Dietary medium-chain fatty acids mitigate Escherichia coli O78 infection in broilers by enhancing intestinal immune barrier and modulating microbiota

Abstract

Avian colibacillosis caused by Escherichia coli O78 (E. coli O78) compromises poultry health and performance. Medium-chain fatty acids (MCFAs) exhibit antimicrobial and immunomodulatory properties. This study explored the effects of dietary MCFAs on growth performance, intestinal immune function, and microbiota composition in broilers under E. coli O78 challenge. A total of 312 one-day-old male Arbor Acres broilers were randomly assigned to three groups (8 replicates × 13 birds): negative control (NC), E. coli O78-challenged (EC), and E. coli O78-challenged supplemented with 700 mg/kg MCFAs (EM). EC and EM birds were orally challenged with 1 mL of E. coli O78 (2 × 10⁸ CFU/mL) on days 7 and 11; NC birds received 1 mL phosphate-buffered saline (PBS). Compared to the EC, EM broilers had significantly higher body weight (BW) on day 11 and lower feed conversion ratio (FCR) during days 11-18 (P < 0.05). EM birds exhibited a significantly lower spleen index (better immune response) on day 14, but a significantly higher spleen index (immune recovery) on day 21 than the EC group (P < 0.05). In addition, the EM group showed a significantly higher duodenal index (morphological protection) than the other groups on day 21 (P < 0.05). Additionally, EM birds showed significantly increased serum lysozyme (LZM) levels on day 14, elevated endotoxin (ET), IgA, IgM, and reduced diamine oxidase (DAO) levels on day 18 (P < 0.05). IgG levels in the EM group were also higher than those in the EC group on day 21 (P < 0.05). Furthermore, broilers in the EM group exhibited increased jejunal villus height (VH) on day 18 (P < 0.05). The EM group also showed significantly reduced Toll-like receptor 4 (TLR4) expression on day 14 and Interleukin-1 beta (IL-1β) and Interleukin-6 (IL-6) levels on day 18 compared to the EC group (P < 0.05). While no significant changes in ileal microbial diversity were observed in the EM group, the abundances of Lactobacillus spp. and Candidatus_Arthromitus were significantly increased on day 21 (P < 0.05). In conclusion, 700 mg/kg MCFAs supplementation may alleviate the negative effects of E. coli O78 infection in broilers by improving growth, immunity, intestinal integrity, and beneficial gut microbes.

Introduction

Avian colibacillosis, caused by Avian Pathogenic Escherichia coli (APEC), poses a significant challenge to the poultry industry. Economically, Escherichia coli (E. coli) infections result in stunted growth, increased mortality, higher carcass rejection rates, and greater veterinary expenses (Yehia et al., 2023). Clinically, these infections manifest both systemic diseases—such as airsacculitis, perihepatitis, and colisepticemia—and localized conditions including cellulitis, salpingitis, and peritonitis (Swelum et al., 2021; Kobayashi et al., 2011; Guabiraba and Schouler, 2015). Additionally, APEC induces intestinal inflammation that disrupts the intestinal barrier and exacerbates negative effects on poultry health (Ewers et al., 2009).

The intestinal barrier consists of chemical secretions, tight junctions, the mucosal immune system, and the resident microbiota (Yegani and Korver, 2008). Disruption of the microbiota balance can lead to excessive proliferation of E. coli, increased toxin production, and a consequent compromise of barrier integrity (Gomes et al., 2016). This breakdown increases the penetration of pathogens, which in turn triggers a mucosal immune response. During this process, macrophages and dendritic cells release pro-inflammatory cytokines that induce vasodilation and enhance vascular permeability, thereby allowing immune cells and plasma proteins to target and eliminate invading pathogens (Perez-Lopez et al., 2016). Although this inflammatory response is essential for pathogen clearance, it may also contribute to tissue damage and other pathological features of APEC infections (Peng et al., 2019). Moreover, the heightened metabolic demands of the immune response, along with intestinal tissue damage, impair nutrient absorption and ultimately hinder growth and development in poultry (Marchingo and Cantrell, 2022). Therefore, effective regulation of the intestinal immune response is crucial for maintaining optimal poultry production.

