Search
Submit Manuscript
Advanced Search
MAP  >  Animal Advances
2024 Volume 1
Article Contents
Abstract
Introduction
Materials and methods
Evaluating of different antioxidants on regulating redox potential ability
Antioxidant assays in vitro
Intervention experiments of different antioxidants in vivo
Rat diarrhea model induced with magnesium citrate
Redox potential measurement
Observation of diarrhea
Determination of T-AOC, SOD, ROS, and MDA in intestine
16S rRNA gene sequencing and processing
Metabolome analysis
Data calculation and statistical analyses
Results
Comparison of the ability of different antioxidants to reduce gut redox potential
Effects of FA supplementation on gut microbiota in rats
Effects of FA supplementation on metabolites in the gut of rats
Improvement of intestinal antioxidant activity by FA supplementation
Alleviation of diarrhea by FA supplementation in a diarrhea model
Effects of FA on fecal microbiota in diarrhea model
Discussion
Conclusions
Ethical statement
Author contributions
Data availability
Acknowledgments
Conflict of interest
Supplementary information
Rights and permissions
References
About this article
Next Previous
RESEARCH ARTICLE   Open Access    

Transgenerational effects on the gene transcriptome of chicken liver

  • 1.

    State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China

  • 2.

    Department of Medicine, The University of Chicago, Chicago, IL 60637, USA

  • # Authors contributed equally: Mingkun Gao, Youying Chen

More Information
  • Received: 16 August 2024
    Revised: 10 September 2024
    Accepted: 27 September 2024
    Published online: 31 October 2024
    Animal Advances  1,  Article number: e003 (2024)  |  Cite this article
  • Chickens are important breeding animals and models for biomedical research, particularly due to their oviparous nature, which makes it an ideal subject for studying maternal effects. This study employs RNA-Seq to conduct a comprehensive analysis of the transcriptomics of the poultry liver, with a focus on maternal transgenerational effects. Samples were examined from broiler breeders, E19 embryos, and 21-day-old offspring, identifying 2,753 DEGs. GO analysis revealed significant enrichment of differentially expressed RNAs in functions such as actin filament binding and lysosomal activity. KEGG analysis identified pathways associated with endocytosis and Toll-like receptor signaling, displaying a high-low-high expression pattern across the broiler breeders, embryos, and offspring, which is closely linked to immune function regulation. Conversely, the Neuroactive ligand-receptor interaction and Calcium signaling exhibited a low-high-low expression pattern, which is intimately associated with organogenesis, and embryonic development. Additionally, based on DEGs, genes such as IGF1, IGFBP, FASN, and ELOVL were identified, which are significantly expressed in embryos and are crucial for development and lipid metabolism regulation. In summary, the present research provides a valuable transcriptional regulatory network for studying maternal effects on liver tissue development in broiler breeders, laying a foundation for further exploration of the molecular mechanisms underlying maternal effects.

  • Introduction

    The compound ferulic acid (FA) is derived from the resin of asafetida, a perennial herb belonging to the Umbelliferaceae family. Within the plant kingdom, FA serves as a prevalent phenolic acid with diverse health benefits, encompassing its role as an efficacious scavenger of free radicals, antithrombotic and antibacterial agent, modulator of inflammation, among other effects[1]. The physiological functions of FA as an antioxidant are numerous, although its mechanism is not fully understood compared to other antioxidants.

    The redox potential value serves as a macroscopic manifestation of intricate reactions occurring within the intestinal environment. Fluctuations in redox potential are intricately associated with the multifaceted response of intestinal bacteria and overall gastrointestinal health[2]. Diarrhea frequently co-occurs with inflammation, resulting in alterations in the microbial composition of the gut as bacteria flourish in an inflammatory and oxygen-rich environment. Intestinal dysbiosis is characterized by enhanced respiratory metabolism of aerobic bacteria, elevated redox potential[3]. This ecological imbalance provides electron acceptors for cellular metabolism, thereby prompting microorganisms to prefer metabolic reactions with higher thermodynamic redox potential energy[4]. Conversely, due to the anaerobic environment, metabolic reactions with lower oxidation-reduction potential energy are favored in thermodynamics within a healthy organism[5]. Therefore, it is crucial to regulate the intestinal chemical environment to reduce redox potential and promote stability within the intestinal microecology while reducing the occurrence of diarrhea. Research has substantiated that dietary consumption of phenolic acids can augment gut functionality through their anti-inflammatory properties on the gastrointestinal tract and modulation of the gut microbiome[6]. The multifaceted regulatory functions of the intestinal microbiota primarily stem from its metabolic capacity and the array of metabolites it generates[7]. These microbial metabolites regulate host homeostasis through various mechanisms, while polyphenols modulate the composition and metabolism of the intestinal microbiome[8,9]. Consumption of antioxidant-rich foods could serve as an effective strategy for modulating the oxygen environment within the gastrointestinal tract[10,11].

    The magnesium citrate diarrhea model involves administering a hypertonic solution of magnesium citrate to animals, which is poorly absorbed by the body and leads to the development of chronic diarrhea in rats[12]. Nutritional chronic diarrhea commonly manifests in weaned piglets during clinical production. Diarrhea often coincides with intestinal Enterobacter dilatation, and the heightened oxygen content within the intestine leads to an elevation in intestinal redox potential, thereby facilitating intestinal Escherichia coli dilatation[13]. Given the association between intestinal redox potential and host susceptibility to pathogenic bacteria, it is worth investigating whether low redox potential correlates with nutritional diarrhea. Therefore, studying the pathogenesis, drug efficacy, and nutritional support of chronic diarrhea holds significant clinical value. As an antioxidant, FA possesses a diverse range of physiological functions. However, the precise mechanism underlying its antioxidative properties remains incompletely understood. Antioxidants can modulate the redox potential within the gastrointestinal tract. Despite limited research on mammals, it is worth noting that apart from its antioxidant effects, FA may exert influence on intestinal redox potential through the regulation of gut microflora and metabolites. Notably, there existed a correlation between redox potential and intestinal health (e.g., diarrhea) in animals. Consequently, further investigation is warranted to explore whether FA can alleviate diarrhea by mitigating redox potential. The current study will provide new insights for modulating intestinal redox potential and alleviating chronic diarrhea.

    Materials and methods

    Evaluating of different antioxidants on regulating redox potential ability

    Antioxidant assays in vitro

    The assessment of antioxidant activity in vitro was conducted using various concentrations of antioxidants, as outlined in the references[14]. Antioxidants including chemical classes [Ethoxyquin (EQ, HPLC, 94.7%) and butylated hydroxytoluene (BHT, HPLC, 98.1%)], polyphenols (FA, Supplementary Information 1, HPLC, 99.9% and resveratrol, HPLC, 99.8%), natural pigments (anthocyanin, HPLC, 95.1% and carotene, HPLC, 99.3%), vitamins [vitamin C (VC, HPLC, 95.2%) and vitamin E (VE, HPLC, 93.6%)] and rutin (HPLC, 96.3%) were evaluated 2,2'-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and ferric-reducing antioxidant power (FRAP) activity. All antioxidants were purchased from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). The DPPH scavenging activity of antioxidants was assessed using the DPPH scavenging assay kit (Solarbio, Beijing, China). Briefly, a 10 µL sample was combined with 190 µL of DPPH working solution and incubated in darkness at room temperature for 30 min. Subsequently, absorbance was measured at 515 nm. The percentage of DPPH free radical scavenging rate (%) was calculated using the formula: [As0 − (As1− As2)] / As0 × 100, where As0 represents the absorbance of the control group (dH2O replaces the sample solution), As1 represents the absorbance of the sample mixed with DPPH solution, and As2 represents the absorbance with only the sample solution. The ABTS clearance assay kit was employed to evaluate the ABTS scavenging activity of nine antioxidants[15]. Briefly, a 10 µL sample was added to 190 µL of ABTS working solution at ambient temperature and incubated in the absence of light for 6 min. Subsequently, the absorbance was measured at 405 nm. The percentage scavenging rate of ABTS free radicals can be calculated using the formula (%) = [As0 − (As1 − As2)] / As0 × 100, where As0 represents the absorbance of the control group (replacing the sample solution with dH2O), As1 represents the absorbance of the sample mixed with ABTS, and As2 represents the absorbance of an individual sample solution. Ferric-reducing activity of antioxidants was evaluated using the FRAP assay kit (Yuanye, Shanghai, China) according to the manual instructions. Briefly, 264 µL FRAP working solution was added to 30 µL of sample and incubated at 37 °C for 30 min. The absorbance was measured at 593 nm. The results were expressed as Fe2+ quivalents.

    Intervention experiments of different antioxidants in vivo

    EQ, FA, rutin, and resveratrol antioxidants were screened based on FRAP analysis. Six-week old male sprague-dawley (SD) rats (average initial weight 150 g) were purchased from Cavens Biogle (Suzhou, China) and allowed to acclimatize to the animal facility environment for a week before the experiments. All rats had ad libitum access to food and water. Thirty rats were randomly divided into five groups, i.e., the control (n = 6) group, EQ (n = 6), FA (n = 6), rutin (n = 6), and resveratrol (n = 6) groups. The experimental rats were individually housed in a single cage with an iron frame at the bottom. Refer to Supplementary Information 2 and Supplementary Information 3 for an inventory of various antioxidant dietary formulations. In detail, low and high concentrations of antioxidants were supplemented into the daily ration of rats in the EQ, FA, rutin, and resveratrol groups. After the end of the low concentration of antioxidants intervention period, the high concentration of antioxidants intervention began after feeding the antioxidant-free diet for one week. All interventions continued for 15 d. During the experiment, fresh fecal samples were collected daily for redox potential measurements. Finally, all rats were euthanized by CO2 asphyxiation, ileum and colonic tissues, and digesta were collected. Samples were immediately snap-frozen and transferred for storage at −80 °C until further 16S rRNA gene sequencing and metabolite analysis.

    Rat diarrhea model induced with magnesium citrate

    A magnesium citrate-induced diarrhea model[12] was established using 6-week-old SD rats. Thirty rats were randomly divided into three groups: Con (no treatment; n = 10); ConMg (Chronic Mg-induced diarrhea; n = 10), and FAMg (a diet supplemented with FA in chronic Mg-induced diarrhea; n = 10). Throughout the experiment, rats in Con and ConMg groups were provided with a standard diet and had ad libitum access to feed and water. Rats in the FAMg group were given a diet enriched with FA antioxidant along with ad libitum access to feed and water. The experimental rats were individually housed in a cage with an iron frame at the bottom. After 10 d of feeding, the corresponding chronic diarrhea model was established. The weight, water intake, redox potential, and diarrhea index were measured at consistent time points. The fecal samples of days 7, 17, and 24 were immediately snap-frozen and transferred for storage at −80 °C for further 16S rRNA gene sequencing analysis.

    Redox potential measurement

    Fresh feces and intestinal digesta were collected in a pre-filled zip-lock bag with CO2, ensuring that each sampling point was controlled within a half-hour timeframe. After sample collection, it was promptly sealed and mixed before determining the redox potential. Subsequently, the samples were collected using freeze-storage tubes free from dnases and rnases to preserve their microbial composition and antioxidant capacity. All samples were initially flash-frozen in liquid nitrogen and then transferred to a −80 °C freezer. In accordance with our previous methodology[13], the redox potential of feces and intestinal digesta was measured using a direct method. The fecal redox potential in the diarrhea model was determined through a dilution method.

