for the IUGR group, further highlighting its separation from the Sham group. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D) The OPLS-DA graph shows the metabolites that exhibited the greatest contribution to the observed separation.

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Fig. 7. The metabolomics results of the 2-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated and depleted metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of NMR dataset. The IUGR group is depicted as red, and the Sham group is represented by green data points. Additionally, T2 Hotelling's ellipses were employed to define a 95% confidence level for the IUGR group, further highlighting its separation from the Sham group. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D) The OPLS-DA graph shows the metabolites that exhibited the greatest contribution to the observed separation.

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Upon analyzing tissue samples extracted from 4-week-old rat livers, notable disparities emerged in the levels of acetoacetate, ethanol, pyruvate, cadaverine, and ethanolamine, which were found to be significantly elevated in the IUGR group compared to the Sham group. The volcano plot graph depicting statistically significant differential metabolites and fold change is illustrated in Fig. 8A. While a distinct separation was not readily apparent through PCA (Fig. 8B), the application of OPLS-DA proved instrumental in delineating the IUGR and Sham groups based on their metabolic profiles (Fig. 5C). OPLS-DA results depicted in Fig. 8C underscore the efficacy of this analysis in discerning the metabolic distinctions between the two groups. Fig. 8D highlights the metabolites most closely associated with this discrimination.

Fig. 8. The metabolomics results of the 4-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of the NMR dataset shows that the metabolite profiles are similar. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D. The OPLS-DA graph shows the metabolites that made the most substantial contributions to the observed separation.

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Fig. 8. The metabolomics results of the 4-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of the NMR dataset shows that the metabolite profiles are similar. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D. The OPLS-DA graph shows the metabolites that made the most substantial contributions to the observed separation.

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Upon analyzing the NMR spectroscopy dataset obtained from 8-week-old rats, a distinct group of metabolites including acetoacetate, niacinamide, valine, phenylalanine, glutamate, cytidine, glucose, uracil, cadaverine, isoleucine, 1-methyhistidine, methionine, glucuronate, glycine, and fumarate exhibited an exclusive increase in the IUGR group (Fig. 9A). Despite the lack of a clear differentiation between the groups through PCA (Fig. 9B), successful distinction was achieved through OPLS-DA (Fig. 9C). The OPLS-DA illustrated a discernible separation, indicative of distinct metabolic profiles between the IUGR and Sham groups. Further examination revealed the metabolites contributing most significantly to this observed separation (Fig. 9D), underscoring their pivotal role in discriminating between the IUGR and Sham groups. These findings highlight the efficacy of OPLS-DA in uncovering metabolic distinctions associated with IUGR and identifying potential biomarkers warranting further investigation.

Fig. 9. The metabolomics results of the 8-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of the NMR dataset represents the principal components of the analysis. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D) The OPLS-DA graph shows the metabolites that made the most substantial contributions to the observed separation.

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Fig. 9. The metabolomics results of the 8-week-old Sham and IUGR group.

(A) Volcano plot graph shows elevated metabolomics in the IUGR group versus the Sham group. (B) PCA 2D score-plot of the NMR dataset represents the principal components of the analysis. (C) The OPLS-DA graph shows a separation between the IUGR and Sham groups. (D) The OPLS-DA graph shows the metabolites that made the most substantial contributions to the observed separation.

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Meta-analyses

To conduct the meta-analysis, all samples were stratified based on their respective groups: IUGR and Sh. The meta-analysis involved samples from animals at three different weeks of age. Upon meta-analysis of the values of the 2-week-old group, 25 significantly different metabolites were identified. In both the 4-week-old and 8-week-old groups, two metabolites exhibited significant differences (Fig. 10A). Of these metabolites, a total of 25 metabolites were detected specifically in the 2-week-old group, with 23 being unique being unique to this age group. The identified metabolites in the 2-week-old group included 3-amino isobutyrate, acetate, acetoacetate, aspartate, betaine, cadaverine, citrate, cytidine, ethanolamine, formate, glucose, glucose-1-phosphate, glutamate, glycine, homocysteine, isocitrate, maltose, phenylalanine, proline, pyruvate, succinate, threonine, tyrosine, uracil, and valine. Among these metabolites, acetoacetate emerged as the most distinct potential metabolite for selecting IUGR independently of the animal’s age. Its discriminatory ability remained consistent across different age groups, indicating its potential as a reliable marker for distinguishing IUGR from non-IUGR conditions. The expression patterns of acetoacetate and valine comparing all 3 groups are depicted in Fig. 10B.

