Fermented corn germ meal modulates intestinal inflammation and influences gut microbial succession in broiler chickens

Abstract

This study explored the effects of fermented corn germ meal (FCGM) on growth performance, inflammatory response, and intestinal microbiota in broiler chickens. A total of 180 one-d-old AA broilers (initial average body weight of 39.16 ± 0.05 g) were randomly divided into three groups: CON group, basal diet; CGM group, basal diet supplemented with 7% corn germ meal (CGM); FCGM group, basal diet supplemented with 7% FCGM. Each group contained 6 replicates with 10 broilers per replicate. The experimental period lasted for 42 d. Compared with unfermented CGM, fermentation significantly increased the contents of crude protein (CP), lactic acid (LA), acetic acid (AA), and propionic acid (PA) (P < 0.05), while reducing crude fiber (CF) content (P = 0.036). The FCGM had no significant impact on growth performance of broiler chickens (P > 0.05), but significantly increased cecal AA content at 21 and 42 d (P < 0.05). Additionally, FCGM modulated the expression of G protein-coupled receptors (GPCRs) and the mitogen-activated protein kinase (MAPK) signaling pathway in cecal tissue, with downregulated relative mRNA expression of ERK and p38 at d 42, and altered inflammatory status via upregulation of the anti-inflammatory cytokine IL-10 relative mRNA expression at d 21 (P < 0.05). Additionally, FCGM stabilized ileal and cecal microbiota succession. Among these, Firmicutes and Lactobacillus are the dominant bacterial groups, and their abundance showed significant changes over time. Ileal microbiota assembly was dominated by stochastic processes, while cecal microbiota was influenced by both deterministic and stochastic processes. These findings indicate FCGM improves broiler gut health via a metabolite-receptor-microbiota, offering a viable nutritional strategy for antibiotic-free poultry production.

1. Introduction

The diversity and spatiotemporal dynamics of the gut microbiota in broiler chickens are influenced by a multitude of factors, including age, genotype, rearing practices, diet, and nutritional supplements (Hitch et al., 2022). These microbial communities are not only involved in the digestion of feed and the absorption of nutrients, but also contribute to the host's energy acquisition and health through the production of metabolic byproducts such as short-chain fatty acids (SCFAs) (Krajmalnik-Brown et al., 2012). Nutritional factors, such as feed ingredients, fiber content, protein, and trace elements, in conjunction with farm management practices, such as stocking density, temperature, ventilation, feed and water availability, treatment, vaccination, humidity, and litter management, have the potential to cause dysbiosis of the gut microbiota.

Short-chain fatty acids including acetic acid (AA), propionic acid (PA), and butyric acid (BA), represent the main end products of microbial fermentation in the gut. They are associated with the regulation of inflammation and the maintenance of intestinal barrier integrity. Short-chain fatty acids influence gut microbiota by binding to specific receptors on the host cell surface, such as G protein-coupled receptor (GPR) 41, GPR43, GPR109A and olfactory receptor 51E2 (OR51E2) (Dalile et al., 2019). The activation of these receptors can lead to further signaling cascades, including the mitogen-activated protein kinase (MAPK) pathway (Dalile et al., 2019). In addition, SCFAs have the ability to promote gene transcription by affecting histone acetylation and deacetylation, a process achieved by inhibiting histone deacetylase (HDAC) activity or increasing histone acetyltransferase activity (Dalile et al., 2019). These underlying mechanisms indicate that SCFAs may play complex regulatory roles in modulating the inflammatory response, either by directly activating of signaling pathways or by influencing gene expression.

To address these challenges, alternative strategies, particularly fermented feeds, have emerged as promising alternatives. Fermented feeds, as a dietary intervention, can introduce beneficial bacteria into the gut, compete with harmful bacteria for space and resources, and promote healthy gut balance (He et al., 2022). It also facilitates healthier microbial growth by reducing feed viscosity and increasing the production of SCFAs (He et al., 2022). Corn germ meal, a byproduct of oil extraction from corn germ by pressing or leaching, is increasingly recognized as a potential feed ingredient because of its richness in fiber and fermentable substrates (Predescu et al., 2024). The fermentation process can significantly improve nutrient bioavailability and increase the concentration of SCFAs, which play a key role in intestinal health and immunomodulation.

Understanding the dynamic changes and early colonization characteristics of the chicken gut microbiota is crucial for developing novel microbial ecological interventions, optimizing feed formulations, and enhancing feed conversion efficiency (Gao et al., 2017). Therefore, modulating this continuous pattern through dietary interventions (e.g., fermented feeds) may have a profound impact on broiler health and performance.

This study aimed to investigate the effects of fermented corn germ meal (FCGM) on the pattern of gut microbial community succession, SCFA content, G protein-coupled receptors (CPCRs) expression, and inflammatory marker expression in broilers. It was hypothesized that FCGM could improve gut health in broilers by increasing beneficial SCFA production, modulating SCFA receptor expression, thereby promoting healthy gut flora and improving overall health. This research could provide novel insights into the use of fermented feeds to promote gut health and replace antibiotics in poultry production.

2. Materials and methods

2.1. Animal ethics statement

The animal experiment was approved by the Animal Care and Use Committee of the Academy of National Food and Strategic Reserves Administration (registration number: 20240723001).