    Observation of diarrhea

    The characteristics and color of the feces were observed. Each rat's loose feces and formed feces were counted daily, and the diameter of each loose feces was measured using a divider and scale. Diarrhea rate and diarrhea index[12] were primary indicators to evaluate diarrhea severity. Loose feces rate = Number of loose feces per animal ÷ Total number of feces per animal. The grading system for watery feces consistency is based on diameter: < 1 cm is grade 1, ~1.9 cm is grade 2, 2~3 cm is grade 3, and > 3 cm is grade 4. Average watery feces consistency = Total volume of watery feces ÷ Number of watery feces.

    Determination of T-AOC, SOD, ROS, and MDA in intestine

    Colon and ileal tissue were excised, rinsed with ice-cold saline, and dried with filter paper. Then, the samples were homogenized with 0.1 g/mL wet weight of ice-cold physiological saline in a glass Teflon homogenizer for 3 min at 4 °C[16], which was followed by centrifugation at 2,000× g for 20 min at 4 °C. The supernatant was collected and used for the determination of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), reactive oxygen species (ROS), and malondialdehyde (MDA) using commercial kits (Solarbio, Beijing, China). Please refer to Supplementary Information 4 for detailed procedures.

    16S rRNA gene sequencing and processing

    The microbial DNA was extracted from frozen intestinal digesta and fecal samples using the E.Z.N.A.® Stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer's recommended protocols. DNA concentration and integrity were measured with Nanodrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis. Extracted DNA was stored at −20 °C until further processing. The extracted DNA was used as a template for PCR amplification of bacteria. 16S rRNA genes with the barcoded primers and takara ex taq (Takara). For bacterial diversity analysis[17], V3−V4 variable regions of 16S rRNA genes was amplified with universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3') for V3−V4 regions. The unweighted unifrac distance matrix performed by R package was used for unweighted unifrac principal coordinates analysis to estimate the beta diversity[18]. Then the R package was used to analyze the significant differences between different groups using the wilcoxon statistical test. The linear discriminant analysis effect size (LEfSe) method was used to compare the taxonomy abundance spectrum[19].

    Metabolome analysis

    The metabolomic analysis was performed by Shanghai Luming Biological Technology Co., Ltd (Shanghai, China). An qcquity uplc I-class plus (Waters Corporation, Milford, USA) fitted with q-exactive mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. The original LC-MS data were processed by software progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK) for baseline filtering, peak identification, integral, retention time correction, peak alignment, and normalization. Compound identification was based on precise mass-to-charge ratio (M/z), secondary fragments, and isotopic distribution using the human metabolome database (HMDB), lipidmaps (V2.3), metlin, and self-built databases. The extracted data were then further processed by removing any peaks with a missing value (ion intensity = 0) in more than 50% in groups, by replacing zero value with half of the minimum value, and by screening according to the qualitative results of the compound. The matrix was imported in R to carry out principal component analysis (PCoA) to observe the overall distribution among the samples and the stability of the whole analysis process. A two-tailed student’s t-test was further used to verify whether the metabolites of difference between groups were significant.

    Data calculation and statistical analyses

    Statistically significant differences were performed using SPSS Statistics 23.0 software and p < 0.05 presented the statistical significance of differences. All data are expressed as mean ± SD. Correlations between datasets were calculated using Pearman rank correlation. Besides, specific details of the statistical analyses for all experiments were displayed in the figure legends and results section.

    Results

    Comparison of the ability of different antioxidants to reduce gut redox potential

    All types of antioxidants exhibited high DPPH clearance (Supplementary Information 5, Fig. 1a), and different antioxidants of the same type also displayed differences in the clearance of ABTS (Supplementary Information 6, Fig. 1b). Both VE and carotene had poor clearances of DPPH and ABTS. FRAP assay involved the reduction of Fe3+ to Fe2+ by the antioxidant in the sample, resulting in the formation of a stable orange-red complex with phenolines and exhibiting a characteristic absorption peak at a wavelength of 520 nm (Supplementary Information 7, Fig. 1c). Based on FRAP analysis, EQ, FA, rutin, and resveratrol were selected for in vivo testing. The effect of different antioxidants on redox potential was analyzed via a one-way analysis of variance. Then, the dynamic changes of redox potential were not significant under the intervention of low concentration of FA (Fig. 1d). However, at high concentration, FA showed a significant reduction in fecal redox potential. The subsequent reduction of the redox potential was observed with resveratrol and rutin, followed by EQ (Fig. 1e). The ratio of Δredox potential to FRAP in high-concentration intervention test could be utilized for assessing the reduction rate of antioxidants on redox potential. The results showed that FA exhibited the highest ratio of Δredox potential to FRAP (Fig. 1f). Furthermore, compared to other antioxidants, FA also displayed the capacity to reduce the redox potential of ileal (Fig. 1g) and colonic (Fig. 1h) digesta.

    Figure 1.  Comparison of regulating redox potential ability of different antioxidants. (a), (b) The DPPH and ABTS clearance of different kinds of antioxidants in vitro. (c) Determination of the FRAP activity of different kinds of antioxidants in vitro. (d), (e) Dynamic changes of redox potential at diverse antioxidant intervention in vivo. (f) Ratio of Δ redox potential to FRAP. (g), (h) Effects of EQ, FA, rutin and resveratrol on regulating redox potential of ileal and colonic digesta.
    DownLoad: Full-Size Img PowerPoint

    Effects of FA supplementation on gut microbiota in rats

    Based on the observed effects of FA on fecal redox potential, the impact of FA on intestinal microbial communities was further investigated. According to the Venn diagram (Fig. 2a), 2,311 ASVs were clustered in colonic digesta, in which 427 and 428 ASVs were unique in the Con and FA groups, respectively. As shown in Fig. 2b, compared with the Con group, FA supplementation had no significant effect on α-diversity (Chao1, Ace) of ileal and colonic microbiota (p > 0.05). Moreover, a plot of principal coordinate analysis (Fig. 2c) showed that there was no separation of colonic microbiota between the two groups (p > 0.05). However, FA significantly decreased colon redox potential and increased the abundance of Actinobacteriota (Fig. 2d; p < 0.05). At the genus level, FA significantly decreased the abundance of Prevotella and Rodentibacter and enhanced the abundance of Corynebacterium, Christensenellaceae_R-7_group in the colon (Fig. 2e; p < 0.05). LEfSe analysis was performed for diverse taxa compared with the integration of ileum and colon microflora. Eubacterium sraeum_group was the only distinctive genus in the FA ileum microbiome. Actinomycetes, Christensenellaceae_R-7_group, Erysipelatoclostridium and Paludicola were enriched by FA treatment in the colon (Fig. 2f). Prevotella and Lachnospiraceae_NK4A136_group were enriched in the colon of the Con group.

    Figure 2.  Effects of FA supplementation on intestinal microbes at different taxonomic levels of rats. (a) Venn diagrams show the numbers of unique and shared ASVs between the four groups. (b) The alpha diversity of ileal and colonic microbiota. (c) Unweighted principal coordinate analysis of ileal and colonic microbiota. (d) Composition of ileal and colonic microbiota at the phylum level. (e) LDA score plot generated from LEfSe analysis used to evaluate the differentially abundant. Taxa from phylum to genus of ileal and colonic microbiota (LDA Score > 2, p < 0.05). Con = Control group; FA = FA supplementation group; ASV = Amplicon sequence variant; LDA = Linear discriminant analysis; LEfSe = Linear discriminant analysis effect size. (f) The relative abundance of colonic microbes with significant differences.
    DownLoad: Full-Size Img PowerPoint

    Effects of FA supplementation on metabolites in the gut of rats

    The volcano plot showed that 48 differentially metabolites (15 up-regulated and 33 downregulated) were identified in ileal digesta (Fig. 3a) and 255 differentially metabolites (111 up-regulated and 144 down-regulated) were identified in colonic digesta (Fig. 3c). Overall, the most significant downregulated metabolite was gluconic acid in ileal digesta (Fig. 3b; p < 0.05) and alpha-muricholic acid in colonic digesta. Whereas the most significant up-regulated metabolite was tic10 in ileal digesta and azelaic acid in colonic digesta (Fig. 3d; p < 0.05). The chemical properties of differential metabolites were classified based on Funmeta (https://funmeta.oebiotech.com/). The results showed that FA increased the proportion of reductive metabolites in up-regulated metabolites and decreased the proportion of oxidizing metabolites in down-regulated metabolites (Fig. 3e & f). Furthermore, D-fructose was found to be a reductive metabolite that was significantly up-regulated in the ileum (Fig. 3g; p < 0.05) but down-regulated in the colon (Fig. 3h; p < 0.05). In the ileum (Fig. 3i), the differential metabolites were predominantly enriched in lipoic acid metabolism and pentose phosphate pathway, as well as in galactose metabolism and amino sugar and nucleotide sugar metabolism. On the other hand, the differential metabolites in the colon (Fig. 3j) were significantly enriched in metabolic pathways such as linoleic acid metabolism, fructose and mannose metabolism, and taurine and hypotaurine metabolism.

    Figure 3.  Effects of FA supplementation on intestinal metabolism of rats. (a), (c) The volcano plot displays the distribution of differentially expressed metabolites of ileal and colonic digesta. Fold change (FC) was calculated by comparing with the Con group. Metabolites over the dashed line have a significant difference (p < 0.05, VIP > 1 and FC < 1). The red dots represent significantly up-regulated differential metabolites (p < 0.05, VIP > 1 and FC > 1), while the blue dots indicate significantly down-regulated differential metabolites (p < 0.05, VIP > 1 and FC < 1). (b), (d) Lolipopmaps of ileal and colonic digesta showing metabolites and their log2 (FoldChange) values. Red font indicates antioxidant; blue font indicates oxidant. An asterisk indicates the significance of differential metabolism (* indicates significance < 0.05, > 0.01; ** indicates significance < 0.01, > 0.001; *** indicates significance < 0.001, > 0.0001; **** means significance < 0.0001), and the dot size is determined by the VIP value. (e), (f) Statistical analysises of redox metabolites in ileal and colonic digesta. (g), (h) Differential expression analysises of D-Fructose in ileal and colonic digesta. (i), (j) KEGG pathway enrichment analysis on the differential metabolites identified in the ileum and colon.
    DownLoad: Full-Size Img PowerPoint

    Improvement of intestinal antioxidant activity by FA supplementation

    The intestinal redox potential is closely associated with the activity of antioxidants in the intestine. FA significantly increased T-AOC level (Fig. 4a) and decreased MDA level (Fig. 4d) in ileal and colonic tissue (p < 0.05). In addition, FA significantly increased SOD (Fig. 4b) and ROS levels (Fig. 4c) in ileal tissue (p < 0.05). Pearman correlation analysis showed that the redox potential of ileum and colon were both negatively correlated with T-AOC (Fig. 4e & i). Additionally, a negative correlation between colonic redox potential and MDA level was observed (Fig. 4l), but no significant correlation in ileal was found (Fig. 4h). Furthermore, the SOD (Fig. 4f & j) and ROS (Fig. 4g & k) levels had no significant association with redox potential in both the ileum and colon. Correlation heat map analysis showed that metabolites including l-phenylalanyl-l-proline, aspartyl-cysteine, and D-fructose were positively correlated with redox potential. On the other hand, alpha-muricholic acid glucoside and furocoumarinic acid glucoside were negatively correlated with T-AOC (Fig. 4m).