Fig. 10. Results of a meta-analysis of the values of the metabolites of all groups.

(A) Distinctive metabolites identified for discrimination between IUGR and Sham groups across all ages. The blue arrow indicates there was one metabolite to distinct Sham or IUGR group among all ages. (B) Expression patterns of two significant metabolites according to meta-analysis.

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Fig. 10. Results of a meta-analysis of the values of the metabolites of all groups.

(A) Distinctive metabolites identified for discrimination between IUGR and Sham groups across all ages. The blue arrow indicates there was one metabolite to distinct Sham or IUGR group among all ages. (B) Expression patterns of two significant metabolites according to meta-analysis.

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Following the comprehensive analysis of merged data from three different age groups, a volcano plot graph depicting differential metabolite levels between the IUGR group and the Sham group was generated (Fig. 11A). Notably, acetoacetate, isoleucine, succinate, valine, niacinamide, formate, and pyruvate exhibited increased levels in the IUGR groups compared to the Sham groups. Conversely, serine, nicotinurate, maltose, homocysteine, and citrate levels were decreased in the IUGR groups versus the Sham groups. The heatmap presented in Fig. 11B illustrates the relative abundance of metabolites across the IUGR and Sham groups. Each row represents a metabolite, while each column corresponds to a sample. The color gradient in the heatmap reflects the magnitude of the variable’s abundance with red shades indicating higher values and blue shades indicating lower values. Although PCA did not yield a clear distinction between the groups (Fig. 12A), a discernible differentiation was successfully achieved through OPLS-DA (Fig. 12B). Further analysis pinpointed the metabolites contributing most significantly to the observed separation (Fig. 12C).

Fig. 11. Differential metabolite levels of the Sham and IUGR groups.

(A)Volcano plot graph of all samples separated according to Sham and IUGR groups. (B) Metabolite abundance Heatmap of IUGR and Sham groups.

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Fig. 11. Differential metabolite levels of the Sham and IUGR groups.

(A)Volcano plot graph of all samples separated according to Sham and IUGR groups. (B) Metabolite abundance Heatmap of IUGR and Sham groups.

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Fig. 12. Further analysis results on distinction between groups.

(A) PCA analysis visualization of all samples. No distinctive pattern in metabolites is observed. (B) The OPLS-DA graph was obtained with a dataset of all samples. A separation between the IUGR and Sham groups was observed. (C) The metabolites that made the most substantial contributions to the observed separation are listed.

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Fig. 12. Further analysis results on distinction between groups.

(A) PCA analysis visualization of all samples. No distinctive pattern in metabolites is observed. (B) The OPLS-DA graph was obtained with a dataset of all samples. A separation between the IUGR and Sham groups was observed. (C) The metabolites that made the most substantial contributions to the observed separation are listed.

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Enrichment and pathway analyses

With the comprehensive dataset analysis, data of all samples were evaluated separately. Fig. 13A presents the top 25 biological processes that were found to be most significantly affected in the enrichment analysis obtained with merged data. This analysis employs statistical methods to assess the relationship between metabolites within the data set and relevant pathways. The identified biological processes represent key functional categories that exhibit significant associations with the metabolite data. The inclusion of these processes provides valuable insights into the potential biological mechanisms underlying the observed metabolic changes. Enrichment analysis revealed ketone body metabolism, butyrate metabolism, and methionine metabolism as the most influenced pathways. The most affected pathways and related metabolites revealed by the pathway analysis are depicted in Figs. 13A and 13B.

Fig. 13. The most significantly affected biological processes.

(A) Enrichment analysis conducted with the merged dataset. The top 25 biological processes that were found to be most significantly affected are listed. (B) Visualization of pathway analysis conducted with merged data. The numbers of the most influenced pathways are shown in Figure as follows: 1. Galactose metabolism, 2. Valine, leucine and isoleucine degradation, 3. Valine, leucine, and isoleucine biosynthesis, 4. Citrate cycle (TCA cycle, 5. Propanoate metabolism, 6. Glycerolipid metabolism, 7. Cysteine and methionine metabolism, 8. Tyrosine metabolism, 9. Aminoacyl-tRNA biosynthesis, 10. Butanoate metabolism, 11. Glutathione metabolism, 12. Glycolysis / Gluconeogenesis.