A central regulator of intestinal inflammation is the TLR4–NF-κB signaling axis. Toll-like receptor 4 (TLR4) is a key pattern recognition receptor of the innate immune system that primarily recognizes lipopolysaccharides (LPS) from bacterial cell walls (Akira et al., 2006; Gay et al., 2014). Upon LPS binding, TLR4 initiates intracellular signaling cascades that activate nuclear factor-κB (NF-κB) (Kerr et al., 2022). Once activated, NF-κB translocates to the nucleus, where it promotes the transcription of pro-inflammatory genes encoding cytokines such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β) (Liu et al., 2017). These mediators attract immune cells to the infection. As the infection resolves, this pathway also supports the production of anti-inflammatory cytokines like interleukin-10 (IL-10), which help to mitigate inflammation, prevent excessive tissue damage, and facilitate tissue repair (Saraiva and O'Garra, 2010). This dynamic modulation of immune responses is critical to preserving gut homeostasis during infection.

Medium-chain fatty acids (MCFAs), originating from coconut oil, palm kernel oil, and dairy products (Babayan et al., 1987; Lindmark, 2008), have been shown to regulate the TLR4-NF-κB signaling pathways (Martinez-Vallespin et al., 2016; Shi et al., 2006). Structurally, MCFAs are classified by chain length into caproic acid (C6), caprylic acid (C8), capric acid (C10), and lauric acid (C12) (Burdge and Calder, 2015). Among these, capric acid has been shown to inhibit pathogens such as Helicobacter pylori, Staphylococcus aureus, Campylobacter jejuni, and Streptococcus spp. (Bergsson et al., 1998, 2002). Similarly, lauric acid exhibits broad-spectrum antimicrobial activity against both Gram-positive and Gram-negative bacteria, including Staphylococcus aureus, Streptococcus mutans, Streptococcus pyogenes, Salmonella spp., Helicobacter pylori, Clostridium difficile, and E. coli (Messens et al., 2010). Moreover, combinations of MCFAs have demonstrated inhibitory effects on Campylobacter jejuni (Greene et al., 2022), Salmonella spp., and E. coli in vitro (Martinez-Vallespin et al., 2016) and have been effective in vivo against Salmonella enteritidis in broilers (Hermans et al., 2024). Additionally, MCFAs promote the growth of beneficial gut bacteria, including Lactobacillus spp., and may attenuate inflammation by modulating immune pathways (Zentek et al., 2012). Studies in weaned piglets have also demonstrated that medium-chain triglycerides (MCTs) can improve intestinal barrier integrity under LPS challenge by inhibiting TLR4, nucleotide-binding oligomerization domain (NOD) proteins, and necroptosis signaling (Xu et al., 2018). Moreover, butyric acid and MCTs have been shown to inhibit the TLR4-NF-κB signaling pathway in the mice intestine and markedly ameliorate colitis (Kono et al., 2010; Zhang et al., 2019). However, whether MCFAs modulate TLR4-NF-κB signaling and influence the composition of microbiota during APEC infection remains unclear.

Here, we hypothesize that dietary MCFAs mitigate intestinal inflammation in APEC-infected broilers by modulating TLR4-NF-κB signaling and restoring microbial balance. To test this hypothesis, we evaluate the effects of MCFAs on broiler growth performance, intestinal histomorphology, immune gene expression, and microbiota composition in response to E. coli O78 infection. This study aims to provide empirical evidence supporting the use of MCFAs as a novel strategy for controlling bacterial infections in poultry production.

Materials and methods

Ethics statement

All experimental procedures were approved by the Animal Care and Use Committee of the Feed Research Institute, Chinese Academy of Agricultural Sciences (CAAS) under protocol number ACE-CAAS-20210520. All methods were conducted in strict accordance with the established guidelines for animal welfare and experimentation in China.

Animal management

A total of 312 newly hatched male Arbor Acres (AA) broiler chicks were obtained from Beijing Zhengda Broiler Company (Beijing, China). The chicks were housed in a three-tier cage system (120 cm × 90 cm × 60 cm) with controlled temperature and ventilation. The ambient temperature was initially set at 34 °C and reduced by 1 °C every two days until reaching 24 °C. Relative humidity was maintained at 65% for the first three days and then adjusted to 55%. The lighting regimen consisted of 23 h of light and 1 h of darkness for the first seven days, followed by 20 h of light and 4 h for the remainder of the research.