    Figure 4.  Improvement of FA supplementation on intestinal antioxidant activity. (a)−(d) The levels of T-AOC, SOD, ROS, and MDA activities. (e)−(l) Correlation analysis of redox potential of ileal and colonic digesta with different antioxidant indexes. (m) Correlations between the colonic microbiota and redox potential, T-AOC, SOD, ROS, and MDA activities. The analysis is based on Pearson's correlation coefficient. T-AOC = Total antioxidant capacity; SOD = Superoxide dismutase; ROS = Reactive oxygen species; MDA = Malondialdehyde. * = p < 0.05; ** = p < 0.01.
    DownLoad: Full-Size Img PowerPoint

    Alleviation of diarrhea by FA supplementation in a diarrhea model

    During the balance period (days 0−7), FA failed to show a weight gain advantage due to the limited intervention time (Fig. 5a & b). The FAMg group did not exhibit a significant increase in body weight compared to the ConMg group on both days 17 and 24 (Fig. 5a; p > 0.05). There was no significant difference in water intake among all groups throughout the experiment (Fig. 5c & d; p > 0.05). While the diarrhea index was consistently maintained at a low level and was significantly reduced on days 13 and 14 in the FA group (Fig. 5e & f; p < 0.05). The diarrhea index gradually declined to zero on days 18 to 24 following the cessation of the diarrhea model. On day 7, FA significantly reduced redox potential (Fig. 5g; p < 0.05). The redox potential of the FA group also remained at a low level during the diarrhea period (days 8-17) and decreased significantly on days 13, 14, 16, and 17 (Fig. 5h; p < 0.05). Furthermore, FA demonstrated its ability to sustain a lower redox potential during the recovery period (Fig. 5i). The correlation analysis indicated that redox potential was positively correlated with the diarrhea index (Fig. 5j; R = 0.6976; p = 0.0006).

    Figure 5.  Alleviation of FA supplementation on diarrhea in diarrhea model. (a) Dynamic changes in body weight. (b) Comparison of body weight changes in different days. (c) Dynamic changes in water intake. (d) Comparison of water intake change in different days. (e) Dynamic changes in diarrhea index. (f) Statistical analysis of diarrhea index. (g) Dynamic changes in redox potential in the balance period. (h) Statistical analysis of redox potential in diarrhea period. (i) Dynamic changes in redox potential in the recovery period. (j) Correlation analysis of redox potential and diarrhea index. The analysis is based on Pearson's correlation coefficient. * = p < 0.05.
    DownLoad: Full-Size Img PowerPoint

    Effects of FA on fecal microbiota in diarrhea model

    FA had no significant effect on the α-diversity (Chao1 and ACE indices) on day 7 (Fig. 6a & c; p > 0.05). However, the Chao1 and ACE indices in FAMg group increased significantly compared with ConMg on day 17 (Fig. 6b & d; p < 0.05). Furthermore, from the structural composition of the microbiota (Fig. 6e), FA had no significant effect on Firmicutes but increased the abundance of Actinobacteriota on day 7 (p < 0.05). However, on day 17, the abundances of Firmicutes in ConMg and FAMg groups were significantly decreased compared to Con group (p < 0.05). Conversely, Bacteroidota expanded significantly in the ConMg group compared to the Con group on day 17 (Fig. 6f; p < 0.05).

    Figure 6.  Effects of FA on fecal microbiota at defferent periods of rats. (a), (c) Dynamic changes of Chao 1 and ACE indexes in each group of diarrhea model. (b), (d) Comparison of the change of Chao 1 and ACE indexes in the balance, diarrhea and recovery periods. (e) Composition of fecal microbiota at the phylum level. (f) Statistical analysis of abundances of Firmicutes, Bacteroidota and Actinobacteria in different periods.
    DownLoad: Full-Size Img PowerPoint

    At the genus level, the abundances of Lachnospiraceae_NK4A136_group, Helicobacter, Eubacterium siraeum group, and Eubacterium xylanophilum group were decreased significantly in the FA group (Fig. 7a; p < 0.05). During day 17, both FAMg and ConMg significantly increased the abundance of Bacteroides, while decreasing the abundance of Lachnospiraceae_NK4A136_group, Lactobacillus, and Limosilactobacillus (p < 0.05). Importantly, FAMg showed an ability to significantly increase Blautia abundance compared to ConMg (Fig. 7b; p < 0.05). During day 24, FAMg significantly reduced the abundance of Bifidobacterium, Flavonifractor and Ruminococcus torques group compared to ConMg (Fig. 7c; p < 0.05). The presence of numerous bacterial genera was identified at the genus level, followed by the prediction of influential variables using a random forest model. GCA-900066575 and Lachnospiraceae_NK4A136_group in the database displayed a higher value of contribution to enhance the redox potential and had a significant positive correlation with redox potential (Fig. 7d & e). Moreover, Negativibacillus, Adlercreutzia, UCG.005, and Ralstonia contributed substantially to reducing the redox potential.

    Figure 7.  Effects of FA on fecal microbiota at genus level by regulating redox potential. (a) Differential microbiota was analyzed using the Wilcoxon test in the balance period. (b) Statistical analysis on the top eight microbiota in the diarrhea period. Different letters on the column indicate significant differences, while the same letters indicate non-significant differences. (c) Differential microbiota was analyzed using the Wilcoxon test in the recovery period. (d) Significantly different microbiota was ranked in descending order of importance to redox potential prediction using random forest regression. (e) The correlation between GCA-900066575, Lachnospiraceae_NK4A136_group and redox potential. The random forest regression was calculated using the hmisc R package (v.4.5.0). Pearman's rho with asymptotic measure-specific P value between microbiota and redox potential and p < 0.05 was used to identify a significant correlation.
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Diarrhea is a major global health problem. Studies have reported that diarrhea can cause a series of disorders such as malnutrition, vitamin deficiency, anemia, and reduced body resistance, which seriously damage body health. Some studies have found that intestinal imbalance is one of the main pathogenic factors of diarrhea[20]. Notably, in our previous study[13], a significant increase in intestinal redox potential was observed during diarrhea. Therefore, it is of great significance to reduce the redox potential by regulating the intestinal chemical environment to promote intestinal microecological stability and further reduce the occurrence of diarrhea. Thus, in the current experiment, a rat model of diarrhea was established to investigate the potential regulatory effects of antioxidant supplementation on the intestinal chemical environment to reduce the occurrence of diarrhea. In this study, the findings demonstrated that fecal administration could enhance the structure of redox metabolites in rats by regulating intestinal microflora, thereby reducing intestinal redox potential. Furthermore, preventing bacterial overgrowth during diarrhea expedited the reestablishment of intestinal microbiome composition and function after diarrhea.

    A fish study has found that the addition of antioxidants in vitro can effectively decrease the intestinal redox potential, thereby inhibiting the colonization of pathogens in the intestine[11]. Any substance or compound that scavenges oxygen free radicals or inhibits cellular oxidation processes is considered an antioxidant[21]. Antioxidants play a crucial role in human health by inhibiting or delaying adverse oxidative reactions, thereby preventing the development of diseases associated with oxidative stress[22]. However, assessing antioxidant capacity is a complex task, as no single method can fully capture the natural reactions occurring in the body[23]. Various methods have been employed to evaluate antioxidant activity, including those based on scavenging stable free radicals (such as DPPH, ABTS, and FRAP)[24]. Although both DPPH and ABTS methods are relatively simple and feasible, their chemical properties limit their applicability in biological environments. On the other hand, FRAP and ABTS methods demonstrated good correlation and consistency in results[25].

    Over the past few decades, extensive studies have been conducted on the antioxidant activity of polyphenols. Numerous studies have been published demonstrating their protective effects against various diseases caused by oxidative stress, including neurodegenerative diseases[26], diabetes[27], cardiovascuar disease[28], and allergies[29]. Recent investigations have also unveiled other mechanisms by which polyphenol antioxidants regulate the gut microbiome[30]. However, these protective effects are primarily attributed to their ability to directly scavenge free radicals such as oxygen and nitrogen species, which is dependent on the presence of intramolecular phenolic hydroxyl groups that generate stable phenoxyl radicals and their spatial arrangement i.e., electronic structure[31,32]. Polyphenol antioxidants, such as FA and resveratrol, exhibited significant efficacy in reducing redox potential, with comparatively higher FRAP-based in vitro antioxidant activity. The association between FARP and redox potential was assessed according to Miliciac et al.[33], and the results demonstrated that the ratio of FA-induced changes in redox potential to FRAP was the highest, indicating that FA had a significant effect on reducing intestinal redox potential.

    Further assessment of ileum and colonic digesta confirmed that FA exhibited superior efficacy in reducing redox potential. Intestinal redox potential serves as a crucial internal environmental factor. To comprehend the effect of FA on intestinal redox potential, 16S rRNA gene sequencing analysis was conducted to investigate the dynamics of microbial community reestablishment in rat feces across ileal, colonic digesta, and diarrhea models following FA treatment. In the absence of diarrhea, FA supplementation did not significantly affect the α-diversity of intestinal microbiota. Similarly, previous studies have reported that although FA does not significantly affect the α-diversity of intestinal microbiota, it does reshape its composition[34]. Furthermore, the present results demonstrate a significant alteration in the β-diversity of intestinal microbiota induced by FA. FA supplementation significantly reduced the abundance of Lachnospiraceae_NK4A136_group, a member of the Lachnospiraceae family, while increasing the prevalence of Christensenellaceae_R-7_group, a genus in the Christensenellaceae family. Lachnospiraceae has been reported to be effective against age-induced oxidative stress and inflammation[35], whereas Christensenellaceae_R-7_group is considered a reliable biomarker for overall health in organisms[36].

    Metabolome sequencing of ileal colon contents was then utilized to evaluate the impact of FA supplementation on intestinal redox status. Gut microbes communicate through metabolites, but the exact effect of these metabolites on gut redox potential remains largely unexplored[37]. However, the redox activity exhibited by these microbial metabolites directly reflects the redox state of the gut[38]. The present findings suggest that FA supplementation significantly increases the proportion of reduced metabolites in both the colon and ileum, thereby maintaining a lower intestinal redox potential. These results are consistent with the findings of previous studies[39]. To further understand the changes in oxidative and reductive metabolism, the analysis of KEGG pathway enrichment of differential metabolites demonstrated that the differential metabolites were predominantly enriched in lipoic acid metabolism and pentose phosphate pathway, as well as galactose metabolism and amino sugar and nucleotide sugar metabolism in the ileum. On the other hand, the differential metabolites in the colon were significantly enriched in metabolic pathways such as linoleic acid metabolism, fructose and mannose metabolism, and taurine and hypotaurine metabolism. Furthermore, they enhance the reductive metabolism in the intestine by increasing intestinal metabolism[40]. However, the causal effect of FA on host response and reduced redox potential remains unclear, even though the reduced gut redox potential has been associated with gut microbiota remodeling and metabolite changes due to FA supplementation. It is known that intestinal redox potential is often influenced by host-related factors[41]. For instance, during periods of stress, intestinal antioxidant active substances are significantly reduced, thereby decreasing intestinal resistance[42]. This study focused on examining the correlation between FA supplementation and antioxidant status as well as redox potential. Additionally, the relationship between metabolites and redox potential was investigated. FA supplementation can enhance intestinal antioxidant activity and is associated with intestinal redox potential. Interestingly, it was discovered that metabolites linked to both redox potential and total antioxidant capacity in the gut did not overlap, suggesting that host-related factors were not primarily responsible for reducing FA-induced redox potential.