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Fig. 13. The most significantly affected biological processes.

(A) Enrichment analysis conducted with the merged dataset. The top 25 biological processes that were found to be most significantly affected are listed. (B) Visualization of pathway analysis conducted with merged data. The numbers of the most influenced pathways are shown in Figure as follows: 1. Galactose metabolism, 2. Valine, leucine and isoleucine degradation, 3. Valine, leucine, and isoleucine biosynthesis, 4. Citrate cycle (TCA cycle, 5. Propanoate metabolism, 6. Glycerolipid metabolism, 7. Cysteine and methionine metabolism, 8. Tyrosine metabolism, 9. Aminoacyl-tRNA biosynthesis, 10. Butanoate metabolism, 11. Glutathione metabolism, 12. Glycolysis / Gluconeogenesis.

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The visualization of results and enrichment analysis for the subgroups is presented in Fig. 14. Fig. 14A highlights the most affected pathways including the oxidation of branched-chain fatty acids, phytanic acid peroxisomal oxidation, and valine leucine and isoleucine degradation in the 2-week-old group. These pathways demonstrate significant associations with the metabolite profiles observed in this group, indicating notable alterations in these metabolic pathways during early development. In the 4- week-old group betaine metabolism, tryptophan metabolism, and ubiquinone metabolism were found to be the most affected pathways (Fig. 14B). In the 8-week-old group, propanoate metabolism, valine, leucine, and isoleucine degradation, and ketone body metabolism were detected as the most influenced pathways (Fig. 14C). These pathway perturbations highlight the distinctive metabolic signatures present in the aged 8-week group. Overall, the visualization of these results offers valuable insights into the specific pathway dysregulations within each age group, contributing to a comprehensive understanding of the metabolic alterations in this study and providing potential targets for further investigation into mechanisms underlying IUGR.

Fig. 14. Enrichment analysis of the groups.

Identification of dysregulated pathways in IUGR groups for aged (A) 2 weeks, (B) 4 weeks, (C) 8 weeks.

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Fig. 14. Enrichment analysis of the groups.

Identification of dysregulated pathways in IUGR groups for aged (A) 2 weeks, (B) 4 weeks, (C) 8 weeks.

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Discussion

Intrauterine growth retardation poses a significant risk factor for various adult metabolic syndromes, including obesity, hypertension, diabetes, and cardiovascular diseases (Godfrey et al., 2010; Oke et al., 2021; Dessì et al., 2011; Lui et al., 2014). The liver emerges as a pivotal organ susceptible to programming, undergoing structural, functional, and epigenetic alterations in response to adverse intrauterine conditions (Cianfarani et al., 2012). Mechanisms such as apoptosis, autophagy, endoplasmic reticulum stress, inflammation, oxidative stress, and mitochondrial dysfunction are implicated in driving changes within metabolic organs (Oke et al., 2021; Gurugubelli Krishna et al., 2018; Rashid et al., 2018). Exposure to uteroplacental insufficiency has been associated with alterations in the gene expression of enzymes involved in hepatic energy production (Lane et al., 1996), leading to decreased hepatic oxidative phosphorylation (Ogata et al., 1990) and impacting glucose transport (Lane et al., 1996). While both humans and rats exhibit long-term metabolic consequences following IUGR, there are specific species- specific differences in how these alterations manifest. Humans often experience more complex interactions due to lifestyle factors, whereas rats serve as controlled models to explore specific pathways and mechanisms. However, the similarities in insulin resistance, obesity, lipid metabolism, mitochondrial dysfunction, and epigenetic changes between both species make rats valuable models for studying the underlying mechanisms of IUGR-related metabolic dysfunction in humans.

