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. 2026 Feb 12;16:7824. doi: 10.1038/s41598-026-38762-7

Non-targeted metabolomics reveals liver metabolome changes in broiler chickens fed diets supplemented with fermented brewer’s grain

Zhongjian Gong 1, Qin Wang 1, Yuanfeng Li 2,✉
PMCID: PMC12953773  PMID: 41680257

Abstract

This study was designed to systematically assess the impact of wet-fermented brewer’s grains (WFBG) on hepatic metabolic profiles and key regulatory pathways in broiler chickens. In this 21-day experiment, 240 one-day-old male broiler chickens (36.46 ± 0.93 g) were randomly assigned to two groups. The experimental diets contained 0% and 20% WFBG, respectively. Non-targeted metabolomics profiling identified 202 liver differentially expressed metabolites (DEMs), comprising 174 up-regulated and 28 down-regulated species, which were significantly associated with WFBG intervention. Pathway enrichment analysis revealed that these DEMs were predominantly involved in 40 metabolic pathways, including glycine/serine/threonine metabolism, fatty acid biosynthesis, and ABC transporter pathways. Notably, a significant upregulation of these DEMs was observed in the 20% WFBG group (P < 0.05). This finding reveals that WFBG modulates hepatic metabolism via multiple pathways, supporting its application in broiler nutrition.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-38762-7.

Keywords: Broiler chicken, Fermented brewer’s grains, Non-targeted metabolomics, Liver metabolomics, Dietary supplementation

Subject terms: Biochemistry, Gastroenterology, Microbiology

Introduction

In modern broiler production, the intensive drive for enhanced growth performance and feed efficiency has intensified the need to explore sustainable feed resources while deciphering their impact on metabolic regulation. As a brewing by-product, fermented brewer’s grain (FBG) has garnered attention as a functional feed additive, owing to its rich composition of dietary fiber, crude protein, and bioactive metabolites—including organic acids and probiotics—produced during fermentation1. The microbial fermentation process not only improves nutrient digestibility and reduces anti-nutritional factors2, but also confers prebiotic properties that modulate gut microbiota3–5, with potential downstream effects on systemic metabolism. Wet brewer’s grain (WBG), high in moisture and crude fiber but low in protein6, is widely used in ruminants due to their specialized ruminal digestion7,8 but rarely in poultry3,9. Both wet and dried fermented brewer’s grain (FBG) enhance nutrient digestibility in broilers10,11, as seen in laying hens12 and ducks13, with documented benefits in poultry production performance12, gut microbiota balance3, antioxidant capacity14, and nutrient utilization10,15. Notably, the liver serves as a central hub for nutrient assimilation, energy metabolism, and detoxification in avian species, orchestrating critical metabolic pathways that govern growth and health16. Despite the established benefits of brewer’s grain in poultry, its influence on hepatic metabolic reprogramming—including shifts in nutrient partitioning, redox homeostasis, and xenobiotic processing within this pivotal organ—remains largely uncharacterized. This knowledge gap underscores the need to investigate how FBG modulates liver function, a key determinant of overall metabolic efficiency in broilers.

Metabolomics, a tool for analyzing metabolites, enables comprehensive profiling of hepatic metabolites, identifying metabolic pathways and biomarkers modulated by WFBG through qualitative, quantitative, and differential analysis17,18. Non-targeted metabolomics, in particular, facilitates global detection of metabolic shifts, critical for uncovering unanticipated mechanisms of WFBG action19. Widely applied in evaluating fermented feeds in pigs20, laying hens21, ducks22, and broilers19, it is especially valuable for decoding how WFBG affects hepatic metabolite profiles in broilers—given the liver’s central role in lipid, carbohydrate, and amino acid metabolism. This can clarify WFBG’s impacts on hepatic energy homeostasis and redox balance. This study aims to employ non-targeted metabolomics to comprehensively characterize liver metabolic alterations in broiler chickens fed WFBG-supplemented diets, with the goal of unraveling WFBG-mediated metabolic networks and providing mechanistic insights for optimized WFBG application in broiler feed formulation.