The 42-day feeding period was divided into three phases: pre-infection (days 1–7), infection (days 8–21), and post-infection (days 22–42). Broiler followed the AA broiler management guide. Chicks had ad libitum access to feed and water. The basal diet was formulated in accordance with the National Research Council (NRC, 1994) guidelines for broilers and was adjusted to meet the specific nutrient requirements of AA broilers. The feed was divided into two formulations: days 1–21 and days 22–42. The same diet was provided during the pre-infection and infection phases; details of the composition and nutrient levels are presented in Table 1.

Table 1.

The composition and nutrient content of the basal diet (as-fed basis).

Items	1-21 d	22-42 d
Ingredient, %
Corn	56.59	59.96
Soybean meal (46 CP)	25.95	20.00
Corn gluten meal (60 CP)	4.00	5.00
Cottonseed meal	4.50	4.42
Low-grade flour	2.00	2.00
Soybean oil	2.49	4.50
Dicalcium phosphate	1.83	1.59
Limestone	1.35	1.27
Salt	0.35	0.35
DL-Methionine (DL-Met)	0.35	0.35
L-Lysine HCl (L-Lys HCl)	0.23	0.21
L-Threonine (L-Thr)	0.05	0.04
Vitamin Premix1	0.02	0.02
Mineral Premix1	0.20	0.20
Choline chloride (50%)	0.10	0.10
Sodium bicarbonate	0.15	0.20
Total	100.00	100.00
Nutrient level2
AME, MJ × kg-1	12.46	13.22
Crude protein (CP)	21.95	19.95
Calcium (Ca)	1.00	0.90
Available phosphorus (P)	0.45	0.4
Lysine (Lys)	1.30	1.15
Methionine (Met)	0.58	0.54
Methionine+cystine (Met+Cys)	0.94	0.87
Threonine (Thr)	0.84	0.75
Tryptophan (Try)	0.23	0.20

¹Vitamin premix provides the following per kg of diet: Vitamin A, 9000 IU; Vitamin D3, 2500 IU; Vitamin E, 15 IU; Vitamin K3, 2.65 mg; Thiamine, 1.20 mg; Riboflavin, 5.80 mg; Niacin, 66.0 mg; Pantothenic acid, 10.0 mg; Pyridoxine, 2.60 mg; Biotin, 0.200 mg; Folic acid, 0.700 mg; Vitamin B12, 0.012 mg. Mineral premix provides the following per kg of diet: Mn, 100 mg; Zn, 75.0 mg; Fe, 80.0 mg; I, 0.650 mg; Cu, 8.00 mg; Se, 0.350 mg.

²Nutrient levels in the diet were calculated based on the ingredient composition and standard nutritional values.

Experimental design

The experiment was conducted using a completely randomized design (CRD). A total of 312 one-day-old healthy AA chicks were randomly assigned to three groups, including a negative control group (NC) without the E. coli O78 challenge, an E. coli O78-challenged group (EC), and an E. coli O78-challenged group supplemented with 700 mg/kg MCFAs (EM). The supplementation level was determined based on unpublished preliminary data. Each group comprised 8 replicates with 13 chicks per replicate. On days 7 and 11, the chicks in EC and EM treatments were orally administered 1 mL E. coli O78 solution (2 × 10⁸ CFU/mL, based on the half-lethal dose determined from the preliminary experiment), while chicks in the NC treatment received 1 mL of phosphate-buffered saline (PBS). The MCFAs used in this study were supplied by Vitamex Shanghai Ltd. and consisted of a synergistic mixture of free caproic acid (C6), caprylic acid (C8), capric acid (C10), and lauric acid (C12). The product (M-prove®, Royal Agrifirm Group, The Netherlands) is commercially standardized. The pathogenic avian E. coli O78 strain (CVCC1555) was obtained from the General Microbiological Culture Collection Center of China. The strain was cultured in liquid medium (LM) at 37 °C with shaking for 24 h, then harvested by centrifugation, washed three times with PBS, and diluted to the required concentration.

Sample collection and indicator testing

Growth performance. At day 1, the average initial body weight (BW) of each replicate was recorded. Subsequently, on days 7, 11, 14, 18, 21, and 42, BW and feed refusals were measured for each replicate. Based on these data, growth performance parameters, including average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), and mortality rate, were calculated.