2.2. Fermented feed preparation

Pichia kudriavzevii B43 (3 × 10⁷ CFU/g) and Lactobacillus plantarum LP17-1 (1 × 10⁹ CFU/g) were obtained from the Academy of National Food and Strategic Reserves Administration (Beijing, China). Bacillus licheniformis BL15-4 (2 × 10¹¹ CFU/g) and Bacillus subtilis BS15-3 (2 × 10¹¹ CFU/g) were purchased from Beijing Smistyle Sci. & Tech. Development Co., Ltd. (Beijing, China). Cellulase (10,000 U/g) and acid protease (60,000 U/g) were purchased from Cangzhou Sunson Enzyme Biotechnology Co., Ltd. (Cangzhou, Hebei, China). The substrate composition of the FCGM was 95% CGM, 1% ammonium sulfate, 0.5% glucose, 2% sodium bicarbonate, 1% corn, and 0.5% soybean meal. The CGM was purchased from Runfeng Feed Distribution Office (Shenyang, Liaoning, China). Corn and soybean meal were purchased from Taikang Biotechnology Co., Ltd. (Tianjin, China). Ammonium sulfate, glucose, and sodium bicarbonate were all purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). None of the raw materials has been sterilized. Additionally, Pichia kudriavzevii B43 (1 × 10⁶ CFU/g), Lactobacillus lactis LP17-1 (1 × 10⁶ CFU/g), Bacillus subtilis BS15-3 (1 × 10⁸ CFU/g), cellulase (100 U/g), and acid protease (100 U/g) were added simultaneously to the fermentation system. The fermentation temperature was 37 °C, the fermentation time was 48 h, and the feed-to-water ratio was 1:0.6 (wt/v). Briefly, the bacterial mixture, bacterial powder, enzymes, fermentation substrate, and water were mixed and then loaded into a fermentation bag (with an exhaust valve only). After fermentation, the fermented feed was dried at 35 °C for 3 d and then set aside.

2.3. Experimental design and animals

The animal experiment was performed at the Wuqing base of the Academy of National Food and Strategic Reserves Administration (Tianjin, China). A total of 180 one-d-old male AA broiler chickens (average body weight 39.16 ± 0.05 g) were purchased from a commercial hatchery (Huadu Yukou Co., Ltd., Beijing, China) and were randomly divided into three groups: the basal diet group (CON), the CGM group, and the FCGM group, with six replicates of 10 chicks in each group. The trial period was 42 d. The dietary supplementation level of both CGM and FCGM was 7%, which was determined based on our previous preliminary experiments focusing on growth performance and serum biochemical indices of broilers. The broilers were recorded as d 1 of the experiment within 24 h after hatching. According to the experimental protocol, a total of four samples were taken (d 1, 7, 21, and 42 of the experiment), and one chicken was randomly selected from each replicate each time.

2.4. Diets and housing

The diets were designed to meet the nutrient requirements of GB/T 5916-2020 (China National Standard, 2020) (Table 1, Table 2). All the feeds were free of antibiotics. Broilers were housed in 4-layer cages, provided with unhindered access to feed and water, 24-h light, and regular cleaning of chicken excrement. Immunization was performed according to routine procedures, and the temperature and humidity in the chicken housing were controlled according to the Arbor Acres commercial broiler management guidelines. The feeding and health status of the chickens were observed daily.

Table 1.

Ingredients and nutrient levels of experimental diets (1–21 d, air-dried basis, %).

Items	Groups1
	CON	CGM	FCGM
Ingredients
Corn	58.60	52.41	53.14
Soybean meal	33.15	31.00	30.48
Corn germ meal	0.00	7.00	0.00
Ferment corn germ meal	0.00	0.00	7.00
Corn gluten meal	1.00	1.19	1.00
Soybean oil	3.75	4.86	4.81
CaHPO4	1.58	1.55	1.56
Limestone	1.16	1.19	1.19
NaCl	0.30	0.30	0.30
Choline chioride (50%)	0.15	0.15	0.15
Vitamin premix2	0.05	0.05	0.05
Mineral premix3	0.20	0.20	0.20
Met (98%)	0.06	0.07	0.07
Lys-HCl (98.5%)	0.00	0.02	0.03
Thr	0.00	0.01	0.02
Total	100.00	100.00	100.00
Nutrients4
CP	20.87	21.25	21.37
Ca	0.87	0.90	0.92
P	0.64	0.68	0.74
ME, MJ/kg	12.75	12.74	12.74
Lys	1.08	1.08	1.07
Met	0.35	0.35	0.35
Thr	0.81	0.81	0.81

CP = crude protein; ME = metabolizable energy; CGM = corn germ meal; FCGM = fermented corn germ meal.

¹CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM.

²Vitamin premix provides per kg of diets: VA 50,000 IU, VD₃ 6250 IU, VE 42.5 mg, VK₃ 7.5 mg, VB₁ 7.25 mg, VB₂ 16 mg, VB₆ 7.5 mg, VB₁₂ 0.0425 mg, niacin 82.5 mg, pantothenic acid 24 mg, folic acid 2.5 mg, biotin 300 mg.

³Mineral premix provides per kg of diets: Cu 12 mg, Zn 120 mg, Fe 100 mg, Mn 160 mg, Se 0.2 mg, I 0.6 mg, Co 0.1 mg.

⁴ME is calculated by Tables of Feed Composition and Nutritional Values in China (Institute of Animal Science, CAAS, 2023), while the others are analyzed values.

Table 2.

Ingredients and nutrient levels of experimental diets (22–42 d, air-dried basis, %).

Items	Groups1
	CON	CGM	FCGM
Ingredients
Corn	58.54	52.33	53.06
Soybean meal	32.97	30.82	30.30
Corn germ meal	0.00	7.00	0.00
Ferment corn germ meal	0.00	0.00	7.00
Corn gluten meal	1.00	1.19	1.00
Soybean oil	3.88	5.01	4.96
CaHPO4	1.58	1.55	1.56
Limestone	1.16	1.19	1.19
NaCl	0.30	0.30	0.30
Choline chioride (50%)	0.15	0.15	0.15
Vitamin premix2	0.06	0.06	0.06
Mineral premix3	0.30	0.30	0.30
Met (98%)	0.06	0.07	0.07
Lys-HCl (98.5%)	0.00	0.02	0.03
Thr	0.00	0.01	0.02
Total	100.00	100.00	100.00
Nutrients4
CP	20.24	21.16	20.99
Ca	0.99	1.04	1.02
P	0.72	0.70	0.73
ME, MJ/kg	12.77	12.77	12.77
Lys	1.07	1.06	1.06
Met	0.35	0.35	0.35
Thr	0.80	0.80	0.80

CP = crude protein; ME = metabolizable energy; CGM = corn germ meal; FCGM = fermented corn germ meal.