Materials and methods

Animals and treatments

A total of 240 one-day-old male broiler chickens (initial body weight: 36.46 ± 0.93 g) were randomly assigned to two groups using a body weight-matched randomization approach. Each group consisted of 6 replicates, with 20 birds per replicate. The control group was provided a basal diet, whereas the experimental group received the basal diet supplemented with 20% WFBG (wet brewer’s grain fermented by bacillus subtilis). The trial was conducted over a 21-day period during the summer season. The broilers were procured from Aoxiang Poultry Industry Co., Ltd. (Liaocheng, Shandong, China). The basal diet was procured from a commercial feed supplier, with its formulation and nutritional composition detailed in in the data published by Li et al.19 (Metabolic energy: 12.28 MJ·kg−1; Crude protein: 21.88%; Lysine: 1.26%). The experimental protocol (LCU20240016) was approved in advance by the Animal Ethics Committee of Liaocheng University (Liaocheng, Shandong, China). This study adheres to the ARRIVE guidelines (https://arriveguidelines.org), with all procedures conducted in compliance with relevant ethical regulations. The WFBG used was laboratory-prepared at Liaocheng University, following a formula of WBG: corn: wheat bran: fermenting agent (45:23:30:2), incubated at 37 °C for 72 h (once weekly). The experimental group was fed a mixture of basal diet and WFBG (80:20 ratio) based on the birds’ average daily feed intake (ADFI). The nutritional composition of basic feed refers to the data published by Li et al.23.

Sample collection

On day 21, birds were fasted for 12 h prior to weighing and blood collection. From each replicate of the control and 20% WFBG groups, six birds with weights closest to the pen average were randomly selected. Detailed body weight data of the cohort can be found in our prior work23. When selecting liver samples, individuals with body weight deviating by more than ± 10% from the group mean were excluded; the six selected birds per group had body weights within 5% of the group mean, ensuring they were representative of the overall population. Birds were euthanized via exsanguination under sodium pentobarbital anesthesia (60 mg·kg−1). After euthanasia, the liver was rapidly dissected; approximately 0.5 g of liver lobule tissue was collected, snap-frozen in liquid nitrogen, and stored at − 80 °C.

Liver metabolite extraction

Liver preparation for metabolomics

According to the growth performance results23, liver samples from the 20% WFBG group were selected for metabolomics analysis. Prior to analysis, samples were thawed on ice and thoroughly mixed via vortexing for 10 s. Liver samples (25 mg) were combined with 500 μL of an extraction solution (Methanol:acetonitrile:water, 2:2:1 v/v) containing deuterated internal standards. The mixture underwent vortexing for 4 min, sonication for 5 min in a 4 °C water bath, and a 1 h incubation at − 40 °C to precipitate proteins. Following this, samples were centrifuged at 12,000 rpm for 15 min at 4 °C. The supernatant was transferred to a new glass vial and introduced into an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) system for analysis. Equal volumes of the supernatant from all samples were combined to prepare quality control (QC) samples.

Metabolomics data capture

For the analysis of positive and negative metabolites, LC–MS/MS detection was conducted using an UHPLC system (Orbitrap Exploris, Thermo Fisher Scientific, USA) coupled with a UPLC BEH Amide column (Waters ACQUITY, 2.1 mm × 50 mm, 1.7 μm) and an Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific, USA). The mobile phase consisted of solvent A (25 mmol/L ammonium acetate aqueous solution with 25% ammonia water, adjusted to pH 9.75) and solvent B (ACN containing 0.1% formic acid). The autosampler was maintained at 4 °C, and the injection volume was set to 2 μL. The mass spectrometer operated under the information-dependent acquisition mode for full-scan MS analysis, controlled by Xcalibur V4.4 software (Thermo Fisher Scientific, USA). Detailed procedures for metabolomics analysis are described in the Supplementary File S1.