Blood immune molecules. On days 14, 18, and 21, one bird with BW closer to the average weight of the replicate was selected from each replicate (n = 8). Following 12 h of fasting, blood samples were collected from the jugular vein into sterile tubes without anticoagulant. To allow complete coagulation and clot retraction, the samples were incubated at 37 °C for 2 h, and then centrifuged at 3000 rpm for 10 min to obtain serum. Serum concentrations of diamine oxidase (DAO), lysozyme (LZM), and endotoxin (ET) were measured using ELISA kits (Shanghai Meilian). Additionally, the concentrations of immunoglobulins A (IgA), G (IgG), and M (IgM) were determined.

Immune organ index and intestinal index. On days 14, 18, and 21, chick was slaughtered after blood collection (n = 8). The thymus, spleen, bursa of Fabricius, and gut samples were collected, excess fat was removed, and the organs were gently dried with filter paper before weighing. The lengths of the duodenum, jejunum, and ileum were also measured. The immune organ index (IOI) and intestinal index were calculated.

Jejunal morphology. On days 14 and 18, following the measurement of intestinal lengths, a 2 cm section from the proximal 1/4 of the jejunum was excised, rinsed with physiological saline, and fixed into 4% paraformaldehyde at room temperature. These samples were processed into histological paraffin sections following standard procedures.

Paraffin-embedded jejunal sections were stained with Hematoxylin and Eosin (HE). For each sample, three intact and representative villus-crypt units were selected. Villus height (VH), from the tip to the base of the villus, and crypt depth (CD), from the crypt opening to the base of the intestinal gland, were measured using ImagePro-Plus 7.0 software (Media Cybernetics, USA). The villus height-to-crypt depth ratio (VCR) was calculated.

Jejunal mucosa immune molecule mRNA expression. Jejunal mucosa samples were collected on days 14 and 18 from one bird per replicate (n = 8). A 2–3 cm segment of the mid-jejunum was precisely dissected, opened along the longitudinal axis, and thoroughly rinsed with ice-cold PBS to eliminate any remaining luminal contents. The mucosal layer was subsequently collected by gentle scraping with a sterile glass slide to obtain epithelial cells and adherent mucus. The harvested mucosal samples were immediately snap-frozen in liquid nitrogen and stored at –80 °C for subsequent molecular analyses. Complementary DNA (cDNA) was synthesized using the EasyPure Bacteria Genomic DNA Kit (EE161-11, Beijing Transgene Biological Technology Co., Ltd., Beijing). Quantitative real-time PCR (qRT-PCR) was then performed using the EasyPure RNA Kit (Beijing Transgene Biological Technology Co., Ltd.) on a CFX96 Touch RT-PCR detection system.

The mRNA expression levels of key genes related to intestinal immune function, including Toll-Like Receptor 4 (TLR4), Tumor Necrosis Factor-α (TNF-α), Nuclear Factor-kappa B (NF-κB), Interleukin-1β (IL-1β), Interleukin-6 (IL-6), and Interleukin-10 (IL-10), were assessed (Table 2). The 2^-ΔΔCt method was used to calculate the relative mRNA expression levels, with avian β-actin as the reference gene.

Table 2.

Primer sequences of target and reference genes.

Gene name	Forward primer (5′to 3′)	Reverse primer (5′to 3′)	Annealing temperature (°C)	GenBank number
TLR4	AGTCTGAAATTGCTGAGCTCAAAT	GCGACGTTAAGCCATGGAAG	59.6	NM_001030693.2
NF-κB	GAAGGAATCGTACCGGGAACA	CTCAGAGGGCCTTGTGACAGTAA	61.4	NM_205134.1
TNF-α	AGATGGGAAGGGAATGAACC	TCAGAGCATCAACGCAAAAG	57.7	NM_204267
IL-1β	GGTCAACATCGCCACCTACA	CATACGAGATGGAAACCAGCAA	58.8	NM_204524
IL-6	TCTGTTCGCCTTTCAGACCTA	GACCACCTCATCGGGATTTAT	57.7	NM_204628
IL-10	GCTGAGGGTGAAGTTTGAGGAA	GAAGCGCAGCATCTCTGACA	60.9	NM_001004414.2
β-actin	ATCCGGACCCTCCATTGTC	AGCCATGCCAATCTCGTCTT	59.0	L08165

Gut microbiota 16S rRNA sequencing. On day 21, terminal ileal contents were aseptically collected from one chick per replicate, immediately frozen in liquid nitrogen, and stored at −80 °C until processing. Total genomic DNA was extracted from the samples using a CTAB-based protocol. The quality and concentration of the extracted DNA were assessed by 1% agarose gel electrophoresis and spectrophotometric analysis, and the DNA was diluted to a final concentration of 1 ng/µL with nuclease-free water.