¹CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM.

²Vitamin premix provides per kg of diets: VA 60,000 IU, VD₃ 7500 IU, VE 51 mg, VK₃ 9 mg, VB₁ 8.7 mg, VB₂ 19.2 mg, VB₆ 9 mg, VB₁₂ 0.051 mg, niacin 99 mg, pantothenic acid 28.8 mg, folic acid 3 mg, biotin 360 mg.

³Mineral premix provides per kg of diets: Cu 18 mg, Zn 180 mg, Fe 150 mg, Mn 240 mg, Se 0.3 mg, I 0.9 mg, Co 0.15 mg.

⁴CP, Ca, and P are analyzed values, while the others are calculated by Tables of Feed Composition and Nutritional Values in China (Institute of Animal Science, CAAS, 2023).

2.5. Sample collection

On d 1, 7, 21, and 42 of the experiment, the broilers were euthanized by exsanguination, and the abdominal cavities were dissected. The intestinal segments were separated, and the contents of the ileum and cecum were collected in 2-mL sterile centrifuge tubes, quickly frozen in liquid nitrogen, and then stored at −80 °C for measurement. On d 21 and 42, broiler cecum tissues were also collected in 2-mL sterile centrifuge tubes, snap-frozen in liquid nitrogen, and then stored in a −80 °C refrigerator for testing.

2.6. Determination of growth performance

During the test period, the health status of the broilers was observed daily, and the mortality and culling of broilers were recorded. The fasting body weight of broilers and the remaining feed weight were weighed as a replicate unit on d 1, 7, 21, and 42, and the average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR) of each stage were calculated.

2.7. Determination of conventional nutritional components

Dry matter (DM) content was determined by oven-drying at 105 °C to constant weight, following GB/T 6435-2014 (China National Standard, 2014), using an electrothermal constant-temperature forced-air drying oven (DHG-9140AL, Beijing Luxi Technology Co., Ltd., Beijing, China). Crude protein (CP) content was determined by the Kjeldahl method following GB/T 6432-2018 (China National Standard, 2018b), using an automatic Kjeldahl nitrogen analyzer (Kjeltec 8400, FOSS Analytical A/S, Hilleroed, Denmark). The content of crude fiber (CF) was determined by an automatic fiber analyzer (2000i, Ankom Technology, Macedon, NY, USA) with reference to GB/T 6434-2022 (China National Standard, 2022). Ether extract (EE) content was determined by the Soxhlet extraction method following GB/T 6433-2006 (China National Standard, 2006), using an automatic Soxhlet extractor (Soxtec 8000, FOSS Analytical A/S, Hilleroed, Denmark). Total calcium (Ca) and phosphorus (P) contents were determined according to GB/T 6436-2018 and GB/T 6437-2018 (China National Standard, 2018a, 2018c) using a ceramic fiber muffle furnace (PYRAMID TX, Beijing Pyramid Technology Co., Ltd., Beijing, China). Amino acid contents were analyzed according to GB/T 18246-2019 (China National Standard, 2019) using an amino acid analyzer (A300, MembraPure GmbH, Bodenheim, Germany).

2.8. Determination of lactic acid (LA) and SCFAs contents

Determination of LA and SCFAs contents in feed: 1 g feed sample was added to 10 mL ultrapure water, sonicated for 30 min, and centrifuged at 2750 × g for 5 min, after which the supernatant was collected and filtered through a 0.45-μm filter membrane.

Determination of LA and SCFAs contents in cecal contents (Zhu et al., 2020): 0.5 g of cecal contents was weighed and mixed evenly with 1 mL of ultrapure water and centrifuged at 3000 × g for 10 min. The 0.5 mL of the supernatant was mixed evenly with 0.2 mL of 25% metaphosphoric acid solution and centrifuged at 15,000 × g for 10 min. The supernatant was collected and filtered through a 0.45-μm filter membrane.

The chromatographic conditions were as follows: the chromatographic column was C18 (5 μm × 4.6 mm × 250 mm), the mobile phase was 0.1% phosphoric acid: methanol (95:5), the flow rate was 1 mL/min, the column temperature was 35 °C, the wavelength of the UV detector was 210 nm, the injection volume was 20 μL, and the test time was 45 min.

2.9. DNA extraction and PCR amplification

Total microbial genomic DNA was extracted from the ileum and cecum contents of the broiler chicken samples via the E.Z.N.A. soil DNA Kit (D5625, Omega Bio-tek, Inc., Norcross, GA, USA) according to the manufacturer’s instructions. The quality and concentration of the DNA were determined via 1.0% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA), and the samples were stored at −80 °C until further use. The hypervariable region V3–V4 of the bacterial 16S rRNA gene was amplified with the primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) via a T100 Thermal Cycler PCR thermocycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) (Liu et al., 2015). The PCR mixture included 10 μL of 2 × Pro Taq, 0.8 μL of each primer (5 μmol/L), 10 ng/μL template DNA, and ddH₂O to 20 μL. The PCR amplification cycling conditions were as follows: initial denaturation at 95 °C for 3 min; 29 cycles of denaturation at 95 °C for 30 s, annealing at 53 °C for 30 s and extension at 72 °C for 45 s; and a single extension at 72 °C for 10 min, ending at 10 °C. The PCR products were extracted and purified from a 2% agarose gel, followed by quantification using a Qubit 4.0 instrument (Thermo Fisher Scientific Inc., Waltham, MA, USA). Illumina sequencing was then performed.

Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina NextSeq 2000 platform (Illumina, Inc., San Diego, CA, USA) according to standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

2.10. Data processing

Raw FASTQ files were demultiplexed via an in-house Perl script and then quality-filtered via fastp version 0.19.6 (Chen et al., 2018) and merged via FLASH version 1.2.7 (Magoč and Salzberg, 2011) with the following criteria: 1) the reads were truncated at any site receiving an average quality score of < 20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded; reads containing ambiguous characters were also discarded; 2) only overlapping sequences longer than 10 bp were assembled according to their overlapping sequence. The maximum mismatch ratio of the overlap region is 0.2. Reads that could not be assembled were discarded. 3) Samples were distinguished according to the barcode and primers, and the sequence direction was adjusted to include exact barcode matching and two nucleotide mismatches in primer matching. The optimized sequences were subsequently clustered into operational taxonomic units (OTUs) via UPARSE 7.1 (Edgar, 2013; Stackebrandt and Goebel, 1994) with a 97% sequence similarity level. The most abundant sequence for each OTU was selected as a representative sequence. The OTU table was manually filtered, i.e., chloroplast sequences in all samples were removed. To minimize the impact of sequencing depth on the subsequent analysis of alpha diversity and beta diversity data, all sample sequences were drawn flat at the minimum number of sample sequences (38886), which still yielded an average Good’s coverage of 99%.

The taxonomy of each OTU representative sequence was analyzed via RDP Classifier version 2.2 against the 16S rRNA gene database (e.g., Silva v138) with a confidence threshold of 0.7 (Wang et al., 2007). Metagenomic function was predicted via Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) on the basis of representative OTU sequences (Douglas et al., 2020). PICRUSt2 is software that contains a series of tools as follows: HMMER was used to align representative OTU sequences with reference sequences. EPA-NG and Gappa were used to put representative OTU sequences into a reference tree. The castor gene was used to normalize the 16S gene copy numbers. MinPath was used to predict gene family profiles and locate them into gene pathways. The entire analysis process was performed according to the protocols of PICRUSt2.

2.11. Real-time quantitative PCR

The cecum tissue was milled, total RNA was extracted via Total RNA Extraction Reagent (DP424, Tiangen Biochemical Technology Co., Ltd., Beijing, China), and the purity and concentration of the extracted RNA were determined. cDNA was synthesized via reverse transcription via a FastKing cDNA First Strand Synthesis Kit (KR116, Tiangen Biochemical Technology Co., Ltd., Beijing, China). cDNA levels were determined via fluorescence quantitative PCR via a FastFire Rapid Fluorescence Quantitative PCR Premix Kit (FP207, Tiangen Biochemical Technology Co., Ltd., Beijing, China). The procedure was carried out with reference to the instruction manual of the kit. The expression of CPCRs (GPR109A, GPR43, and GPR41), HDACs, ERK, JNK, p38, NF-κB, IL-1β, IL-6, and IL-10 was detected in the cecum. β-Actin was used as the internal reference gene, and the relative mRNA expression of target genes was calculated via the 2^−ΔΔCt method (Livak and Schmittgen, 2001). All the primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China). The sequences of the gene primers are shown in Table 3. The full names of the abbreviations of all genes can be found in Table S1.

Table 3.

Gene primer sequences.

Items	Primer sequence (5′-3′)	Accession number	Length, bp
GPR41	F: GAAGGTGGTTTGGGAGTGAA	XM_040693461.2	103
	R: CAGAGGATTTGAGGCTGGAG
GPR43	F: AACGCCAACCTCAACAACTC	XM_046934039.1	149
	R: TGGGAGAAGTCATCGTAGCA
GPR109A	F: TTTCTGTTTCCACCTCAAGTCC	NM_030701.3	190
	R: GCTGCCCTGTCGGTTCATAG
HDAC	F: GCGGCGGCAAGAAGAAAGTC	XM_046912048.1	117
	R: TTAGGAGCAAGTTGTGAGTCATTCG
p38	F: GCAGAACAGTGGCGTTATCCTC	XM_015296032.4	81
	R: TCGTCATCGTTTCCATTAAAGTCAAAG
ERK	F: GGCTGTTTCCCAATGCTGACC	NM_204150.2	90
	R: CTTGCTCCACTTCAATTCGCTTATG
JNK	F: TATGGAACTGATGGATGCCAACTTG	XM_046926861.1	147
	R: TTGCTGGGTTTCAAATCTCTGTGG
NF-κB	F: TGATGATGATGATGAAGGAATCGTACC	NM_001296395.1	109
	R: TGCCGCTGCTGCTATGTAGG
IL-1β	F: GCCCTCCTCCAGCCAGAAAG	NM_204524.2	112
	R: TGTAGCCCTTGATGCCCAGTG
IL-6	F: GAAATCCCTCCTCGCCAATCTG	NM_204628.2	106
	R: CCTCACGGTCTTCTCCATAAACG
IL-10	F: CGCTGTCACCGCTTCTTCAC	NM_001004414.4	88
	R: TCCCGTTCTCATCCATCTTCTCG
β-Actin	F: CAGCCATGTATGTAGCCATCCAG	NM_205518.2	84
	R: CATCACCAGAGTCCATCACAATACC

2.12. Statistical analysis

An independent-samples t-test was used to analyze nutrient contents in raw materials using SPSS 27.0 (IBM Corporation, Chicago, IL, USA). One-way ANOVA was applied to evaluate other data, followed by Tukey’s post hoc test, via the following model:

Y_ij = μ + T_i + e_ij,

where Y_ij is observed value of the j-th replicate in the i-th treatment group; μ is the overall mean; T_i is the fixed effect of the i-th treatment (i = CON, CGM, and FCGM), e_ij is the random error. Data were presented as means and standard error of the mean (SEM). Significance levels was set as ∗ 0.01 < P ≤ 0.05, ∗∗ 0.001 < P ≤ 0.01, and ∗∗∗ P ≤ 0.001.