    In the case of diarrhea, FAMg slowed down the decline in gut microbiota α-diversity and facilitated a faster recovery from significantly reduced levels compared to ConMg. To investigate the examined process of the disrupted gut microbiome during diarrhea, both its composition and function were examined[43]. The average abundance of Lactobacillus and Limosilactobacillus in the FAMg group was more similar to that in the Con group. Limosilactobacillus has been demonstrated to have a potent antioxidant mechanism against oxidative stress and its associated chronic ailments[44]. In the meantime, empirical studies have demonstrated that Limosilactobacillus can effectively mitigate the symptoms of diarrhea and colon inflammation induced by antibiotics and concurrently facilitate the normal expression of colon immune factors[45], indicating that the gut microflora can cascade with the body's immune system to fight against the disease in the case of diarrhea. Importantly, FAMg demonstrated the ability to significantly enhance Blautia abundance compared to ConMg. Studies have shown that Blautia, one of the most prevalent and crucial acetogenic bacteria in the gut, could alleviate depression and accelerate the progression of breast cancer[46]. Consequently, a decrease in gut redox potential leads to an increase in the abundance of key probiotics in the gut during diarrhea. To elucidate the microbial contributors and their associated functions in diarrhea following FA supplementation, the bacterial genera responsible for redox potential regulation was identified. Notably, Lachnospiraceae_NK4A136_group made a significantly higher contribution to redox potential regulation. Interestingly, the abundance of Lachnospiraceae_NK4A136_group was found to be markedly reduced in the FA-treated group during in vivo screening and subsequent recovery period after diarrhea. Lachnospiraceae_NK4A136_group belongs to sporogenic anaerobic bacteria of the trichospirillaceae family. Lachnospiraceae_NK4A136_group produces SCFAs through fermentation of dietary polysaccharides, which is negatively associated with a variety of metabolic diseases and chronic inflammation[47]. The abundance of Lachnospiraceae_NK4A136_group was positively correlated with redox potential. Therefore, Lachnospiraceae_NK4A136_group may represent a bacterial genus that potentially regulates the redox potential in the intestinal environment. However, the important role of Lachnospiraceae_NK4A136_group in regulating intestinal redox potential and its mechanism still needs to be further studied.

    Conclusions

    In summary, the present study demonstrated that FA supplementation reduced diarrhea by decreasing the intestinal redox potential. Furthermore, it has been observed that FA-regulated gut microbiota and metabolites contributed to a more rapid reduction in intestinal redox potential and enhanced microbial reductive metabolism in rats after diarrhea. This study uncovered the potential of FA in alleviating diarrhea and highlights the interplay between FA-regulated gut microbiota and changes in redox potential during post-diarrhea processes. The present findings provide novel insights into the mechanisms by which FA regulates intestinal redox potential and identify potential strategies for mitigating diarrhea.

  • All procedures were reviewed and preapproved by the Animal Care and Use Committee of Nanjing Agricultural University, identification number: SYXK2019-0066, approval date: 2023-05-09, and implemented based on the standard of Experimental Animal Care and Use Guidelines of China, identification number: EACUGC2018-01. The research followed the 'Replacement, Reduction, and Refinement' principles to minimize harm to animals. This article provides details on the housing conditions, care, and pain management for the animals, ensuring that the impact on the animals is minimized during the experiment.

  • The authors confirm contribution to the paper as follows: data curation: Feng N, You J, Wang D, Li L; formal analysis: Feng N, Wang D; writing - original draft: Feng N, You J; writing - review: Feng N; writing - editing: Feng N, You J, Su Y, Feng X; investigation: You J; resources: Xu R; validation: Xu R, Chen L; conceptualization, supervision: Su Y, Feng X; project administration, funding acquisition: Su Y. All authors reviewed the results and approved the final version of the manuscript.

  • The raw data of 16S rRNA gene sequencing in the study have been deposited in the repository of the Sequence Read Archive, with number SUB14680623. The data of metabolome analysis have been included as part of the supplementary material.

    • Acknowledgments

    This research was supported by the National Natural Science Foundation of China (32272891) and the National Key R&D Program of China (2022YFD1300402).

  • The authors declare that they have no conflict of interest. Lian Li and Yong Su are the Editorial Board members of Animal Advances who were blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of these Editorial Board members and the research groups.

  • Supplementary Table S1 The differential expression levels of lncRNAs between maternal, embryo, and offspring.
    Supplementary Table S2 The correlation index between differentially expressed mRNAs and lncRNAs, as well as the differential mRNA expression levels between maternal, embryo, and offspring.
    Supplementary Table S3 The six differential clusters analyzed by time analysis, along with the associated GO pathways and the enriched differentially expressed mRNAs.
    Supplementary Fig. S1 Quality assessment of whole-transcriptome data and time analysis clusters. (a) Quality assessment of full-length transcriptome. (b) GO pathway analysis enriched in clusters 4, 5, and 6.
  • [1]

    Gao M, Wang J, Lv Z. 2023. Supplementing Genistein for Breeder Hens Alters the Growth Performance and Intestinal Health of Offspring. Life 13:1468

    doi: 10.3390/life13071468

    CrossRef   Google Scholar

    [2]

    Macpherson AJ, de Agüero MG, Ganal-Vonarburg SC. 2017. How nutrition and the maternal microbiota shape the neonatal immune system. Nature Reviews Immunology 17:508−17

    doi: 10.1038/nri.2017.58

    CrossRef   Google Scholar

    [3]

    Percy Z, Vuong AM, Xu Y, Xie C, Ospina M, et al. 2021. Maternal Urinary Organophosphate Esters and Alterations in Maternal and Neonatal Thyroid Hormones. American Journal of Epidemiology 190:1793−802

    doi: 10.1093/aje/kwab086

    CrossRef   Google Scholar

    [4]

    Das A, Iwata-Otsubo A, Destouni A, Dawicki-McKenna JM, Boese KG, et al. 2022. Epigenetic, genetic and maternal effects enable stable centromere inheritance. Nature Cell Biology 24:748−56

    doi: 10.1038/s41556-022-00897-w

    CrossRef   Google Scholar

    [5]

    Wong EA, Uni Z. 2021. Centennial Review: The chicken yolk sac is a multifunctional organ. Poultry Science 100:100821

    doi: 10.1016/j.psj.2020.11.004

    CrossRef   Google Scholar

    [6]

    Fan H, Lv Z, Gan L, Guo Y. 2018. Transcriptomics-Related Mechanisms of Supplementing Laying Broiler Breeder Hens with Dietary Daidzein to Improve the Immune Function and Growth Performance of Offspring. Journal of Agricultural and Food Chemistry 66:2049−60

    doi: 10.1021/acs.jafc.7b06069

    CrossRef   Google Scholar

    [7]

    Lv Z, Fan H, Zhang B, Ning C, Xing K, et al. 2018. Dietary genistein supplementation in laying broiler breeder hens alters the development and metabolism of offspring embryos as revealed by hepatic transcriptome analysis. The FASEB Journal 32:4214−28

    doi: 10.1096/fj.201701457R

    CrossRef   Google Scholar

    [8]

    Na W, Wu YY, Gong PF, Wu CY, Cheng BH, et al. 2018. Embryonic transcriptome and proteome analyses on hepatic lipid metabolism in chickens divergently selected for abdominal fat content. BMC Genomics 19:384

    doi: 10.1186/s12864-018-4776-9

    CrossRef   Google Scholar

    [9]

    Bednarczyk M, Dunislawska A, Stadnicka K, Grochowska E. 2021. Chicken embryo as a model in epigenetic research. Poultry Science 100:101164

    doi: 10.1016/j.psj.2021.101164

    CrossRef   Google Scholar

    [10]

    Zhang M, Ma X, Zhai Y, Zhang D, Sui L, et al. 2020. Comprehensive transcriptome analysis of lncRNAs reveals the role of lncAD in chicken intramuscular and abdominal adipogenesis. Journal of Agricultural and Food Chemistry 68:3678−88

    doi: 10.1021/acs.jafc.9b07405

    CrossRef   Google Scholar

    [11]

    Sarropoulos I, Marin R, Cardoso-Moreira M, Kaessmann H. 2019. Developmental dynamics of lncRNAs across mammalian organs and species. Nature 571:510−14

    doi: 10.1038/s41586-019-1341-x

    CrossRef   Google Scholar

    [12]

    Wang Z, Kong L, Zhu L, Hu X, Su P, et al. 2021. The mixed application of organic and inorganic selenium shows better effects on incubation and progeny parameters. Poultry Science 100:1132−41

    doi: 10.1016/j.psj.2020.10.037

    CrossRef   Google Scholar

    [13]

    Fu C, Zhang Y, Yao Q, Wei X, Shi T, et al. 2020. Maternal conjugated linoleic acid alters hepatic lipid metabolism via the AMPK signaling pathway in chick embryos. Poultry Science 99:224−34

    doi: 10.3382/ps/pez462

    CrossRef   Google Scholar

    [14]

    Liu X, Fang Y, Ma X, Li P, Wang P, et al. 2022. Metabolomic profiling to assess the effects of chlordanes and its bioaccumulation characteristics in chicken embryo. Chemosphere 308:136580

    doi: 10.1016/j.chemosphere.2022.136580

    CrossRef   Google Scholar

    [15]

    Li C, Guo S, Zhang M, Gao J, Guo Y. 2015. DNA methylation and histone modification patterns during the late embryonic and early postnatal development of chickens. Poultry Science 94:706−21

    doi: 10.3382/ps/pev016

    CrossRef   Google Scholar

    [16]

    Gao M, Liao C, Fu J, Ning Z, Lv Z, et al. 2024. Probiotic cocktails accelerate baicalin metabolism in the ileum to modulate intestinal health in broiler chickens. Journal of Animal Science and Biotechnology 15:25

    doi: 10.1186/s40104-023-00974-6

    CrossRef   Google Scholar

    [17]

    Lv Z, Fan H, Gao M, Zhang X, Li G, et al. 2024. The accessible chromatin landscape of lipopolysaccharide-induced systemic inflammatory response identifying epigenome signatures and transcription regulatory networks in chickens. International Journal of Biological Macromolecules 266:131136

    doi: 10.1016/j.ijbiomac.2024.131136

    CrossRef   Google Scholar

    [18]

    Gao M, Chen Y, Li X, Li D, Liu A, et al. 2024. Methionine supplementation regulates eggshell quality and uterine transcriptome in late-stage broiler breeders. Animal Nutrition In press

    doi: 10.1016/j.aninu.2024.04.026

    CrossRef   Google Scholar

    [19]

    Dai H, Huang Z, Shi F, Li S, Zhang Y, et al. 2024. Effects of maternal hawthorn-leaf flavonoid supplementation on the intestinal development of offspring chicks. Poultry Science 103:103969

    doi: 10.1016/j.psj.2024.103969

    CrossRef   Google Scholar

    [20]

    Ren J, Sun C, Clinton M, Yang N. 2019. Dynamic transcriptional landscape of the early chick embryo. Frontiers in Cell and Developmental Biology 7:196

    doi: 10.3389/fcell.2019.00196

    CrossRef   Google Scholar

    [21]