The V3-V4 hypervariable regions of the 16S rRNA gene were amplified using the universal primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′). Polymerase chain reaction (PCR) was conducted in a 15 µL reaction volume, which included Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 2 µM of both forward and reverse primers, and 10 ng of template DNA. The thermal cycling conditions were as follows: an initial denaturation at 98 °C for 1 minute; 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and extension at 72 °C for 30 s; final extension at 72 °C for 5 min. The PCR products were combined with 1X TAE buffer and subjected to electrophoresis on a 2% agarose gel for visualization. PCR products were pooled, and the resulting mixture was purified using the Universal DNA Purification Kit (TianGen, China).

Sequencing libraries were constructed utilizing the NEB Next® Ultra DNA Library Prep Kit (Illumina, USA), incorporating index codes. The quality of the libraries was evaluated using the Agilent 5400 (Agilent Technologies Co. Ltd., USA). Ultimately, the libraries were sequenced on the Illumina platform (Illumina PE 250), yielding 250 bp paired end reads.

After demultiplexing, the raw FASTQ files were imported into QIIME2 for further analysis. Sequences were quality-filtered using Fastp (v0.19.6) (Chen et al., 2018) and merged with FLASH (v1.2.11) (Magoc and Salzberg, 2011). Then raw FASTQ data files were imported into Mothur. Operational taxonomic units (OTUs) were clustered at 97% similarity using an open-reference approach, and chimeric sequences were removed with the UCHIME algorithm implemented in QIIME2 (Edgar et al., 2011). Taxonomy classification of OTUs was conducted using the feature-classifier plugin, aligning sequences against the pre-trained GREENGENES 13_8 database (trimmed to the V3-V4 region targeted by the 341F/806R primer pair) (Bokulich et al., 2018). Mitochondrial and chloroplast sequences were filtered out using the feature-table plugin.

Statistical analyses were conducted as follows: mcrobiota bioinformatics analysis was conducted using R (v3.3.1) and Image GP software (http://www.ehbio.com/ImageGP/index.php/Home/Index/PCAplot.html). Based on OTUs, rarefaction curves and alpha diversity indices, including observed OTUs, Faith's phylogenetic diversity (Faith_PD), and the Shannon index, were calculated using Mothur v1.30.1 (Schloss et al., 2009). Differential abundance of taxa among groups was evaluated using Analysis of Variance (ANOVA), Kruskal–Wallis, Linear discriminant analysis effect size (LEfSe) (with a Linear Discriminant Analysis (LDA) score > 4 and P < 0.05), and DESeq2 (Love et al., 2014; Mandal et al., 2015; Segata et al., 2011). Alpha diversity metrics, including observed OTUs, Chao1, and the Shannon index, were calculated using the core-diversity plugin in QIIME2 to assess within-sample diversity. Beta diversity was evaluated using Bray-Curtis dissimilarity and visualized via principal coordinate analysis (PCoA). Additionally, Partial Least Squares Discriminant Analysis (PLS-DA) was performed using the mixOmics package (v3.4.4) in R (Rohart et al., 2017) to investigate group separation. The numbers of shared and unique OTUs among groups were determined using Python (v2.7.10) and visualized with Venn diagrams in R (v3.3.1). Species composition at the phylum, family, and genus levels was illustrated with bar charts generated in R (v3.3.1). The significance of microbial community differences among groups was assessed using ANOSIM from the vegan package in R (v3.3.1). Co-occurrence network analysis was performed by calculating Spearman’s rank correlations between predominant taxa, and the resulting network was visualized to display taxonomic associations. Finally, PICRUSt was used to predict the functional profiles of the microbial communities based on Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologs (Langille et al., 2013).

Data processing and analysis

Statistical analysis was performed using one-way ANOVA within a completely randomized block design, employing the MIXED procedure of the SAS statistical software (version 9.4; SAS Institute Inc., Cary, NC, USA). For growth performance, each replicate served as the experimental unit, for other parameters, the experimental unit was defined as the individual chick. Microbiota data processing and statistical analysis were performed as detailed in the “Gut Microbiota 16S rRNA Sequencing” section. Statistical significance was set at P < 0.05; values between 0.05 and 0.10 (0.05 ≤ P < 0.10) were interpreted as trends. Results are presented as means ± pooled standard error of the mean (SEM).