3. Results

3.1. Changes in the nutrient composition and organic acid contents of CGM before and after fermentation

Compared with CGM, FCGM exhibited higher contents of CP, LA, AA and PA, but a lower CF content than CGM (P < 0.05; Table 4).

Table 4.

Changes in the nutrient contents of CGM before and after fermentation (dry matter basis).

Items	Groups1		SEM	P-value
	CGM	FCGM
CP, %	20.41b	22.06a	0.057	0.002
EE, %	2.47	1.84	0.065	0.066
CF, %	14.16a	12.55b	0.237	0.036
LA, mg/g	10.64b	34.64a	0.493	<0.001
AA, mg/g	4.74b	23.41a	0.816	<0.001
PA, mg/g	0.38b	1.80a	0.178	0.022

CGM = corn germ meal; FCGM = ferment corn germ meal; CP = crude protein; EE = ether extract; CF = crude fiber; LA = lactic acid; AA = acetic acid; PA = propionic acid.

Values in the same row marked with different lowercase letters are significantly different at P < 0.05. The data are presented as the means and standard error of the mean (SEM) (n = 3).

¹CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM.

3.2. Growth performance

During each test stage and the entire test period, there were no significant differences in ADG, ADFI, and FCR among the test groups (P > 0.05; Table 5).

Table 5.

Effects of FCGM on the growth performance of broilers.

Items	Groups1			SEM	P-value
	CON	CGM	FCGM
Body weight, g
1 d	39.16	39.12	39.16	0.022	0.739
ADG, g/d
1–7 d	12.49	12.76	14.12	0.117	0.080
8–21 d	28.42	27.62	29.55	0.357	0.076
22–42 d	49.86	52.67	51.91	0.819	0.397
ADFI, g/d
1–7 d	12.38	12.6	12.71	0.106	0.443
8–21 d	41.09	41.08	42.46	0.426	0.333
22–42 d	80.16	82.75	83.06	0.788	0.297
FCR
1–7 d	0.99	0.99	0.97	0.006	0.317
8–21 d	1.45	1.49	1.44	0.012	0.208
22–42 d	1.61	1.58	1.60	0.019	0.748

CGM = corn germ meal; FCGM = ferment corn germ meal; ADG = average daily gain; ADFI = average daily feed intake; FCR = feed conversion rate.

Values in the same row marked with different lowercase letters are significantly different at P < 0.05. The data are presented as the means and standard error of the mean (SEM) (n = 3).

¹CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM.

3.3. Lactic acid and SCFA contents of the cecal contents

Compared with the CON group, the FCGM group increased AA and PA contents at 21 d (P < 0.05), FCGM and CGM groups increased AA and LA contents at 42 d (P < 0.05; Table 6).

Table 6.

The contents of LA and short-chain fatty acids (mg/g) in the cecal contents of broilers.

Items	Groups1			SEM	P-value
	CON	CGM	FCGM
d 21
AA	4.00b	2.84c	6.31a	0.401	<0.001
PA	0.26b	0.31ab	0.45a	0.034	0.041
BA	1.46	1.01	1.22	0.099	0.169
d 42
LA	6.81b	9.71a	9.88a	0.450	0.002
AA	8.41b	11.92a	12.44a	0.641	0.010
PA	2.78	4.49	5.07	0.510	0.166
BA	2.30	2.21	2.30	0.236	0.985

CGM = corn germ meal; FCGM = ferment corn germ meal; LA = lactic acid; AA = acetic acid; PA = propionic acid; BA = butyric acid.

Values in the same row marked with different lowercase letters are significantly different at P < 0.05. The data are presented as the means and standard error of the mean (SEM) (n = 3).

¹CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM.

3.4. Expression of the GPCRs and HDAC in cecum

On d 21 and 42, there were no significant differences in the relative mRNA expression levels of GPR43, GPR109A, or HDAC in the cecal tissue among the treatment groups (P > 0.05; Fig. 1A and B). On d 42, compared with the CON group, both the CGM and FCGM groups increased the relative mRNA expression level of GPR41 (P < 0.05; Fig. 1B).

Fig. 1.

Short-chain fatty acid receptors and HDAC expression in broiler cecum tissues. Expression of G protein-coupled receptors (CPCRs) and HDACs in broiler cecum tissues at d 21 (A) and 42 (B). CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. The data are presented as the means and standard error of the mean (SEM) (n = 6). ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. CGM = corn germ meal; FCGM = ferment corn germ meal.

3.5. Expression of the MAPK signaling pathway in cecum

On d 21, there were no significant differences in the relative mRNA expression levels of ERK, JNK or p38 in the cecum tissue among the treatment groups (P > 0.05; Fig. 2A). On d 42, compared with CGM group, FCGM group reduced the relative mRNA expression levels of ERK and p38 (P < 0.05; Fig. 2B).

Fig. 2.

Gene expression of the mitogen-activated protein kinase (MAPK) signaling pathway in broiler cecum tissues. Expression of MAPK pathway-related genes in broiler cecum tissues at d 21 (A) and 42 (B). CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. The data are presented as the means and standard error of the mean (SEM) (n = 6). ∗P < 0.05. CGM = corn germ meal; FCGM = ferment corn germ meal.