    Liao L, Yao Z, Kong J, Zhang X, Li H, et al. 2022. Transcriptomic analysis reveals the dynamic changes of transcription factors during early development of chicken embryo. BMC Genomics 23:825

    doi: 10.1186/s12864-022-09054-x

    CrossRef   Google Scholar

    [22]

    Hivert MF, White F, Allard C, James K, Majid S, et al. 2024. Placental IGFBP1 levels during early pregnancy and the risk of insulin resistance and gestational diabetes. Nature Medicine 30:1689−95

    doi: 10.1038/s41591-024-02936-5

    CrossRef   Google Scholar

    [23]

    Salvatore D, Simonides WS, Dentice M, Zavacki AM, Larsen PR. 2014. Thyroid hormones and skeletal muscle—new insights and potential implications. Nature Reviews Endocrinology 10:206−14

    doi: 10.1038/nrendo.2013.238

    CrossRef   Google Scholar

    [24]

    Wakoh T, Sugimoto M, Terauchi K, Shimada J, Maruyama M. 2009. A novel p53-dependent apoptosis function of TARSH in tumor development. Nagoya Journal of Medical Science 71:109−14

    Google Scholar

    [25]

    Wei W, Qin B, Wen W, Zhang B, Luo H, et al. 2023. FBXW7β loss-of-function enhances FASN-mediated lipogenesis and promotes colorectal cancer growth. Signal Transduction and Targeted Therapy 8:187

    doi: 10.1038/s41392-023-01405-8

    CrossRef   Google Scholar

    [26]

    Ding Y, Yang J, Ma Y, Yao T, Chen X, et al. 2019. MYCN and PRC1 cooperatively repress docosahexaenoic acid synthesis in neuroblastoma via ELOVL2. Journal of Experimental & Clinical Cancer Research 38:498

    doi: 10.1186/s13046-019-1492-5

    CrossRef   Google Scholar

    [27]

    Zuidhof MJ, Schneider BL, Carney VL, Korver DR, Robinson FE. 2014. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005. Poult Sci 93:2970−82

    doi: 10.3382/ps.2014-04291

    CrossRef   Google Scholar

    [28]

    Briggs JA, Weinreb C, Wagner DE, Megason S, Peshkin L, et al. 2018. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360:eaar5780

    doi: 10.1126/science.aar5780

    CrossRef   Google Scholar

    [29]

    Han VX, Patel S, Jones HF, Dale RC. 2021. Maternal immune activation and neuroinflammation in human neurodevelopmental disorders. Nature Reviews Neurology 17:564−79

    doi: 10.1038/s41582-021-00530-8

    CrossRef   Google Scholar

    [30]

    Cai B, Li Z, Ma M, Wang Z, Han P, et al. 2017. LncRNA-Six1 encodes a micropeptide to activate Six1 in cis and is involved in cell proliferation and muscle growth. Frontiers in Physiology 8:230

    doi: 10.3389/fphys.2017.00230

    CrossRef   Google Scholar

    [31]

    Ahrens M, Ammerpohl O, von Schönfels W, Kolarova J, Bens S, et al. 2013. DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metabolism 18:296−302

    doi: 10.1016/j.cmet.2013.07.004

    CrossRef   Google Scholar

    [32]

    Martín AI, Priego T, Moreno-Ruperez Á, González-Hedström D, Granado M, et al. 2021. IGF-1 and IGFBP-3 in inflammatory cachexia. International Journal of Molecular Sciences 22:9469

    doi: 10.3390/ijms22179469

    CrossRef   Google Scholar

    [33]

    Burgdorf JS, Yoon S, Dos Santos M, Lammert CR, Moskal JR, et al. 2023. An IGFBP2-derived peptide promotes neuroplasticity and rescues deficits in a mouse model of Phelan-McDermid syndrome. Molecular Psychiatry 28:1101−11

    doi: 10.1038/s41380-022-01904-0

    CrossRef   Google Scholar

    [34]

    Yin H, Zhang S, Sun Y, Li S, Ning Y, et al. 2017. MicroRNA-34/449 targets IGFBP-3 and attenuates airway remodeling by suppressing Nur77-mediated autophagy. Cell Death & Disease 8:e2998

    doi: 10.1038/cddis.2017.357

    CrossRef   Google Scholar

    [35]

    Liu Y, Zhang M, He T, Yang W, Wang L, et al. 2020. Epigenetic silencing of IGFBPL1 promotes esophageal cancer growth by activating PI3K-AKT signaling. Clinical Epigenetics 12:22

    doi: 10.1186/s13148-020-0815-x

    CrossRef   Google Scholar

    [36]

    Liu J, Tang T, Wang GD, Liu B. 2019. LncRNA-H19 promotes hepatic lipogenesis by directly regulating miR-130a/PPARγ axis in non-alcoholic fatty liver disease. Bioscience Reports 39:BSR20181722

    doi: 10.1042/BSR20181722

    CrossRef   Google Scholar

    [37]

    Li X, Wang J, Wang L, Gao Y, Feng G, et al. 2022. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal Transduction and Targeted Therapy 7:162

    doi: 10.1038/s41392-022-00964-6

    CrossRef   Google Scholar

    [38]

    Jaynes JM, Sable R, Ronzetti M, Bautista W, Knotts Z, et al. 2020. Mannose receptor (CD206) activation in tumor-associated macrophages enhances adaptive and innate antitumor immune responses. Science Translational Medicine 12:eaax6337

    doi: 10.1126/scitranslmed.aax6337

    CrossRef   Google Scholar

    [39]

    Hossain MI, Marcus JM, Lee JH, Garcia PL, Singh V, et al. 2021. Restoration of CTSD (cathepsin D) and lysosomal function in stroke is neuroprotective. Autophagy 17:1330−48

    doi: 10.1080/15548627.2020.1761219

    CrossRef   Google Scholar

    [40]

    Zhang X, Wei M, Fan J, Yan W, Zha X, et al. 2021. Ischemia-induced upregulation of autophagy preludes dysfunctional lysosomal storage and associated synaptic impairments in neurons. Autophagy 17:1519−42

    doi: 10.1080/15548627.2020.1840796

    CrossRef   Google Scholar

    [41]

    Feng J, Lin P, Wang Y, Zhang Z. 2019. Molecular characterization, expression patterns, and functional analysis of toll-interacting protein (Tollip) in Japanese eel Anguilla japonica. Fish & Shellfish Immunology 90:52−64

    doi: 10.1016/j.fsi.2019.04.053

    CrossRef   Google Scholar

    [42]

    Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. 2017. Shotgun metagenomics, from sampling to analysis. Nature Biotechnology 35:833−44

    doi: 10.1038/nbt.3935

    CrossRef   Google Scholar

    [43]

    Ouyang Q, Hu S, Li L, Ran M, Zhu J, et al. 2021. Integrated mRNA and miRNA transcriptome analysis provides novel insights into the molecular mechanisms underlying goose pituitary development during the embryo-to-hatchling transition. Poultry Science 100:101380

    doi: 10.1016/j.psj.2021.101380

    CrossRef   Google Scholar

    [44]

    Tritsch NX, Granger AJ, Sabatini BL. 2016. Mechanisms and functions of GABA co-release. Nature Reviews Neuroscience 17:139−45

    doi: 10.1038/nrn.2015.21

    CrossRef   Google Scholar

    [45]

    Cao C, Cai Y, Li Y, Li T, Zhang J, et al. 2023. Characterization and comparative transcriptomic analysis of skeletal muscle in female Pekin duck and Hanzhong Ma duck during different growth stages using RNA-seq. Poultry Science 102:103122

    doi: 10.1016/j.psj.2023.103122

    CrossRef   Google Scholar

    [46]

    Yin L, Chen Q, Huang Q, Wang X, Zhang D, et al. 2023. Physiological role of dietary energy in the sexual maturity: clues of body size, gonad development, and serum biochemical parameters of Chinese indigenous chicken. Poultry Science 102:103157

    doi: 10.1016/j.psj.2023.103157

    CrossRef   Google Scholar

    [47]

    Li-Villarreal N, Forbes MM, Loza AJ, Chen J, Ma T, et al. 2015. Dachsous1b cadherin regulates actin and microtubule cytoskeleton during early zebrafish embryogenesis. Development 142:2704−18

    doi: 10.1242/dev.119800

    CrossRef   Google Scholar

    [48]

    Bechstedt S, Albert JT, Kreil DP, Müller-Reichert T, Göpfert MC, et al. 2010. A doublecortin containing microtubule-associated protein is implicated in mechanotransduction in Drosophila sensory cilia. Nature Communications 1:11

    doi: 10.1038/ncomms1007

    CrossRef   Google Scholar

    [49]

    McKenna ED, Sarbanes SL, Cummings SW, Roll-Mecak A. 2023. The tubulin code, from molecules to health and disease. Annual Review of Cell and Developmental Biology 39:331−61

    doi: 10.1146/annurev-cellbio-030123-032748

    CrossRef   Google Scholar

    [50]

    Vitre B, Guesdon A, Delaval B. 2020. Non-ciliary roles of IFT proteins in cell division and polycystic kidney diseases. Frontiers in Cell and Developmental Biology 8:578239

    doi: 10.3389/fcell.2020.578239

    CrossRef   Google Scholar

  • Cite this article

    Gao M, Chen Y, Fan H, Chen S, Wang H, et al. 2024. Transgenerational effects on the gene transcriptome of chicken liver. Animal Advances 1: e003 doi: 10.48130/animadv-0024-0003
    Gao M, Chen Y, Fan H, Chen S, Wang H, et al. 2024. Transgenerational effects on the gene transcriptome of chicken liver. Animal Advances 1: e003 doi: 10.48130/animadv-0024-0003
    shu

Figures(8)  /  Tables(4)

Download PDF

Article Metrics

Article views(1623) PDF downloads(223)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Transgenerational effects on the gene transcriptome of chicken liver

  • 1.

    State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China

  • 2.

    Department of Medicine, The University of Chicago, Chicago, IL 60637, USA

  • Received: 16 August 2024
  • Revised: 10 September 2024
  • Accepted: 27 September 2024
  • Published online: 31 October 2024
Animal Advances  1,  Article number: e003  (2024)  |  Cite this article

Abstract: Chickens are important breeding animals and models for biomedical research, particularly due to their oviparous nature, which makes it an ideal subject for studying maternal effects. This study employs RNA-Seq to conduct a comprehensive analysis of the transcriptomics of the poultry liver, with a focus on maternal transgenerational effects. Samples were examined from broiler breeders, E19 embryos, and 21-day-old offspring, identifying 2,753 DEGs. GO analysis revealed significant enrichment of differentially expressed RNAs in functions such as actin filament binding and lysosomal activity. KEGG analysis identified pathways associated with endocytosis and Toll-like receptor signaling, displaying a high-low-high expression pattern across the broiler breeders, embryos, and offspring, which is closely linked to immune function regulation. Conversely, the Neuroactive ligand-receptor interaction and Calcium signaling exhibited a low-high-low expression pattern, which is intimately associated with organogenesis, and embryonic development. Additionally, based on DEGs, genes such as IGF1, IGFBP, FASN, and ELOVL were identified, which are significantly expressed in embryos and are crucial for development and lipid metabolism regulation. In summary, the present research provides a valuable transcriptional regulatory network for studying maternal effects on liver tissue development in broiler breeders, laying a foundation for further exploration of the molecular mechanisms underlying maternal effects.