Results

Growth performance

Table 3 summarizes the effects of dietary MCFAs supplementation on the growth performance of broilers challenged with E. coli O78. During the pre-challenge period (days 1-7), there were no significant differences among groups in BW, ADG, ADFI, and FCR (P > 0.05).

Table 3.

Effect of dietary supplementation of MCFAs on growth performance in broilers.

Item	NC	EC	EM	SEM	P value
1∼7 d
BW at 7 d (g)	174.2 ± 6.5	165.4 ± 10.1	168.1 ± 7.2	1.843	0.136
ADG (g)	18.3 ± 0.8	17.3 ± 1.4	18.2 ± 0.9	0.231	0.174
ADFI (g)	20.5 ± 1.4	20.6 ± 1.1	21.3 ± 1.2	0.256	0.450
FCR	1.123 ± 0.061	1.170 ± 0.059	1.169 ± 0.040	0.012	0.165
Mortality (%)	0	0	0	0	0
8∼11 d
BW at 11 d (g)	312.7 ± 5.1a	290.9 ± 12.9b	304.9 ± 10.0a	2.982	0.003
ADG (g)	35.1 ± 1.8	33.0 ± 3.5	33.5 ± 3.2	0.621	0.364
ADFI (g)	42.3 ± 3.3	41.2 ± 3.5	40.6 ± 2.6	0.660	0.586
FCR	1.183 ± 0.099	1.250 ± 0.043	1.237 ± 0.086	0.018	0.280
Mortality (%)	0.961 ± 2.719	2.884 ± 5.722	0.961 ± 2.719	1.411	0.648
1 ∼ 14 d
BW at 14d (g)	381.9 ± 12.1	364.3 ± 22.1	383.8 ± 24.5	8.336	0.151
ADG (g)	23.9 ± 0.9	22.2 ± 1.8	22.9 ± 1.3	0.426	0.075
ADFI (g)	31.3 ± 1.7	30.1 ± 2.0	29.6 ± 1.5	0.248	0.220
FCR	1.309 ± 0.046	1.356 ± 0.042	1.305 ± 0.054	0.011	0.109
8 ∼ 18 d
BW at 18d (g)	577.1 ± 26.4a	528.7 ± 38.6b	555.7 ± 36.1ab	7.835	0.032
ADG (g)	34.5 ± 2.3a	29.8 ± 3.2b	31.5 ± 2.4b	0.675	0.007
ADFI (g)	49.8 ± 1.3a	45.7 ± 4.7b	44.4 ± 3.0b	0.907	0.047
FCR	1.381 ± 0.042b	1.484 ± 0.094a	1.413 ± 0.028b	0.016	0.016
Mortality (%)	0.961 ± 2.719	6.729 ± 6.417	5.768 ± 6.816	1.996	0.103
8 ∼ 21 d
BW at 21 d (g)	775.5 ± 31.6	748.4 ± 59.8	762.2 ± 38.7	9.316	0.503
ADG (g)	38.7 ± 2.1a	34.5 ± 2.2b	35.4 ± 2.4b	0.619	0.005
ADFI (g)	56.4 ± 3.0a	51.8 ± 3.8b	51.7 ± 3.8b	0.884	0.029
FCR	1.456 ± 0.045	1.498 ± 0.062	1.465 ± 0.038	0.010	0.233
Mortality (%)	0.961 ± 2.719b	8.653 ± 7.624a	6.729 ± 6.417a	2.065	0.026
1 ∼ 21 d
ADG (g)	33.6 ± 1.3a	30.3 ± 2.0b	31.0 ± 2.0b	0.494	0.005
ADFI (g)	46.8 ± 2.2a	43.4 ± 3.1b	41.6 ± 2.9b	0.746	0.006
FCR	1.391 ± 0.033	1.434 ± 0.053	1.400 ± 0.027	0.009	0.090
Mortality (%)	0.961 ± 2.719b	8.653 ± 7.624a	6.729 ± 6.417a	2.065	0.026
22 ∼ 42 d
ADG (g)	92.3 ± 8.6	93.1 ± 10.5	95.0 ± 7.2	1.747	0.824
ADFI (g)	161.7 ± 7.7	171.3 ± 5.9	168.6 ± 8.6	1.923	0.109
FCR	1.760 ± 0.075	1.777 ± 0.083	1.764 ± 0.108	0.018	0.927
Mortality (%)	0	1.923 ± 3.560	0	0.928	0.122
1 ∼ 42 d
BW at 42 d (g)	2869.4 ± 151.4	2876.8 ± 150.0	2885.9 ± 111.8	29.270	0.977
ADG (g)	58.4 ± 2.9	57.0 ± 2.0	58.8 ± 4.5	0.777	0.624
ADFI (g)	97.3 ± 5.3	91.7 ± 2.5	91.6 ± 7.3	1.287	0.108
FCR	1.596 ± 0.040	1.607 ± 0.059	1.589 ± 0.060	0.011	0.804
Mortality (%)	0.961 ± 2.719b	10.575 ± 9.137a	6.729 ± 6.417a	2.131	0.007