3.6. Expression of the inflammatory factors in cecum

On d 21, no differences were observed in the relative mRNA expression levels of NF-κB, IL-1β, or IL-6 in cecum tissues among the treatment groups (P > 0.05; Fig. 3A). Compared with the CON group, the FCGM group increased the relative mRNA expression levels of IL-10 (P = 0.042). On d 42, there were no significant differences in the relative mRNA expression levels of NF-κB, IL-1β, IL-6, or IL-10 in the cecum tissue between the treatment groups (P > 0.05; Fig. 3B).

Fig. 3.

Inflammatory factor gene expression in the cecum tissues of broiler chickens. Inflammatory factor gene expression in broiler cecum tissues at d 21 (A) and 42 (B). CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. The data are presented as the means and standard error of the mean (SEM) (n = 6). ∗P < 0.05. CGM = corn germ meal; FCGM = ferment corn germ meal.

3.7. Correlation of SCFAs with genes in cecum

In the cecum tissue of broilers (Fig. 4), the acetic acid (AA) level was positively correlated with the GPR41 relative mRNA expression levels (P < 0.05). It was negatively correlated with relative mRNA expression levels of ERK, IL-6, and IL-10 (P < 0.05). PA content was positively correlated with the relative mRNA expression levels of GPR41 and GPR43 (P < 0.05) and negatively correlated with the relative mRNA expression levels of ERK and IL-6 (P < 0.05).

Fig. 4.

Correlations between short-chain fatty acids in cecum contents and each gene in cecum and hypothalamic tissues. (A) Correlation between short-chain fatty acid contents in cecum contents and gene expression in cecum tissues of broilers fed 7% FCGM. (B) Correlation between short-chain fatty acid contents in cecum contents and gene expression in hypothalamic tissues of broilers fed 7% FCGM. Box size and color indicate Spearman’s rank correlation coefficient. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001. FCGM = ferment corn germ meal; AA = acetic acid; PA = propionic acid; BA = butyric acid.

3.8. Alpha and beta diversity of broiler intestinal microorganisms

Shannon's index was higher in the ileum at d 1 than at d 7, 21, and 42 in all treatment groups (Fig. 5A). The Shannon index of the cecum was higher at d 42 than at d 1 in all treatment groups (P < 0.05; Fig. 5B). The Shannon index of the ileum was at a high level in all broilers at d 1, followed by a significant decrease, reaching the lowest value at d 7 (Fig. 5C). The Shannon index of the CON and CGM groups recovered from d 7 to 21, and then declined again from d 21 to 42. The Shannon index of the FCGM group increased starting from d 7, and was maintained at a relatively stable level. In the cecum, Shannon's index of the FCGM group increased gradually over time and reached the highest value at d 42, while the CON and CGM groups showed a tendency to increase and then decrease. In addition, Shannon's index was higher in the cecum than in the ileum at all time points except at d 1.

Fig. 5.

Bacterial Shannon index. (A) Bacterial Shannon index of the ileum within each group. (B) Bacterial Shannon index of the cecum within each group. (C) Trend of Shannon indices of the ileum and cecum, with the solid line representing the ileum and the dashed line representing the cecum. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. ∗ 0.01 < P ≤ 0.05, ∗∗ 0.001 < P ≤ 0.01, ∗∗∗ 0.0001 < P ≤ 0.001. The data are presented as the means and standard error of the mean (SEM) (n = 4–6). CGM = corn germ meal; FCGM = ferment corn germ meal.

The beta diversity of the broiler ileum and cecum flora varied over time among the treatment groups (PERMANOVA, P = 0.001). In the ileum, the correlation coefficients of the flora of the CON, CGM, and FCGM groups over time were 0.556, 0.549, and 0.606, respectively (Fig. 6A). In the cecum, the correlation coefficients of bacterial populations over time for the CON, CGM, and FCGM groups were 0.414, 0.397, and 0.434, respectively (Fig. 6B).

Fig. 6.

Principal coordinate analysis (PCoA) of bacterial community composition based on Bray-Curtis distance. (A) Composition of the bacterial community structure in the ileum over time for different treatment groups. (B) Composition of the bacterial community structure in the cecum over time for different treatment groups. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM (n = 4-6). CGM = corn germ meal; FCGM = ferment corn germ meal; PC = principal component.

3.9. Species composition of broiler intestinal microorganisms

The intestinal microflora displayed distinct compositional profiles, with 21 bacterial phyla in the ileum and 22 in the cecum. At the phylum level, temporal shifts in dominance were evident. In the ileum, Proteobacteria were the dominant phylum on d 1; however, it was subsequently supplanted by Firmicutes (Fig. 7A). Conversely, within the cecum, Firmicutes remained the dominant phylum throughout the entire trial (Fig. 7B). While Proteobacteria was predominantly present across the intestine on d 1, Bacteroides abundance showed spatial specificity; it was largely absent in the ileum but relatively high in the cecum on d 21 and 42. Statistical analysis confirmed highly significant temporal differences for Firmicutes and Bacteroidota in both the ileum and cecum across treatment groups (one-way ANOVA, P ≤ 0.001) (Fig. S1A and B).

Fig. 7.

Bacterial structural composition at the phylum level. (A) Bacterial structural composition at the ileal phylum level. (B) Bacterial structural composition over time at the cecum phylum level. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. CGM = corn germ meal; FCGM = ferment corn germ meal.

At the genus level, the bacterial composition was more complex. On d 1, both the ileum and cecum were characterized by a large percentage of low-abundance species (predominantly classified as “other” categories). Following this initial phase, distinct patterns emerged based on gut segment and treatment. In the ileum, Lactobacillus consistently dominated the CON group from d 7 onward (Fig. 8A). In contrast, the CGM and FCGM groups were dominated by Enterococcus at d 7, after which Lactobacillus became the dominant genus. These observations were supported by significance tests indicating that the relative abundance of Lactobacillus, Stenotrophomonas, Acinetobacter, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Pseudomonas, and Delftia in the ileum differed significantly over time (P ≤ 0.05; Fig. S2A).