    HTML

    Introduction

    • Maternal transgenerational effects significantly influence the phenotypic traits of the offspring, encompassing factors such as nutrition, hormones, gut microbiota, and epigenetic regulation[1−4]. As oviparous animals, chickens rely solely on the nutrition provided by the egg post-separation from the mother, rendering maternal effects critical from the embryonic to early developmental stages in chicks. However, the patterns of these changes remain largely unexplored[5]. Our previous research identified the chicken liver as a target organ influenced by maternal effects at both the transcriptional and epigenetic levels[6,7]. The liver, being the most crucial metabolic organ in chickens, facilitates fat and protein metabolism, as well as the storage of vitamins and minerals, playing a pivotal role in hematopoiesis and glucose and lipid metabolism during embryonic development[8]. Yet, systematic analyses of the transcriptional diversity in the liver across generations, from breeders to embryos to offspring, are scarce.

      During the reprogramming process of avian embryonic development, epigenetic modifications of specific genes can undergo aberrations. Mutations in genes encoding epigenetic regulatory proteins may also occur. These changes represent critical phases where maternal effects are exerted[9]. Epigenetics plays a significant role in regulating complex cellular networks. They are involved in key physiological processes such as chromatin assembly and mRNA splicing. The distribution of epigenetic regulators within both the cytoplasm and nucleus is closely associated with their mechanisms of action[10,11]. Notably, genes are intimately linked to embryogenesis and development, with maternally inherited LncRNAs being abundantly present in early embryos[9]; in chicken embryos, there is a marked increase in the expression of mRNA during developmental stages, suggesting that maternal effect contributes to maintaining pluripotency in stem cells and plays a role in embryonic development[6]. Furthermore, studies indicate that the duration of maternal effects in birds is relatively short[12], which may be closely related to the efficiency of early chick development. However, the changes in marker genes involved in this process and the underlying mechanisms remain largely unclear.

      In the production cycle of broiler breeders, individuals entering the late laying phase exhibit significant declines in egg production and increased body fat deposition, impacting liver lipid metabolism and other metabolic pathways[13]. By contrast, during rapid cell division and differentiation, the liver of hatching chicken embryos showcases an efficient energy metabolism system and biosynthesis activities, laying the foundation for subsequent life stages[14]. Furthermore, at 21 d post-hatch, during a period of rapid growth, the liver's functions in energy and protein/amino acid metabolism, lipid metabolism adjustment, vitamin and mineral storage, and immune regulation are crucial to support this growth rate[15].

      To delve into the temporal and spatial variations in the liver transcriptomes of broiler chickens at different developmental stages and identify key transcription factors associated with economic traits, particularly metabolic efficiency, this study analyzed 23 high-throughput RNA-Seq libraries from liver tissue samples of AA strain broiler breeders, offspring at the E19 embryonic stage, and 21-day-old post-hatch offspring. Through this approach, we aim to gain a deeper understanding of the functional changes in the liver across developmental stages and their potential impacts on overall health and productivity, thereby uncovering key gene expression patterns that influence liver health and metabolic characteristics.

    Materials and methods

      Animals and sample collection

    • The experiment utilized late-laying 57-week-old broiler breeders and their male broiler offspring, ROSS 308, raised under standardized conditions at the Meat Chicken Science and Technology Backyard (Zhuozhou, China). At the end of 65 weeks, liver samples were collected from eight selected broiler breeders. Fertilized eggs were collected, and incubated, and only male offspring chicks were used in this study. Samples were taken on the 19th day of embryonic development for collection and sex determination (seven were selected), and chicks were raised until the age of 21 d, at which point liver samples from eight selected individuals were collected. The samples were immediately frozen in liquid nitrogen and stored at −80°C for further analysis. The experimental workflow is illustrated in Fig. 1.

      Figure 1. 

      Liver transcriptome profiles of maternal, embryo, and offspring stages. (a) Workflow diagram, (b) differential gene expression profiles across the three stages, (c) PCoA plot of sample distribution, (d) number of differentially expressed genes between stages, (e) Venn diagram of differentially expressed genes.

      Total RNA isolation

    • The liver samples were collected and immediately placed in RNase-free centrifuge tubes, followed by rapid freezing in liquid nitrogen. Total RNA isolation was performed using 100 mg tissue samples and 1 mL Trizol reagent (Vazyme #R701), adhering to the protocols provided by the supplier. To evaluate the RNA integrity and purity, a NanoDrop microvolume spectrophotometer (Thermo Fisher, Wilmington, USA) was utilized, following the procedure outlined by Gao et al.[16]. This procedure guaranteed the structural integrity and dependability of the RNA specimens before further examination.

      RNA-Seq library construction and sequencing

    • Total RNA that met the criteria, with a RIN value of ≥ 7 as determined by the Agilent 2100 Bioanalyzer, was utilized for mRNA library preparation for sequencing[17]. To precisely quantify the molar concentration of the constructed libraries, qPCR with standards was conducted, employing the KAPA Library Quantification Kit (Cat no. KK4602) as recommended by Illumina, ensuring accurate library loading volumes for sequencing. The libraries were then hybridized to a Flowcell using the TruSeq Rapid PE Cluster Kit on a cBot, followed by clustering on the HiSeq2500 system. This step involves immobilizing the library molecules on Flowcell primers and undergoing bridge PCR amplification before sequencing. Illumina's data collection software managed the sequencing operation.

      Gene expression analysis

    • Gene expression differentiation analysis employed DESeq2[18], adhering to the approach outlined by Fan et al.[6]. Differentially expressed genes were identified by setting the criteria to an absolute log2 |fold change| ≥1 and a Q-value ≤ 0.05. To assess the expression correlation among samples, the Pearson correlation coefficient for all gene expression data was calculated. For intra-group comparisons, only Pearson correlation coefficients exceeding 0.80 were considered, denoting superior reproducibility. Prediction pipeline for lncRNA transcripts：(1) Cufflinks were used to assemble the transcripts for each sample. (2) Cuffmerge was used to merge the transcripts predicted from all individuals. (3) The merged transcripts were compared with the known protein-coding transcripts downloaded from Ensembl using Cuffcompare, filtering out lncRNA loci that encode proteins. (4) Transcripts shorter than 200 bp and with fewer than two exons were removed. (5) CNCI was used to predict the coding potential of transcripts, and transcripts with coding potential were removed. (6) BLAST was used to further filter out transcripts with coding potential by comparing them with the NCBI protein database. Based on the FPKM values of lncRNA expression, differential expression analysis between samples was performed using the DEGseq package. The differential lncRNAs were screened using the statistical methods of fold-change (expression difference multiple) and Fisher's exact test.

      Functional enrichment analysis

    • Enrichment analysis was executed with the entire genome serving as the background set, focusing on the biological process (BP) category of Gene Ontology (GO) for the functional assessment. GO terms were deemed significant if they achieved a P value < 0.01 and were associated with ≥ 2 genes.

      Time analysis

    • Short Time-series Expression Miner (STEM), Using STEM, time-series gene expression data was normalized and analyzed by clustering genes into predefined temporal expression profiles. Parameters such as the number of profiles and FDR correction were set, and the analysis identified significant profiles based on gene expression trends across time points. The results were then visualized, and significant profiles were subjected to Gene Ontology (GO) enrichment analysis to identify related biological processes. Key findings included the identification of gene clusters with distinct temporal expression patterns and their associated GO terms.

    Results

      Expression of DEGs in the liver across maternal-embryo-offspring stages

    • To delineate the RNA expression profiles involved in liver function during maternal effects in broiler breeders (Fig. 1a,b), high-throughput RNA sequencing generated 185.88 GB of clean data from 23 liver samples, encompassing both maternal and offspring stages, specifically embryos at day 19 of gestation and chicks 21 d post-hatch. Each sample contributed 7.9 GB of clean data, with a Q30 base percentage consistently exceeding 91.71% (Table 1). A total of 19,031 genes were identified across the liver samples. Clean reads were aligned to the reference chicken genome using HISAT, resulting in a mapping success rate between 90.83% and 93.35% (Table 1). The identification of differentially expressed genes (DEGs) was grounded on comparative analyses and their expression levels across the various samples. A comprehensive array of DEGs was identified in the liver tissue (Table 2, Supplementary Fig. S1a). Principal component analysis (PCA) was employed to model the distribution and segregation trends among the maternal, embryonic, and offspring comparison groups, illustrating distinct clustering patterns (Fig. 1c). A Venn diagram further emphasized the shared and unique gene expression alterations occurring across the maternal, embryonic, and offspring stages. When considering these stages as distinct entities, pairwise differential expression analyses revealed that the maternal versus embryonic stages exhibited the highest number of differentially expressed genes, followed by the embryonic versus offspring stages, with the least differential expression observed between the maternal and offspring stages (Fig. 1d, e).

      Table 1.  RNA-seq read statistics.

      Sample name Clean_reads Raw_bases Q20 Q30 GC_content HISAT mapped HISAT Uniquely mapped
      Breeder1 38362812 11040224580 96.53% 93.16% 51.99% 88.5% 75.78%
      Breeder2 37847708 10940622840 96.28% 92.59% 56.15% 72.49% 52.58%
      Breeder3 34672132 9846336276 96.52% 93.10% 51.41% 89.35% 77.37%
      Breeder4 40482362 11592999936 96.48% 93.04% 51.51% 89.92% 76.84%
      Breeder5 37196291 10296891108 96.86% 93.68% 49.86% 92.12% 82.17%
      Breeder6 40958805 12072458664 96.59% 93.12% 59.16% 59.57% 32.85%
      Breeder7 35709829 10510141572 96.47% 92.96% 56.03% 75.07% 55.02%
      Breeder8 33669242 9914908752 96.79% 93.69% 51.39% 90.98% 78.82%
      Embryo1 40485557 11654523972 95.90% 91.83% 52.50% 89.67% 74.26%
      Embryo2 37432078 11069231292 96.48% 93.04% 51.34% 88.79% 74.68%
      Embryo3 39065142 11126909556 96.26% 92.59% 51.62% 88.44% 75.25%
      Embryo4 35508865 10322469360 96.30% 92.65% 51.73% 90.69% 77.44%
      Embryo5 37076246 10837283688 96.46% 92.94% 52.48% 89.48% 74.28%
      Embryo6 39932521 11473593264 96.23% 92.53% 51.41% 89.88% 77.24%
      Embryo7 43619518 12556149732 96.36% 92.82% 51.46% 90.88% 76.31%
      Embryo8 36859302 10562648292 96.14% 92.44% 53.36% 76.34% 58.07%
      Offspring1 35823274 10444364028 96.48% 93.12% 50.66% 91.04% 78.7%
      Offspring2 38678468 11108159496 96.39% 92.82% 53.96% 81.18% 63.04%
      Offspring3 38105455 11117678544 96.33% 92.70% 55.17% 79.53% 62.3%
      Offspring4 38509142 11038901832 95.75% 91.71% 51.52% 88.96% 74.73%
      Offspring5 38362812 11040224580 96.53% 93.16% 51.99% 88.5% 75.78%
      Offspring6 37847708 10940622840 96.28% 92.59% 56.15% 72.49% 52.58%
      Offspring7 34672132 9846336276 96.52% 93.10% 51.41% 89.35% 77.37%

      Table 2.  Pairwise comparison of up and downregulated DEGs between maternal, embryo and offspring chickens in liver.