According to the histopathological findings of our study, it is evident that IUGR significantly impairs liver development. The classical organization of the hepatic lobule was disrupted, accompanied by notable vacuolization and perinuclear edema. These observations align with previous studies by Zhang et al.,Zhang et al., 2018; Zhang et al., 2020) which report similar IUGR-induced vacuolation and parenchymal disorganization. Furthermore, our study revealed a sustained increase in caspase-8 + cell numbers, persisting even in the postnatal 8 weeks (p<0.005). This finding corroborates with prior research indicating elevated hepatocyte apoptosis rates in IUGR conditions (Lui et al., 2014; Zhang et al., 2018; Zhang et al., 2020; Zhang et al., 2021). Moreover, the increased tissue RIP3 level in the 2-week- old IUGR group (p<0.005) suggests the involvement of necroptosis, at least during the early postnatal period in rats affected by IUGR. This implies a broader spectrum of cell death mechanisms at play in response to IUGR-induced stress. The dysregulation observed in tissue NF-kB levels, a central mediator in inflammation, stress response, cell differentiation, proliferation, and cell death, further underscores abnormalities in hepatocyte metabolism induced by IUGR. Consistent with our findings, Zhang et al., (Zhang et al., 2020) reported elevated hepatic NF-Kb p65 levels along with apoptosis antigen-1, Bax, cytochrome-c, and TNF-α levels in the liver of IUGR lambs.

Studies have consistently reported increased oxidative stress in both the serum and the livers of newborns affected by IUGR (Zhang et al., 2018; Zhang et al., 2021; Saker et al., 2008; Wang et al., 2022). This heightened hepatic oxidative stress is often attributed to an imbalance between the accumulation of free radicals and the production of antioxidant enzymes (Zhang et al., 2020; Wang et al., 2008). Notably, the levels of antioxidant enzymes are lower in IUGR newborns compared to their healthy counterparts (Smith et al., 1993; Sullivan et al., 1988). Oxidative stress, a pivotal factor in the pathogenesis of IUGR, is closely linked to uteroplacental insufficiency, which contributes to early liver injury (Biri et al., 2007). However, in our study, we did not find significant evidence of increased oxidative stress in IUGR groups except for a decrease in TAS level in the 2-week-old IUGR group (p<0.05). We observed reductions in TThiol and NThiol levels (p<0.05, p<0.005; respectively) which are organic compounds essential for protection against oxidative damage. Interestingly, by the 8^th week, the TThiol level had normalized, while the NThiol level had increased instead (p<0.05). These findings suggest a potential restoration of the oxidative balance in the liver over time, despite previous indications of oxidative stress in fetal livers affected by IUGR.

In our study, we observed intriguing changes in hepatocyte physiology reflected in serum levels of key enzymes such as IDH and MDH, tissue levels of GDH, G-6-PD, IDH, fumarase, and minerals such as zinc, and copper. Notably, while the levels of GDH, G6PD, and NADP-IDH normalized in the 8-week-old IUGR group, there were distinct alterations in serum and tissue levels of some other enzymes and minerals. For instance, serum IDH and MDH levels decreased whereas tissue NAD-IDH, fumarase, zinc, and copper levels were increased. Some of these enzymes play crucial roles in the citric acid cycle, a series of biochemical reactions essential for energy release through the oxidation of acetyl-CoA. Previous research has linked IUGR with compromised mitochondrial function, characterized by reduced ATP content, diminished complex activity, and downregulated expression of genes related to mitochondrial biogenesis and energy metabolism (Cheng et al., 2021). Interestingly, our findings in the 8-week-old IUGR group revealed a complex interplay: while serum levels of IDH and MDH levels, principal enzymes of the citric acid cycle, decreased, tissue levels of NAD-IDH, and fumarase, indicative of mitochondrial energy metabolism, increased. This observation is pivotal as energy homeostasis controls tissue growth, with substrate utilization and oxidative phosphorylation playing crucial roles (Krebs 1970; Seppet et al., 2009). Moreover, IUGR newborns often exhibit catch-up growth accompanied, as we detected in our study, by elevated resting energy expenditure (Chessex et al., 1984; Davies et al., 1996) potentially leading to overstimulation of the energy production cycle and the generation of excessive free radicals. The elevated liver copper levels observed in the 8-week-old IUGR group may also be linked to increased mitochondrial metabolism. For the assembly of cuproenzymes like the cytochrome-c oxidase and the antioxidant enzymes, mitochondria collect copper. Copper is essential for mitochondrial dynamics, aiding in energy metabolism, detoxification of reactive oxygen species, and signaling in eukaryotic organisms. Since copper homeostasis is tightly regulated by the liver (Ruiz et al., 2021), significant alterations in copper levels may signify disturbances in liver metabolism. On the other hand, zinc facilitates mitochondrial pyruvate transport, oxidative phosphorylation, and regulation of energy metabolism. In conditions of toxin-induced stress, zinc plays a role in restoring energetic metabolism (Yang et al., 2017). The observed elevation in zinc levels could potentially be a compensatory mechanism to regulate impaired energy metabolism. These findings underscore the intricate interplay between mitochondrial function, energy metabolism, and trace element homeostasis in the context of IUGR, shedding light on potential avenues for further investigation into liver metabolism and its implications for overall health.