Fig. 8.

Bacterial structural composition at the genus level. (A) Bacterial structural composition of the ileum at the genus level. (B) Bacterial structural composition of the cecum at the genus level. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. CGM = corn germ meal; FCGM = ferment corn germ meal.

The cecal microbiota exhibited greater complexity than that of the ileum. While Lactobacillus remained the dominant genus, it was followed by Barnesiella (Fig. 8B). Similar to the ileal profile, Enterococcus was briefly enriched in the cecum at d 7 before being replaced by other genera. Statistically significant temporal differences were observed in the cecum for the Ruminococcus_torques_group, Lachnoclostridium, and Shuttleworthia in each treatment group (P ≤ 0.05; Fig. S2B).

3.10. Changes in the relative abundances of major phyla and major genera in the ileum and cecum of broilers

The abundance of microorganisms in the host gut varies with spatial and temporal fluctuations, for which the developmental trajectories of the major phyla and genera were analyzed. In the ileum, the relative abundance of the Firmicutes did not differ between treatment groups at the same time point (P > 0.05; Fig. 9A). In contrast, the relative abundance of Firmicutes was higher (at d 7, 21, and 42 than d 1 when compared within the same treatment group (P ≤ 0.05; Fig. 9B). Further analysis revealed that the relative abundance of ileal Firmicutes in both CON and FCGM groups followed a similar trend, showing a rapid increase in the early stages of growth, peaking at d 7, and then declining, but with the smallest decrease in the FCGM group (Fig. 9C). In the cecum, the relative abundance of Firmicutes was likewise not different between treatment groups at the same time point (P > 0.05; Fig. 9D). In both CON and CGM groups, the relative abundance of Firmicutes was higher at d 7 than at d 1 (P ≤ 0.05; Fig. 9E). In the FCGM group, the relative abundance of cecum Firmicutes increased rapidly during the early growth period and continued to decline after peaking at d 7. In contrast, the relative abundance of Firmicutes in the CON and CGM groups consisted of a small increase in the late growth period of broilers (Fig. 9F). The relative abundance of Firmicutes in both the ileum and cecum peaked at d 7.

Fig. 9.

Changes in relative abundance of major phylum (Firmicutes). (A) Relative abundance of Firmicutes changing in different treatment groups at the same time point in the ileum. (B) Relative abundance of Firmicutes within the same treatment group in the ileum over time. (C) Trend in relative abundance of Firmicutes in the ileum. (D) Relative abundance of Firmicutes changing in different treatment groups at the same time point in the cecum. (E) Relative abundance of Firmicutes in the cecum over time in the same treatment group. (F) Trends in relative abundance of Firmicutes in the cecum. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. ∗ 0.01 < P ≤ 0.05, ∗∗∗∗P ≤ 0.0001 (n = 4–6). CGM = corn germ meal; FCGM = ferment corn germ meal.

In the ileum, the relative abundance of the genus Lactobacillus did not differ among different treatment groups at the same time point (P > 0.05; Fig. 10A). The relative abundance of the genus Lactobacillus in the CON and FCGM groups was higher at d 21 and 42 than at d 1 (P ≤ 0.05; Fig. 10B). The trend of the relative abundance of the genus Lactobacillus was an increase from d 1, reaching the highest value at d 21, followed by a varying degree of decline (Fig. 10C). In the cecum, the relative abundance of the genus Lactobacillus did not differ among different treatment groups at the same time point (P > 0.05; Fig. 10D). The relative abundance of the genus Lactobacillus in the FCGM group was lower at d 42 than at d 1 (P = 0.045; Fig. 10E). The relative abundance of the genus Lactobacillus in the CON and FCGM groups generally decreased over time (Fig. 10F).

Fig. 10.

Changes in relative abundance of major genus (Lactobacillus). (A) Relative abundance of Lactobacillus changing in different treatment groups at the same time point in the ileum. (B) Relative abundance of Lactobacillus within the same treatment group in the ileum over time. (C) Trend in relative abundance of Lactobacillus in the ileum. (D) Relative abundance of Lactobacillus changing in different treatment groups at the same time point in the cecum. (E) Relative abundance of Lactobacillus in the cecum over time in the same treatment group. (F) Trends in relative abundance of Lactobacillus in the cecum. CON group, basal diet; CGM group, basal diet supplemented with 7% corn germ meal; FCGM group, basal diet supplemented with 7% fermented corn germ meal. ∗ 0.01 < P ≤ 0.05, ∗∗ 0.001 < P ≤ 0.01, ∗∗∗ 0.0001 < P ≤ 0.001, ∗∗∗∗P ≤ 0.0001 (n = 4-6). CGM = corn germ meal; FCGM = ferment corn germ meal.

3.11. Analysis of the structure of the intestinal microbial community in broilers

Ecological modeling was employed to investigate the mechanisms underlying the ecological succession of intestinal bacterial communities during broiler development. The results showed that the assembly mechanism of ileal bacterial communities was dominated by ecological drift processes (Fig. 11A). In the cecum, the ecological succession was dominated by the ecological drift process at d 1, and then shifted to heterogeneous selection and dispersal limitation from d 7 (Fig. 11B). At d 7, heterogeneous selection dominated the CON and FCGM groups, and ecological drift dominated the CGM group. At d 21, stochastic processes (dispersal limitation and ecological drift) dominated the CON group, and heterogeneous selection dominated the CGM and FCGM groups. At d 42, the CON and CGM groups were dominated by dispersal limitation and the FCGM group was dominated by heterogeneous selection.