      Compare group Down Up Total
      Liver Maternal-vs-Embryo 1540 4569 6109
      Embryo-vs-Offspring 1449 4578 6027
      Maternal-vs-Offspring 26 38 64

      Differential GO analysis of DEGs in the maternal effect process

    • To further elucidate the characteristic DEGs involved in the liver's role during maternal effects, the top 10 DEGs in the maternal-embryo and embryo-offspring comparisons were identified, including IGFBP1, ABI3BP, DIO3, PDK4, IGFALS, IGF1, among others (Tables 3 & 4). In addition, 2,090 differentially expressed long non-coding RNAs (LncRNAs) were detected, each longer than 1,000 nucleotides (Supplementary Table S1). The distribution of DEGs in the liver are visually depicted in the volcano plots (Fig. 2a).

      Table 3.  Maternal-vs-embryo Top 10 DEGs.

      Gene Log2FC p−value Regulation
      IGFBP1 −3.4921 < 0.0001 down
      ABI3BP −4.4975 < 0.0001 down
      MT3 −4.7465 < 0.0001 down
      DIO3 −4.8438 < 0.0001 down
      CHRNA7 −5.4058 < 0.0001 down
      TTLL2 −5.4702 < 0.0001 down
      NEGR1 −6.8204 < 0.0001 down
      SREBF2 −2.9490 6.11E-303 down
      PDK4 −2.8915 3.08E-300 down
      IRF1 −3.0829 4.15E-299 down
      IGFBP1, Insulin-like Growth Factor Binding Protein 1; ABI3BP, ABI Family Member 3 Binding Protein; MT3, Metallothionein 3; DIO3, Deiodinase Iodothyronine Type III; CHRNA7, Cholinergic Receptor Nicotinic Alpha 7 Subunit; TTLL2, Tubulin Tyrosine Ligase Like 2; NEGR1, Neuronal Growth Regulator 1; SREBF2, Sterol Regulatory Element Binding Transcription Factor 2; PDK4, Pyruvate Dehydrogenase Kinase 4; IRF1, Interferon Regulatory Factor 1.

      Table 4.  Embryo-vs-Offspring Top 10 DEGs.

      Gene Log2FC p−value Regulation
      MT3 5.1899 < 0.0001 up
      ABI3BP 4.5664 < 0.0001 up
      IGFBP1 4.1745 < 0.0001 up
      HMGCR −3.1252 < 0.0001 down
      IGFALS −3.7697 < 0.0001 down
      IGFBP4 −3.8229 < 0.0001 down
      FASN −5.1979 < 0.0001 down
      IGF1 −6.3826 < 0.0001 down
      CDKN2B −6.4335 < 0.0001 down
      ELOVL2 −6.2947 < 0.0001 down
      MT3, Metallothionein 3; ABI3BP, ABI Family Member 3 Binding Protein; IGFBP1, Insulin-like growth factor-binding protein 1; HMGCR, 3-Hydroxy-3-Methylglutaryl-CoA Reductase; IGFALS, Insulin-like growth factor-binding protein; acid labile subunit; IGFBP4, Insulin-like growth factor-binding protein 4; FASN, Fatty Acid Synthase; IGF1, Insulin-like growth factor 1; CDKN2B, Cyclin Dependent Kinase Inhibitor 2B; ELOVL2,ELOVL Fatty Acid Elongase 2.

      Figure 2. 

      GO enrichment analysis of differentially expressed genes (DEGs). (a) Volcano plot, (b) top 10 GO pathways enriched in DEGs between maternal and embryo stages. The cyan color represents GO's Biological Process (BP) pathways, while the yellow color represents GO's Cellular Component (CC) pathways.

      Enrichment analysis was also performed to pinpoint key genes and pathways implicated in the maternal effect. The liver undergoes a functional transition from cell development and differentiation during the embryonic stage to glucose and lipid metabolism and immune function by the 21-d post-hatch stage. Gene Ontology (GO) enrichment analysis revealed significant biological pathways, including those related to multicellular organism development, synaptic transmission (GABAergic), adherens junction organization, cholesterol biosynthetic process, unsaturated fatty acid biosynthetic process, immune response, and the B cell receptor signaling pathway (Fig. 2b, Fig. 3a & b).

      Figure 3. 

      GO functional annotation analysis of embryo vs offspring stages. (a) Enriched functions at the GOBP (Gene Ontology Biological Process) level, (b) enriched functions at the GOCC (Gene Ontology Cellular Component) level.

      Expression patterns of developmentally related genes across three stages

    • Based on the analysis of RNA expression profiles across the three stages, 20 characteristic genes involved in the regulation of cell proliferation and organ development were successfully identified (Fig. 4). Key genes such as IGF1, IGFBP1, IGFBP2, IGFBP4, IGFALS, and HMGCR were significantly upregulated in the liver tissues of both embryos and offspring, with expression levels being particularly higher in the embryonic stage. Additionally, critical genes regulating lipid metabolism, including FASN, ELOVL2, ELOVL5, ELOVL6, SREBF2, ACACA, and CYP7A1 were also found to be significantly upregulated in the liver of both embryos and offspring. A correlation analysis of characteristic target genes (including LncRNAs) across the three stages was conducted to gain deeper insights into the regulatory patterns of maternal effects (Fig. 5, Supplementary Table S2).

      Figure 4. 

      Expression of DEGs in maternal, embryo, and offspring liver tissues. The red and blue squares represent FPKM values, with darker red indicating higher expression levels. Upregulated DEGs are marked in dark yellow, while downregulated DEGs are shown in white.

      Figure 5. 

      Correlation analysis of DEGs in maternal, embryo, and offspring stages. Pink represents negative correlations, while cyan indicates positive correlations.

      Time analysis of liver function across three developmental stages

    • Time-series analysis was utilized to explore the DEGs across the maternal-embryo-offspring stages, aiming to delineate the dynamic changes in liver mRNA expression profiles influenced by maternal effects (Fig. 6a). By comparing DEGs with a Q value ≤ 0.05 and an absolute log2|fold change| ≥ 1 across the various stages, the DEGs were organized into six distinct clusters, with expression patterns predominantly following 'high-low-high' and 'low-high-low' trends (Fig. 6b).

      Figure 6. 

      Time analysis of liver expression profiles across maternal, embryo, and offspring stages. (a) Workflow diagram of the time analysis. (b) Cluster analysis of DEGs across the three stages, showing six distinct clusters.

      In Cluster 1, KEGG pathway enrichment analysis revealed a significant association with the 'Toll-like receptor signaling pathway', where key characteristic genes included TLR2, TLR4, TLR5, MyD88, and NF-κB. The 'Lysosome' pathway was also enriched, with CTSD (Cathepsin D) and LAMP1 (Lysosomal Associated Membrane Protein 1) identified as pivotal genes, alongside the 'C-type lectin receptor signaling pathway'. Furthermore, the Gene Ontology (GO) categories in this cluster were significantly enriched for activities such as GTPase activator activity, actin filament binding, small GTPase binding, and protein tyrosine kinase activity (Fig. 7a−c).

      Figure 7. 

      Analysis of DEGs in the 'high-low-high' expression pattern (Cluster 1). (a) KEGG enrichment analysis of DEGs. (b) GOMF (Gene Ontology Molecular Function) enrichment analysis of DEGs. (c) GOCC (Gene Ontology Cellular Component) enrichment analysis of DEGs.

      In Cluster 2, KEGG analysis highlighted enrichment in the 'Neuroactive ligand-receptor interaction' pathway, with GABRA identified as a characteristic gene, as well as in the 'Calcium signaling pathway' and 'ECM-receptor interaction'. GO categories in this cluster were enriched in functions such as 'Calcium ion binding', 'nucleotide binding', 'synapse', 'neuron projection', 'glutamatergic synapse', and 'microtubule cytoskeleton', with TUBB, MAPs, and KIFs identified as key genes (Supplementary Table S3). The remaining four clusters also displayed enrichment in GO categories related to metabolism, development, and immune function, reflecting dynamic processes that occur throughout the maternal-embryo-offspring transition (Fig. 8a−c, Supplementary Fig. S1b).

      Figure 8. 

      Analysis of DEGs in the 'low-high-low' expression pattern (Cluster 2). (a) KEGG enrichment analysis of DEGs. (b) GOMF (Gene Ontology Molecular Function) enrichment analysis of DEGs. (c) GOCC (Gene Ontology Cellular Component) enrichment analysis of DEGs.

    Discussion

    • Chicken embryos require 21 d to develop and hatch, constituting one-third of the overall grow-out period. Consequently, early developmental stages are crucial in determining the final body weight at market. The maternal effects exerted by breeder hens play a decisive role during embryonic development[19]. The development of the liver during the embryonic stage is critical for broiler growth and is regulated by factors such as maternal nutrition and maternal epigenetic modifications[7]. Throughout embryonic development, the liver's metabolic functions transition from an early reliance on anaerobic glycolysis to a dependence on aerobic respiration. As development progresses, the liver's capacity for glucose metabolism strengthens, with gluconeogenesis and glycogen metabolism becoming increasingly refined[20]. Simultaneously, lipid metabolism becomes more active, with enhanced fatty acid oxidation capabilities[21]. However, the core regulatory genes involved in liver development, as well as the mechanisms underlying their changes during maternal effects, remain inadequately understood. To address this gap, a comprehensive transcriptome analysis was performed to elucidate the gene expression changes and related functional pathways that characterize liver development from breeder chickens, through embryos, to offspring broilers. This analysis offers critical insights into the molecular underpinnings of liver development, laying a solid foundation for future research into the maternal effects influencing growth and development in broiler chickens.

      Based on the comparative analysis of liver expression profiles between breeder hens and E19 chicken embryos, the top 10 significantly differentially expressed genes were identified, including IGFBP1, ABI3BP, MT3, DIO3, CHRNA7, TTLL2, NEGR1, SREBF2, PDK4, and IRF1. Previous studies have demonstrated that IGFBP1 plays a crucial role in regulating cell growth, differentiation, and metabolism by modulating the activity of insulin-like growth factors (IGFs)[22]. Additionally, DIO3 is involved in the regulation of thyroid hormones and promotes embryonic development by inactivating T3 and T4[23]. Through a comparative analysis of liver expression profiles between E19 chicken embryos and D21 broiler chickens, MT3, ABI3BP, and IGFBP1 were identified as characteristic genes highly expressed in E19 embryos. ABI3BP is involved in cytoskeletal reorganization and extracellular matrix signaling, playing a crucial role in influencing cell migration and tissue morphology during embryonic development[24]. Furthermore, a significant upregulation of key genes regulating lipid metabolism, FASN, and ELOVL2, was observed in D21 broilers. FASN acts as a pivotal enzyme in fatty acid synthesis, is closely associated with adipogenesis, and is highly expressed in metabolically active liver tissues[25]. ELOVL2 is essential for the elongation of fatty acids and is integral to the synthesis of polyunsaturated fatty acids[26]. The elevated expression of these genes is intricately linked to the nutritional demands of different developmental stages; E19 chicken embryos primarily rely on glycogen metabolism for energy, whereas D21 broilers, during their rapid growth phase, necessitate extensive lipid metabolism to satisfy their energy requirements[27]. Maternal nutrition plays an important role in shaping the offspring's metabolic system. For instance, lipid and carbohydrate metabolism are closely related to the mother's fat and carbohydrate intake. Nutritional imbalances could lead to metabolic disorders or abnormal fat deposition in the offspring.