Metabolomics data analysis

The raw data were converted to mzXML format using ProteoWizard software (version 3.0.23000, ProteoWizard Software Foundation, USA) and processed via an in-house program developed with R language (version 4.1.3) based on the XCMS platform (version 3.14.0). Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed using SIMCA 18.0.1 (Umetrics) to distinguish metabolic profile differences between the two groups. Differential metabolites were screened based on two criteria: variable importance in projection (VIP) > 1 and P < 0.05 (from independent-samples t-test). These differential metabolites were then identified using the R packages (including CAMERA version 1.40.0 and MetaboAnalystR version 3.2.0) in conjunction with the BiotreeDB (version 3.0)24. Finally, candidate metabolites with VIP values > 1 and adjusted P < 0.05 were identified as potential biomarkers.

Statistical analysis

All data were first tested for normality using the Shapiro–Wilk test (P > 0.05 indicated normality) and for homogeneity of variance using Levene’s test (P > 0.05 indicated homogeneous variance). All results were presented as mean ± standard deviation (SD).

Data on growth performance were analyzed by analysis of variance (ANOVA) using the general linear model (GLM) procedure of SPSS software (version 23.0, SPSS Inc., Chicago, Illinois, USA)25. For continuous variables involving two groups (control vs. WFBG), comparisons were performed using the independent-samples t-test, with Welch’s correction applied to account for potential unequal variances. Correlation heatmaps were generated with the corrplot package (version 0.92) in R, using Pearson correlation coefficients to represent the strength of associations between metabolites. KEGG pathway enrichment bubble plots were created with the ggplot2 package (version 3.4.4) in R. Volcano plots of differential metabolites were generated using MetaboAnalyst 5.0. A P-value < 0.05 was considered statistically significant.

Results

Non-targeted liver metabolomics profiling of WFBG

To investigate the metabolic profile alterations in broilers exposed to high-level WFBG, metabolomics analyses were performed. OPLS-DA was applied to visualize the clustering patterns of metabolic profiles and identify differentially expressed metabolites (DEMs) across groups. Notably, liver samples from broiler chickens fed diets supplemented with 20% WFBG exhibited distinct metabolic profile alterations (Fig. 1A). The OPLS-DA score plots of liver samples showed clear separation between the control and WFBG groups (R2X = 0.333, R2Y = 0.975, Q2 = 0.475) (Fig. 1A), demonstrating the robustness of the models without overfitting. DEMs in the liver samples of broiler chickens from the 20% WFBG and control groups were screened using criteria of VIP score > 1 and P-value < 0.05. Heatmaps were generated to visualize the relative abundance of these liver DEMs between the two groups (Fig. 1B). A total of 202 DEMs were identified in the liver (Fig. 2A), including 174 upregulated DEMs and 28 downregulated DEMs compared to the control group. In the 20% WFBG group, the hepatic levels of 10 DEMs—including N2-acetyllysine (a marker of protein acetylation), indole-3-pyruvic acid (a tryptophan-derived neuroregulator), branched-chain keto acids (e.g., 2-ketocaproic acid and ketoleucine), hydroxyisocaproic acid (a leucine metabolite with anti-proteolytic activity), pipecolic acid (implicated in mitochondrial function), and the xenobiotic flutamide—were significantly elevated. Concomitantly, 10 DEMs showed reduced abundance, encompassing phospholipids PC(10:0/10:0) and PC(20:1) (critical for membrane integrity), sulfated bile acid conjugates (glycolithocholic acid 3-sulfate), the redox cofactor NAD, antioxidant delta-tocotrienol, the opioid derivative diphenoxylate, and diverse drug-like compounds. The observed changes in hepatic DEMs in the 20% WFBG group reflect multifaceted shifts in liver metabolism, involving amino acid metabolism, mitochondrial function, membrane integrity, redox balance, and xenobiotic processing. The top-10 liver DEMs identified between the 20% WFBG and control groups are listed in Table 1, with further details provided in the Supplementary Table 1.