Fig. 11.

Mechanism of bacterial community assembly. (A) Different ecological processes in the ileum over time. (B) Different ecological processes in the cecum over time. Homogeneous selection (HoS): beta nearest taxon index (βNTI) < −2; heterogeneous selection (HeS): βNTI > 2; dispersal limitation (DL): |βNTI| < 2 and based on the Bray-Curtis Raup-Crick index (RC_Bray) > 0.95; homogeneous dispersal (HD): |βNTI| < 2 and RC_Bray < −0.95; drift (DR) and other ecological processes: |βNTI| < 2 and |RC_Bray| < 0.95. CON group, basal diet; CGM group, basal diet supplemented with 7% CGM; FCGM group, basal diet supplemented with 7% FCGM. CGM = corn germ meal; FCGM = ferment corn germ meal.

4. Discussion

The fermentation process significantly increased the CP, LA and PA contents of CGM, while reducing the CF content. This suggests that Bacillus subtilis and Lactobacillus successfully degraded anti-nutritional factors and converted non-starch polysaccharides into microbial biomass and bioactive peptides. Despite these nutritional improvements, FCGM supplementation did not significantly alter growth performance compared to the CON. This finding aligns with the conclusions of studies by Yeh et al. (2018) and Yaqoob et al. (2022). The reason for this discrepancy may lie in the fact that when nutritional requirements are fully met, the benefits of fermented feed are more evident in maintaining metabolic homeostasis and gut health.

A key finding of this study was the significant accumulation of SCFAs, specifically AA and PA, in the cecal digesta of the FCGM group. The SCFAs are critical signaling molecules that lower intestinal pH, inhibiting pathogens while nourishing epithelial cells (Guan et al., 2022; Hills et al., 2019). Crucially, this increase in SCFAs coincided with a significant upregulation of the GPR41 in cecal tissue at d 42. G-protein-coupled receptor 41, which has a high affinity for propionate, plays a vital role in energy metabolism and intestinal motility (Miyamoto et al., 2016). This suggests that FCGM enhances the gut’s capacity to sense metabolites and harvest energy, thereby strengthening the intestinal barrier and maintaining physiological stability.

Beyond metabolic regulation, FCGM exhibited distinct immunomodulatory effects. The MAPK signaling pathway (including ERK, JNK, and p38) acts as a central mediator of inflammatory responses (Fang and Richardson, 2005). This study showed that at d 42, FCGM significantly downregulated the expression of ERK and p38 in cecal tissue. This suppression of proinflammatory signaling pathways is likely linked to the elevated SCFA levels, as PA and AA are known to inhibit MAPK activation (Li et al., 2018). By dampening ERK and p38 signaling, FCGM helps prevent excessive inflammation and preserves mucosal integrity. While no pronounced changes were detected in the levels of downstream inflammatory factors, modulation of these upstream kinases suggests that FCGM contributes to the formation of an anti-inflammatory microenvironment in the cecum.

This study revealed that FCGM stabilizes the dynamic succession of the broiler gut microbiota, with distinct spatial separation observed. The ileum is predominated by Lactobacillus to support nutrient absorption, whereas the cecum maintains a diverse community of Firmicutes and Bacteroidota that facilitates fiber fermentation. The rapid decline of Proteobacteria and the consistent dominance of Firmicutes across all groups indicated healthy gut maturation. However, FCGM treatment further supported this stability, particularly by maintaining higher abundances of beneficial fermenters. While Lactobacillus abundance generally decreased with age, the FCGM group maintained beneficial populations of Ruminococcus and Lachnoclostridium in the cecum. These genera are efficient degraders of complex polysaccharides, driving the production of the SCFAs observed in our metabolic analysis (Schaus et al., 2024).

Finally, ecological modeling highlighted that the ileum and cecum follow different assembly rules. The ileal microbiota was driven largely by ecological drift (stochastic processes) due to rapid transit times and environmental fluctuations (Sprockett et al., 2018). In contrast, the cecal microbiota shifted from stochasticity to deterministic selection as the birds matured. This suggests that the stable, nutrient-rich environment of the cecum exerts specific selection pressures that favor functional bacteria (McCallum and Tropini, 2024). FCGM supplementation appears to optimize this deterministic process, providing the necessary substrates (prebiotics) that select for a robust, SCFA-producing community capable of competitive exclusion of pathogens.

5. Conclusion

In summary, FCGM serves as a functional feed ingredient that improves broiler gut health through a metabolite-receptor-microbiota. By providing specific substrates for fermentation, FCGM enhances the production of SCFAs, which activate GPR41 and suppress inflammatory MAPK signaling (ERK/p38). Simultaneously, it stabilizes the succession of the gut microbiota, favoring a mature, Firmicutes-dominated community. These mechanisms collectively improve local intestinal health, offering a viable nutritional strategy for antibiotic-free poultry production.

CRediT authorship contribution statement

Xiaoyu Ji: Writing – review & editing, Writing – original draft, Software, Formal analysis, Data curation. Beibei He: Supervision, Conceptualization. Li Wang: Supervision, Methodology, Conceptualization. Kuanbo Liu: Conceptualization. Jingjing Shi: Methodology. Junlin Cheng: Methodology. Dan Song: Methodology. Hang Ma: Methodology. Aike Li: Resources. Lin Qiao: Methodology. Tao Duan: Writing – review & editing, Methodology. Yongwei Wang: Writing – review & editing, Supervision, Methodology, Conceptualization.

Data availability

The raw sequencing reads were deposited into the NCBI Sequence Read Archive (SRA) database (accession number: SRP552521).

Declaration of competing interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

Appendix A. Supplementary data

The following is the supplementary data to this article.