      In the GO analysis of DEGs between maternal and embryo samples, significant enrichment in terms related to multicellular organism development, cell morphogenesis, and regulation of cell differentiation were observed. These biological processes are critical for ensuring the correct spatial arrangement of cells within tissues and organs during embryonic development, allowing cells to acquire the necessary functional characteristics[28]. Epigenetic regulation plays a crucial role in influencing both embryonic and offspring development[29]. In this study, 2,090 differential LncRNAs were identified and an association analysis performed with differential mRNAs, uncovering relationships of synergistic regulation and mutual inhibition. Previous studies have suggested that the maternal effects in breeder hens can influence the transcriptional levels of key genes such as MyoD in chicken embryos through LncRNA-mediated cis-regulation, thereby impacting embryonic metabolic development and immune processes[30]. Moreover, the present study analyzed DEGs across maternal, embryo, and offspring stages, and constructed an inter-regulatory GO network based on the distinct metabolic characteristics of each stage. This network provides a theoretical basis for further research into the mechanisms by which maternal effects regulate offspring development. The distinct metabolic characteristics observed across maternal, embryo, and offspring stages suggest that maternal effects play a crucial role in shaping the developmental trajectory and functional maturation of offspring.

      To further investigate the maternal effects on liver expression profiles, the top 20 DEGs were identified by selecting those with low expression levels in breeder hens and high expression levels in embryos and offspring broilers. Among these, the IGF family members, including IGF1, IGFBP1, IGFBP2, IGFBP4, and IGFALS, were found to exhibit consistently high expression from the E19 embryo stage to D21 broilers. The expression of IGF1 is subject to regulation by DNA methylation and non-coding RNAs, where maternal malnutrition can lead to decreased levels of fetal IGF1 through epigenetic modifications, subsequently inhibiting fetal growth and development[31]. IGFBP1, IGFBP2, and IGFBP4 modulate the activity of growth factors by binding to IGF1 and IGF2[32,33]. Additionally, maternal metabolic conditions, such as obesity or diabetes, can suppress the activity of these IGFBPs through mechanisms involving miRNA and DNA methylation, thereby reducing the secretion of growth factors and potentially affecting the growth and development of the offspring[34,35]. Additionally, lipid metabolism-related genes, such as FASN, ELOVL2, ELOVL5, ELOVL6, and ACACA, were found to be consistently highly expressed from the E19 embryo stage to D21 broilers. Research has shown that FASN is subject to indirect regulation by LncRNA H19, which promotes the process of fatty acid synthesis[36]. Although there are few reports on the epigenetic regulation of the ELOVL gene family, these genes play a crucial role in the elongation of fatty acids. Their activity is modulated under varying lipid nutritional conditions, thereby enhancing the elongation process to support lipid metabolism[37]. These findings further highlight the critical role of maternal effects in regulating the development of the offspring's liver.

      To elucidate the gene regulatory mechanisms and temporal characteristics during maternal effects, a time-series analysis of DEGs was constructed across the maternal, embryo, and offspring stages. The enriched DEGs predominantly followed 'high-low-high' and 'low-high-low' expression patterns. In the 'high-low-high' pattern, the enriched genes were primarily associated with immune function. The 'Lysosome pathway' was particularly significant in both the maternal and D21 offspring broiler stages, where cellular renewal, tissue repair, and immune responses depend heavily on lysosomal activity[38]. During the chicken embryo development stage, the primary cellular functions are rapid proliferation and differentiation, which leads to a relatively lower demand for lysosomal degradation. In this context, key genes such as Lysosomal Associated Membrane Protein 1 (LAMP1) and Cathepsin D (CTSD) were identified as crucial players. LAMP1 is essential for lysosomal transport, facilitating the movement of enzymes and proteins within lysosomes. CTSD, on the other hand, plays a significant role in lysosomal protein degradation, breaking down cellular waste and damaged proteins. These genes are vital for maintaining lysosomal function, which is critical for cellular homeostasis and metabolic regulation[39,40]. These findings provide deeper insights into the gene regulatory dynamics influenced by maternal effects throughout different developmental stages. The 'Toll-like receptor signaling pathway' plays a crucial role in recognizing pathogen-associated molecular patterns (PAMPs) and initiating immune responses[41]. During the maternal and offspring stages, exposure to external environments increases the likelihood of encountering bacteria and viruses, which in turn activates the expression of relevant characteristic genes involved in this pathway[42]. However, during the chicken embryo developmental stage, the immune system remains immature, and the relatively enclosed embryonic environment limits exposure to pathogens, resulting in fewer opportunities for activation of this pathway. The present study revealed that MyD88, TLR family genes, and NF-kB were significantly downregulated in chicken embryos, indicating that the immune response in chicken embryos relies more heavily on maternally provided antibodies and other protective mechanisms, resulting in a lower requirement for TLR pathway activation.

      In the 'low-high-low' expression pattern, the enriched genes were predominantly linked to cell proliferation and organogenesis. The 'Neuroactive ligand-receptor interaction pathway', which reflects the functional demands of the nervous system at various developmental stages showed that during the chicken embryo stage, neurotransmitter activity is primarily concentrated on early neural network formation and initial signal transduction[43]. The differentially upregulated gene Gamma-Aminobutyric Acid Receptor Subunit Alpha (GABRA), a major receptor for GABA plays a critical role in the synthesis of neurons in chicken embryos[44]. The 'Calcium signaling pathway' plays a crucial role during chicken embryo development, particularly in regulating rapid cell division, differentiation, and the formation of the muscle system[45]. As breeder hens and offspring broilers reach maturity, the rate of cell division and differentiation significantly decreases, leading to reduced dependence on calcium signaling[46]. Concurrently, metabolic processes undergo reprogramming, especially in offspring broilers, where the focus shifts toward energy metabolism, muscle hypertrophy, and enhanced production performance[27].

      Significant enrichment of the 'Microtubule cytoskeleton' was also observed during the chicken embryo stage, where it plays a pivotal role in maintaining cell structure and supporting cell proliferation[47]. During this developmental phase, cells require frequent structural reorganization and dynamic changes, and the high expression of microtubule cytoskeleton components ensures these processes occur efficiently, thereby facilitating organ formation and functional development[48]. Among the enriched genes, TUBB (Beta-tubulin) stands out as a core gene involved in maintaining cell morphology, driving cell division, mediating endocytosis, and supporting intracellular transport[49]. Additionally, MAPs (Microtubule-associated proteins) are crucial for regulating microtubule growth by binding to microtubules, ensuring the proper functioning of the cytoskeleton and thereby maintaining cellular integrity and facilitating development[50]. In summary, the distinct gene regulatory networks observed in this study reflect the dynamic changes in gene expression and function across different developmental stages. Immune-related pathways become increasingly active as broilers grow, supporting the complex functions of a mature physiological system. In contrast, the pathways enriched in chicken embryos are primarily focused on fundamental developmental functions, such as cell division, migration, and nervous system development. These differences in gene expression underscore the varying metabolic and physiological processes occurring at each stage of development. Moreover, these findings provide a foundational basis for further investigation into the epigenetic regulation of maternal effects in breeder hens, offering insights into how these effects influence the development and function of offspring.

    Conclusions

    • This study, by comparing liver gene expression profiles among breeder hens, chicken embryos, and offspring broilers, has highlighted the pivotal role of maternal effects in embryonic development. Significantly differentially expressed genes such as IGFBP1, ABI3BP, and DIO3, were identified which are crucial for glucose metabolism, lipid metabolism, and cytoskeletal reorganization. Time analysis revealed two primary gene expression patterns: 'high-low-high' and 'low-high-low'. The former pattern, associated with immune function, includes genes like LAMP1 and CTSD that are highly expressed in breeder hens and offspring broilers, indicating enhanced immune activity. The latter pattern includes genes such as GABRA and TUBB, which are highly expressed in chicken embryos, supporting neural network formation and cytoskeletal reorganization. In summary, maternal effects influence the expression of key genes in embryonic development through epigenetic regulation. Understanding this mechanism is crucial for improving broiler production performance. Future research should focus on exploring the relationship between maternal nutrition and epigenetics to optimize feeding strategies for breeder hens.

      Acknowledgments

      • This study was funded by the National Key R&D Program of China (2021YFD1300404), the China Agriculture Research System program (CARS-40 and CARS-41), the Beijing Natural Science Foundation (No. 6222036), National Natural Science Foundation of China (32202724).

      Ethical statement

      • All procedures were reviewed and preapproved by the China Agricultural University Animal Care and Use Committee (Beijing, China), identification number: SYXK20130013, approval date: 13/09/2013. The research followed the 'Replacement, Reduction, and Refinement' principles to minimize harm to animals. This article provides details on the housing conditions, care, and pain management for the animals, ensuring that the impact on the animals was minimized during the experiment.

      Author contributions

      • The authors confirm contribution to the paper as follows: study conception and design: Lv Z, Guo Y; data collection: Fan H, Wang H, Chen S, Nie W; analysis and interpretation of results: Gao M, Chen Y; draft manuscript preparation: Gao M. All authors reviewed the results and approved the final version of the manuscript.

      Data availability

      • The data that support the findings of this study are available in the Genome Sequence Archive at the National Genomics Data Center, China National Center for Bioinformation (GSA: CRA016504).

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      • # Authors contributed equally: Mingkun Gao, Youying Chen

      Supplementary information
      • Supplementary Table S1 The differential expression levels of lncRNAs between maternal, embryo, and offspring.
      • Supplementary Table S2 The correlation index between differentially expressed mRNAs and lncRNAs, as well as the differential mRNA expression levels between maternal, embryo, and offspring.
      • Supplementary Table S3 The six differential clusters analyzed by time analysis, along with the associated GO pathways and the enriched differentially expressed mRNAs.
      • Supplementary Fig. S1 Quality assessment of whole-transcriptome data and time analysis clusters. (a) Quality assessment of full-length transcriptome. (b) GO pathway analysis enriched in clusters 4, 5, and 6.
      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (8)  Table (4) References (50)
  • About this article
    Cite this article
    Gao M, Chen Y, Fan H, Chen S, Wang H, et al. 2024. Transgenerational effects on the gene transcriptome of chicken liver. Animal Advances 1: e003 doi: 10.48130/animadv-0024-0003
    Gao M, Chen Y, Fan H, Chen S, Wang H, et al. 2024. Transgenerational effects on the gene transcriptome of chicken liver. Animal Advances 1: e003 doi: 10.48130/animadv-0024-0003
    shu

Catalog

  • HTML

  • Introduction
  • Materials and methods

  • Animals and sample collection

  • Total RNA isolation

  • RNA-Seq library construction and sequencing

  • Gene expression analysis

  • Functional enrichment analysis

  • Time analysis

  • Results

  • Expression of DEGs in the liver across maternal-embryo-offspring stages

  • Differential GO analysis of DEGs in the maternal effect process

  • Expression patterns of developmentally related genes across three stages

  • Time analysis of liver function across three developmental stages

  • Discussion
  • Conclusions
  • Acknowledgments
  • Ethical statement
  • Author contributions
  • Data availability
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
logo

Contents

Policies

Services

Partnerships

© 2025 Maximum Academic Press, except Open Access articles. MAP is a member of ALPSP, COPE, CrossRef, cOAlition S, OASPA and STM.

Privacy Policy  |  Site Map  |  Terms and Conditions

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return