Fig. 1.

Fig. 1

Fig. 1

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Liver metabolome analysis. (A) OPLS-DA of the liver metabolites from the control and 20% WFBG groups. (B) Hierarchical cluster analysis of the metabolome from the control and 20% WFBG groups. Heat map representation of metabolites that differed significantly between liver samples of two groups. Each block represents the abundance of one metabolite from one sample. CKS, control group; SM, 20% WFBG group.

Fig. 2.

Fig. 2

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Volcanic map and pathway analysis of identified serum metabolites related to WFBG. (A) Volcanic map of the 202 liver DEMs in the control and 20% WFBG groups. Volcano plot representing the significant variables in the discrimination of serum metabolites from two groups. Gray, metabolites with unchanged abundance; Red, up-regulated metabolites; Green, down-regulated metabolites. Red and blue square in volcano plots are model-separated metabolites following the conditions of P-value of t-test < 0.05 and Fold Change (FC) > 2.0, or FC < 0.5. (B) The KEGG pathways of DEMs from the control and 20% WFBG group comparisons were displayed. Advanced bubble chart shows the enrichment of DEMs in signaling pathways. The x-axis represents the rich factor (rich factor = number of DEMs enriched in the pathway/number of all metabolites in the background metabolites set). The y-axis represents the enriched pathway. Size of the bubble represents the number of DEMs enriched in the pathway, and the color represents enrichment significance.

Table 1.

Identification results of top-10 different metabolites in the liver between the 20% WFBG and control groups.

No. Metabolite m/z RT(s) VIP FC Mode P-value
1 Flutamide 275.06 5.60 1.68 1.84 up 0.0470
2 Indole-3-pyruvic acid 202.05 28.20 1.71 2.42 up 0.0451
3 N2-Acetyllysine 189.12 227.20 2.31 2.72 up 0.0056
4 Gly-Asp 189.05 239.60 1.94 1.64 up 0.0095
5 Hydroxyisocaproic acid 131.07 43.30 1.86 1.95 up 0.0212
6 2-Ethyl-2-hydroxybutyric acid 131.07 43.30 1.86 1.95 up 0.0212
7 2-Hydroxyhexanoic acid 131.07 43.30 1.86 1.95 up 0.0212
8 Pipecolic acid 130.09 180.80 1.56 1.73 up 0.0343
9 2-Ketocaproic acid 129.06 25.20 1.88 2.39 up 0.0486
10 3-Methyl-2-oxovaleric acid 129.06 25.20 1.88 2.39 up 0.0486
11 Nicotinamide adenine dinucleotide (NAD) 664.12 245.30 1.97 0.41 down 0.0088
12 PC(10:0/10:0) 566.38 92.00 1.85 0.25 down 0.0494
13 PC(20:1) 564.37 90.00 2.06 0.20 down 0.0271
14 1H-Indole-4-carboxamide, N-[(1,2-dihydro-4,6-dimethyl-2-oxo-3-pyridinyl)methyl]-3-methyl-1-[(1S)-1-methylpropyl]-6-[6-(1-piperazinyl)-3-pyridinyl]- 527.31 310.70 2.49 0.32 down 0.0167
15 Tauro-beta-muricholic acid 514.28 69.20 1.56 0.51 down 0.0399
16 Glycolithocholic acid 3-sulfate 512.27 91.80 1.66 0.41 down 0.0421
17 Diphenoxylate 453.25 274.40 2.20 0.35 down 0.0144
18 delta-Tocotrienol 397.31 150.80 1.62 0.45 down 0.0351
19 [(1aR,2S,2aS,5R,5aS,6S,7aS)-2-Acetyloxy-5-hydroxy-2a,7a-dimethyl-5-propan-2-yl-2,3,4,5a,6,7-hexahydro-1aH-azuleno[6,7-b]oxiren-6-yl] (Z)-2-methylbut-2-enoate 395.24 292.90 2.18 0.43 down 0.0044
20 5-Acetoacetamido-2-benzimidazolone 234.09 257.30 1.78 0.46 down 0.0344
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WFBG, wet fermented brewer’s grain; m/z, mass-to-charge ratio; RT, retention time; VIP, variable importance in the projection; FC, fold change, 20% WFBG group vs. control group.