Data are presented as the mean ± SD; SEM is provided separately (n = 8).

NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; SEM, standard error of the mean; d, day; g, gram; BW, body weight; ADG, average daily gain; ADFI, average daily feed intake; FCR, feed conversion ratio. The p-values are obtained by comparing the NC group, EC group, and EM group within a specific characteristic.

^abMeans with different superscript within diet treatment differ significantly (P < 0.05).

Following the challenge, the EM group exhibited a significantly higher BW than the EC by day 11 (P < 0.05). On day 18, the BW in the EC group was significantly lower than that in the NC (P < 0.05), while no significant difference was detected between the EM and NC groups (P > 0.05).

Between days 8-18, 8-21, and 1-21, both EC and EM groups showed significantly lower ADG and ADFI compared to the NC group (P < 0.05). In addition, the FCR from days 8 to 18 was significantly higher in the EC group relative to the NC group (P < 0.05), whereas the FCR in the EM group did not differ significantly from the NC group (P > 0.05). By day 21, differences in BW and FCR (days 8-21) were not statistically significant among the three groups (P > 0.05).

Moreover, mortality rates were significantly elevated in both the EC and EM groups than in the NC group during days 8-18, 8-21, and 1-42 (P < 0.05). During the later growth phase (days 22-42 and days 1-42), no significant differences were observed among the groups for the remaining performance parameters (P > 0.05).

Blood immune molecules

Table 4 presents the levels of blood immune molecules measured during the experiment. On day 14, the EM group exhibited significantly higher levels of IgG and LZM compared to the NC group (P < 0.05). In contrast, DAO levels in the EC group were significantly lower than those in the NC group, with no significant difference observed between the NC and EM groups (P > 0.05).

Table 4.

Effect of dietary supplementation of MCFAs on blood immune in broilers.

Item	NC	EC	EM	SEM	P value
14 d
IgA (g/L)	2.235 ± 0.059	2.238 ± 0.017	2.234 ± 0.042	0.009	0.985
IgG (g/L)	4.170 ± 0.038b	4.208 ± 0.081ab	4.265 ± 0.051a	0.015	0.027
IgM (g/L)	1.601 ± 0.044	1.659 ± 0.039	1.657 ± 0.072	0.012	0.115
LZM (μg/mL)	1.255 ± 0.162c	1.431 ± 0.206b	1.683 ± 0.024a	0.048	< 0.001
DAO (U/L)	9.173 ± 1.063a	7.861 ± 0.766b	8.650 ± 0.545ab	0.195	0.015
ET (EU/mL)	16.712 ± 1.085	17.976 ± 0.852	17.569 ± 1.094	0.244	0.104
18 d
IgA (g/L)	2.193 ± 0.057b	2.212 ± 0.064b	2.289 ± 0.049a	0.014	0.007
IgG (g/L)	4.080 ± 0.079b	4.165 ± 0.070a	4.223 ± 0.081a	0.019	0.005
IgM (g/L)	1.567 ± 0.043b	1.595 ± 0.092b	1.708 ± 0.053a	0.019	0.001
LZM (μg/mL)	1.226 ± 0.136b	1.866 ± 0.073a	1.848 ± 0.082a	0.065	< 0.001
DAO (U/L)	8.574 ± 0.694a	8.463 ± 0.257a	7.749 ± 0.825b	0.152	0.043
ET (EU/mL)	16.463 ± 0.974b	16.502 ± 1.266b	18.557 ± 0.581a	0.305	0.001
21 d
IgA (g/L)	2.266 ± 0.123	2.290 ± 0.071	2.329 ± 0.088	0.020	0.434
IgG (g/L)	4.197 ± 0.069a	4.131 ± 0.035b	4.200 ± 0.067a	0.013	0.050
IgM (g/L)	1.623 ± 0.056	1.648 ± 0.062	1.631 ± 0.049	0.011	0.667
LZM (μg/mL)	1.326 ± 0.250b	1.589 ± 0.237a	1.616 ± 0.076a	0.050	0.023
DAO (U/L)	8.641 ± 0.964a	7.650 ± 1.056b	7.374 ± 0.598b	0.208	0.025
ET (EU/mL)	16.516 ± 2.482a	14.020 ± 2.690b	12.769 ± 0.742b	0.549	0.011