In our study, we conducted an extensive analysis of metabolite levels using NMR spectroscopy analysis, aiming to identify potential disease associations linked to these alterations. We observed significant changes in various metabolites within the IUGR groups across different age groups. In the 2-week-old IUGR group, we noted increases in succinate, acetoacetate, isoleucine, cytidine, and formate levels while decreases in citrate, glucose, glucose-1-phosphate, maltose, isocitrate, and homocysteine levels. Interestingly, while most metabolites normalized in the 4-week-old IUGR group, some, like ethanol, pyruvate, cadaverine, and ethanolamine exhibited increased levels. Furthermore, in the 8-week- old IUGR group, acetoacetate levels remained elevated, alongside isoleucine, cytidine, and pyruvate levels were increased. Additionally, exclusive increases were observed in metabolites such as niacinamide, valine, phenylalanine, glutamate, cytidine, glucose, uracil, cadaverine, isoleucine, 1- methyhistidine, methionine, glucuronate, glycine, and fumarate. Overall, acetoacetate, isoleucine, succinate, valine, niacinamide, formate, pyruvate and cadaverine showed notable increases, while serine, nicotinurate, maltose, homocysteine, and citrate levels were decreased in the IUGR group compared to the Sham group. These changes can be indicative of various metabolic and pathological conditions. For example, in both type I and II diabetes, cells may switch to alternative energy sources such as fatty acids, leading to increased ketogenesis and elevated tissue acetoacetate levels (She et al., 2007). The persistent negative effects of IUGR on liver metabolism were evident even in later postnatal stages, despite IUGR puppies achieving growth levels comparable to healthy puppies shortly after birth. Metabolic pathways such as ketone body, butyrate, methionine, galactose, betaine, glycerol metabolisms, valine, leucine, and isoleucine degradation, were significantly induced by IUGR. These pathways remained affected even in the 8^th postnatal week, indicating long-term metabolic alterations associated with IUGR. In contrast to a recent study by Gao et al.,2022), our findings differed in terms of enriched metabolic pathways. According to their study, linoleic acid and galactose metabolism, oxytocin signaling pathway, aldosterone synthesis, and unsaturated fatty acid biosynthesis were significantly enriched. Conversely, the levels of gamma-linolenic acid, arachidonic acid, hydroxy octadecanoic acids, and N-acetyl-D-galactosamine were significantly decreased in the IUGR groups. In our study valine, leucine and isoleucine degradation, ketone body, and butyrate metabolism were among the most affected metabolisms in the whole period and also among the most affected metabolisms according to the persistent abnormalities in the 8-week-old IUGR group. However, alterations in galactose metabolism and fatty acid biosynthesis were consistent between these two studies. Branched-chain amino acids (BCAAs), including valine, leucine, and isoleucine, play essential roles in stress response, energy production, protein synthesis, and overall metabolic health. Dysregulation of BCAAs has been linked to various metabolic diseases, such as insulin resistance (IR), obesity, heart failure, and cancer (Du et al., 2022). The liver, known for its high metabolic efficiency, is crucial in the oxidative decarboxylation of branched-chain keto acids (BCKAs) (Shin et al., 2014), highlighting its significance in metabolic regulation and disease pathogenesis. Elevated concentrations of plasma BCAAs, BCKAs, and carnitine esters have been reported in conditions like IR, obesity, and type-2 diabetes in patients and mice (She et al., 2007).