KEGG pathway analysis

A total of 2392 liver metabolites detected in positive/negative ion modes were subjected to KEGG pathway analysis, identifying 202 DEMs (Supplementary Table 1). MetaboAnalyst-based functional enrichment analysis (Fig. 2B) revealed 40 significantly perturbed hepatic metabolic pathways in the 20% WFBG group (P < 0.05). The enriched pathways comprised metabolic pathways (involving 17 DEMs), 2-oxocarboxylic acid metabolism (4 DEMs), biosynthesis of cofactors (4 DEMs), glycine, serine and threonine metabolism (4 DEMs), ABC transporters (4 DEMs), carbon metabolism (4 DEMs), biosynthesis of amino acids (4 DEMs), fatty acid biosynthesis (4 DEMs), and fatty acid metabolism (4 DEMs). Glycine, serine and threonine metabolism, fatty acid biosynthesis, and ABC transporters emerged as the most prominent hepatic metabolic pathways (P < 0.05), potentially playing key roles in the 20% WFBG group. Thus, we further explored the metabolite alterations within these three metabolic pathways. Among these DEMs, the levels of glyoxylic acid, tryptophan, 5,10-methylene-THF, and 3-hydroxypyruvic acid (involved in glycine, serine, and threonine metabolism); malonic acid, myristic acid, and palmitic acid (related to fatty acid biosynthesis); and valine, cystine, and a quinolone derivative (5-amino-1-cyclopropyl-7-[(3R,5S)-3,5-dimethylpiperazin-1-yl]-6,8-difluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid, associated with ABC transporters) were all elevated. The concurrent elevation of metabolites across these pathways indicates that 20% WFBG may reprogram hepatic amino acid metabolism, lipid biosynthesis, and substance transport via synergistic modulation of multiple pathways. The detailed outcomes of metabolic pathway analysis are presented in the Supplementary Table 2.

Correlation analysis of liver metabolites

Notably, significant correlations were observed among liver DEMs (|r|> 0.8, P < 0.05). For instance, desmethylverapamil showed positive correlations with fluctuations in diuron–desdimethyl, candoxatril, and 3-isoxazolecarboxamide, N,N-dicyclohexyl-5-cyclopropyl-. In contrast, glutathionylaminopropylcadaverine exhibited negative correlations with changes in NAD, N-isobutyl-3-nitrobenzenesulfonamide, and diphenoxylate. Additional correlations among liver DEMs are visualized in the correlation heatmap (Fig. 3).

Fig. 3.

Fig. 3

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Correlation analysis of liver metabolites. The color of each dot denotes the category of DEMs, while the connecting lines represent the magnitude of correlation coefficients between metabolites at corresponding positions.

Discussion

Liver non-targeted metabolomics profiles of WFBG

Using non-targeted UHPLC-MS/MS liver metabolomics, this study investigated the hepatic regulatory mechanisms of WFBG in broilers. Metabolomic profiling revealed significant deviations in liver metabolic profiles of the 20% WFBG group compared to the control group, indicating profound metabolic reprogramming. A total of 202 liver metabolites were identified as DEMs in response to WFBG, including up-regulated DEMs (e.g., N2-acetyllysine, 2-ketocaproic acid, 2-hydroxyhexanoic acid) and down-regulated DEMs (e.g., PC (10:0/10:0), PC (20:1), NAD, delta-tocotrienol). Pathway analysis indicated that these DEMs were primarily associated with glycine, serine, threonine metabolism, fatty acid biosynthesis, and ABC transporter pathways. The alterations in these hepatic metabolites and corresponding metabolic pathways may offer novel evidence for elucidating the mechanism of action of WFBG.

Glycine, serine, threonine metabolism plays a central role in cell proliferation, antioxidant stress, and homeostasis through one carbon unit cycling, energy metabolism, and substance synthesis26. Its metabolic abnormalities are closely related to various diseases, and in-depth analysis of this pathway can provide theoretical basis for disease diagnosis, treatment, and nutritional intervention26. Research has found that metabolites of glyoxylic acid, tryptophan, 5,10-methylene-THF, and 3-hydroxypyruvic acid are significantly upregulated in the metabolic pathways of glycine, serine, and threonine pathway. Glyoxylic acid, a product of glycine decomposition catalyzed by glycine oxidase, participates in gluconeogenesis or converts to oxalic acid27. 5,10-Methylene-THF serves as a key one-carbon metabolic carrier and intermediary in serine-to-glycine conversion28. 3-Hydroxypyruvic acid is a precursor for serine synthesis (via 3-phosphoglycerate dehydrogenation) or an intermediate in serine catabolism29. Tryptophan, though an independent amino acid, influences one-carbon metabolism through metabolites like kynurenine; its upregulation may reflect transport or degradation dysregulation30. B vitamins (e.g., folate precursors) in WFBG drive 5,10-methylene-THF accumulation by serving as THF synthesis substrates, while enhanced serine hydroxymethyltransferase (SHMT) activity elevates serine consumption, causing compensatory upregulation of 3-hydroxypyruvic acid as a serine biosynthetic precursor31. The resulting 5,10-methylene-THF upregulation promotes DNA methylation and purine synthesis, potentially underlying effects of WFBG on intestinal mucosal proliferation or antitumor activity. Mechanistically, WFBG can enhance the activity of serine hydroxymethyltransferase (SHMT) in liver glycine/serine metabolism by enriching gut butyric acid producing bacteria, providing folate precursors, etc., promoting the conversion of serine to glycine and generating 5,10-methylene-THF to enhance one carbon metabolism. At the same time, butyric acid inhibits liver gluconeogenesis, leading to the accumulation of 3-hydroxypyruvic acid, a precursor for serine synthesis, while glyoxylate produced by glycine decomposition enhances the supply of glutathione synthesis precursor and improves liver antioxidant capacity. In addition, propionic acid from bacterial metabolism inhibits serine dehydratase to maintain the stability of the serine/glycine metabolic pool. Ultimately, amino acid interactions are coordinated through the “dietary fiber microbiota one carbon metabolism” axis, resulting in systematic changes in metabolites such as glyoxylate and 3-hydroxypyruvic acid. For WFBG The application in metabolic regulation provides pathway level basis.

ABC transporters (ATP binding cassette transporters) is a superfamily of transmembrane transporters that drive substrate transmembrane transport through ATP hydrolysis32. Valine, a branched-chain essential amino acid, plays a pivotal role in protein synthesis, energy production (particularly in muscle tissue via β-oxidation), and mTOR-mediated signaling for cell growth33. Cystine, a dimer of cysteine linked by a disulfide bond, serves as a precursor for glutathione (GSH) synthesis, critical for antioxidant defense, and redox homeostasis, while also stabilizing protein structures through disulfide bridges34. 5-amino-1-cyclopropyl-7-[(3R,5S)-3,5-dimethylpiperazin-1-yl]-6,8-difluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid, a quinoline carboxylic acid derivative, is characterized by fluoro-substituted heterocyclic rings and a piperazine moiety. This compound is likely a xenobiotic or drug metabolite; its structure resembles fluoroquinolone antibiotics, suggesting potential antibacterial activity or involvement in xenobiotic detoxification via ABC transporter-mediated efflux35. Collectively, these metabolites—upregulated in the ABC transporter pathway—highlight roles in amino acid homeostasis, antioxidant defense, and xenobiotic metabolism, with ABC transporters modulating their cellular efflux or uptake to maintain metabolic balance.