Accumulation of BCKAs is also associated with glucose intolerance and cardiac insufficiency (East et al., 2021), further underscoring the clinical relevance of BCCA metabolism. Additionally, the ketone bodies (KBs), such as β-hydroxybutyrate, and acetoacetate serve as important alternative energy sources during fasting. Increased levels of acetoacetate, a prominent metabolite observed in our study, suggest alterations in hepatic ketogenesis and metabolic reprogramming. Besides, the increase in hydroxybutyrate levels observed in our study further highlights the dysregulation of ketone body metabolism associated with IUGR. KBs, including acetoacetate and hydroxybutyrate, are synthesized through hepatic ketogenesis and serve as crucial alternative energy sources during periods of fasting or low glucose availability. KBs synthesized by hepatic ketogenesis are converted into acetyl- CoA in extrahepatic tissues, which then enter the citric acid cycle and are oxidized for energy (Hwang et al., 2022). To enter the citric acid cycle, acetyl CoA first condenses with oxaloacetate derived from pyruvate during glycolysis. During periods of low glucose availability, such as fasting or in conditions like diabetes, oxaloacetate, a key intermediate in the citric acid cycle, is preferentially utilized for gluconeogenesis rather than condensing with acetyl-CoA to enter the citric acid cycle (Laffel 1999). As a result, acetyl-CoA is diverted towards KB formation, leading to increased production of KBs. Hepatic ketogenesis interfaces with other metabolic pathways, including fatty acid β-oxidation, the citric acid cycle, and gluconeogenesis (Cotter et al., 2013) reflecting its integral role in metabolic hemostasis. Dysregulations in ketone body metabolism have been implicated in various disease states, including diabetes and heart failure (Hall et al., 1984; Lommi et al., 1997). In these conditions, alterations in KB levels and metabolism contribute to derangements and further exacerbate disease progression.

The findings from our study revealed significant alterations in phenylalanine and tyrosine metabolism, particularly in the 8-week-old IUGR group, suggesting a dysregulation in these pathways associated with IUGR. Galactose, glycerol, and betaine metabolism were among the most affected pathways in the whole postnatal period. Phenylalanine and tyrosine metabolism are closely interconnected, with phenylalanine being metabolized into acetoacetic acid and fumaric acid via tyrosine. Previous research by Tang et al.,Tang et al., 2022) has also reported upregulated phenylalanine and tyrosine metabolisms in the IUGR piglet groups corroborating our findings. Additionally, Faverzani et al.,2021) highlighted the potentially detrimental effects of elevated phenylalanine levels and its keto acid derivatives, including DNA oxidative damage in glial cells. Accumulation of phenylalanine and its organic acid derivatives in tissues and biological fluids has been implicated in various adverse outcomes, including mental retardation (Casey 2013). Furthermore, our study and the metabolomics study of Gao et al. (2022) both identified galactose metabolism as one of the most affected pathways in the IUGR. Galactosemia, a disorder characterized by impaired galactose metabolism, has been associated with liver and kidney diseases, brain damage, and cataracts (Qi et al., 2019).

Conclusion

Our study sheds light on the comprehensive metabolic alterations associated with IUGR in rats. The pups affected by IUGR were involved in a variety of changes including histological features, apoptosis rate, mitochondrial energy metabolism, propanoate metabolism, ketone body metabolism, butyrate metabolism, phenylalanine and tyrosine metabolism, valine, leucine, and isoleucine degradation. Importantly, our findings indicate that despite reaching similar levels of body development as normal-born offspring, rats with IUGR continue to experience ongoing dysregulation in several metabolic pathways. This suggests that the consequences of IUGR extend beyond early postnatal life and may predispose individuals to a range of health issues later in life. Furthermore, our metabolomics analysis provides insights into the potential disease susceptibility of individuals born with IUGR, including obesity, diabetes, heart failure, cancer, mental retardation, kidney and liver diseases, and cataracts. The identification of these metabolic changes underscores the importance of monitoring and managing the long-term health outcomes of individuals affected by IUGR. Overall, our study contributes to a deeper understanding of the hepatic metabolic characteristics associated with IUGR, providing a valuable theoretical framework for further research aimed at elucidating the underlying mechanisms and developing targeted interventions to mitigate the adverse effects of IUGR on long-term health.

Material and Methods

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