Malonic acid, a key intermediate in fatty acid biosynthesis, serves as the primary source of two-carbon units for elongating fatty acid chains, while also inhibiting mitochondrial β-oxidation by blocking carnitine palmitoyltransferase 1 (CPT1) to regulate energy metabolism36. Myristic acid (C14:0), a saturated fatty acid, plays a crucial role in protein myristoylation, facilitating membrane protein targeting and signal transduction37. Additionally, it serves as a precursor for the synthesis of longer-chain fatty acids in lipid anabolism38. Palmitic acid (C16:0), the most abundant saturated fatty acid in mammals, is essential for phospholipid biosynthesis, energy storage as triglycerides, and can modulate insulin sensitivity when excessively accumulated, while also acting as a substrate for palmitoylation to regulate protein trafficking and enzyme activity39. Collectively, these metabolites are central to lipid anabolism, energy homeostasis, and post-translational protein regulation in cellular physiology. The synergistic upregulation of malonic acid, myristic acid, and palmitic acid in the fatty acid biosynthesis pathway indicates that WFBG enhances fatty acid anabolism and carbon chain elongation through multi-target regulation. This regulatory effect presents a dual role: In livestock farming, WFBG promotes saturated fatty acid synthesis (e.g., palmitic acid), enhancing intramuscular fat deposition to improve meat quality. As a mitochondrial respiratory inhibitor, malonic acid accumulation induces cellular autophagy through ATP depletion, which is consistent with the evidence of WFBG’s anti-tumor activity. This regulatory paradigm provides a pathway-level theoretical basis for the application of WFBG as a functional unconventional feed ingredient (to promote animal growth) or metabolic syndrome intervention (via lipid metabolism regulation).

Conclusion

In summary, the results consistently showed that WFBG exerted a significant positive regulatory effect on liver function of broiler chickens, as evidenced by the modulation of 202 DEMs (174 up-regulated and 28 down-regulated) associated with key metabolic pathways, including glycine/serine/threonine metabolism, fatty acid biosynthesis, and ABC transporter pathways. Additionally, previous studies by our team have shown that liver metabolomic changes are intricately linked to WFBG-mediated gut microbiota remodeling, particularly the enrichment of Bacteroides and unclassified Eubacteriales in the 20% WFBG group. These microbial alterations potentially facilitate nutrient absorption via enhanced carbohydrate, amino acid, and nucleotide metabolic pathways. Collectively, these findings provide a theoretical basis for understanding liver health mechanisms in broilers at the metabolomic level, highlighting WFBG’s potential as an unconventional feed ingredient to optimize hepatic metabolic homeostasis and mitigate metabolic disorders in poultry production. Future studies may explore the dose-dependent effects of WFBG on liver–transcriptome–microbiome crosstalk to further delineate the underlying regulatory networks. This study has limitations: hepatic metabolomic changes unlinked to phenotypes, only hepatic metabolism studied, no WFBG regulatory mechanisms. Future studies need larger cohorts and multi-omics to validate and clarify pathways.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (12.7MB, zip)

Author contributions

**Zhongjian Gong:** Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing-original draft. **Qin Wang:** Software, Formal analysis, Investigation, Data curation, Funding acquisition, Writing—reviewing &; editing. **Yuanfeng Li:** **Conceptualization, Supervision, Project administration, Funding acquisition, Writing—reviewing &; editing.**

Funding

This research was supported by the Sichuan Provincial Science and Technology Plan Project (No. 2023JDRC0126), Dazhou Vocational and Technical College Skills Master (Wang Qin Studio), and the Doctoral Research Start-up Fund of Liaocheng University (No. 2024318052415).

Data availability

Data are available in the manuscript and supplementary materials. Metabolomics data are deposited in MetaboLights (accession: MTBLS13642; [https://www.ebi.ac.uk/metabolights/MTBLS13642].

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (12.7MB, zip)

Data Availability Statement

Data are available in the manuscript and supplementary materials. Metabolomics data are deposited in MetaboLights (accession: MTBLS13642; [https://www.ebi.ac.uk/metabolights/MTBLS13642].

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