Data are presented as the mean ± SD; SEM is provided separately (n = 8).

NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; SEM, standard error of the mean; d, day; g, gram; L, liter; EU, Endotoxin Units; mL, milliliter; μg, microgram; IgA, Immunoglobulin A; IgG, Immunoglobulin G; IgM, Immunoglobulin M; LZM, Lysozyme; DAO, Diamine Oxidase; ET, Endotoxin; The p-values are obtained by comparing the NC group, EC group, and EM group within a specific characteristic.

^ab Means with different superscript within diet treatment differ significantly (P < 0.05).

On day 18, the EM group had significantly higher levels of IgA, IgM, and ET compared to both the EC and NC groups (P < 0.05). Additionally, both the EC and EM groups showed significantly elevated IgG and LZM levels relative to the NC group (P < 0.05). Notably, DAO levels in the EM group were significantly lower than those in both the EC and NC groups (P < 0.05).

By day 21, IgA and IgM levels did not differ significantly among treatments (P > 0.05). However, both EC and EM groups exhibited significantly lower DAO and ET levels compared to the NC group (P < 0.05), while LZM levels remained significantly higher in the EC and EM groups than in the NC group (P < 0.05). Furthermore, the EC group showed a significantly lower IgG level compared to both the NC and EM groups (P < 0.05).

Organ index

Table 5 details the effects of MCFA supplementation on immunological organ and intestinal development indices. On day 14, the spleen index was significantly higher in the EC group than in the other groups (P < 0.05). By day 21, however, the spleen index was significantly higher in the EM group compared with the other groups (P < 0.05). On day 18, the ileum index in the EC group was significantly higher than the NC group (P < 0.05), with no significant difference between the NC and EM groups (P > 0.05). On day 21, the duodenum index was significantly higher in the EM group than in the other treatments (P < 0.05). Similarly, the jejunum index in the EM group was significantly higher than in the NC group (P < 0.05), while no significant difference was observed between the EC and NC groups.

Table 5.

Effect of dietary supplementation of MCFAs on organ index in broilers.

Item	NC	EC	EM	SEM	P value
14 d
Thymus index	0.199±0.038	0.187±0.033	0.169±0.037	0.008	0.285
Spleen index	0.070 ± 0.013b	0.105 ± 0.015a	0.081 ± 0.022b	0.005	0.002
Bursa of Fabricius index	0.210 ± 0.042	0.198 ± 0.034	0.222 ± 0.033	0.008	0.486
Duodenum index	5.226 ± 0.274	5.501 ± 0.316	5.530 ± 0.310	0.069	0.142
Jejunum index	12.613 ± 0.861	13.185 ± 1.193	13.035 ± 1.447	0.242	0.629
Ileum index	11.024 ± 0.916	11.796 ± 0.876	11.588 ± 1.113	0.211	0.333
18 d
Thymus index	0.228 ± 0.053	0.263 ± 0.047	0.236 ± 0.016	0.009	0.249
Spleen index	0.078 ± 0.014	0.084 ± 0.010	0.091 ± 0.017	0.003	0.215
Bursa of Fabricius index	0.206 ± 0.046	0.178 ± 0.039	0.230 ± 0.034	0.009	0.063
Duodenum index	4.137 ± 0.260	4.151 ± 0.470	4.064 ± 0.435	0.082	0.902
Jejunum index	9.642 ± 0.685	10.801 ± 1.192	9.967 ± 1.325	0.241	0.117
Ileum index	8.865 ± 0.762b	10.160 ± 1.068a	9.570 ± 0.532ab	0.209	0.021
21 d
Thymus index	0.205 ± 0.057a	0.190 ± 0.043ab	0.166 ± 0.058b	0.011	0.383
Spleen index	0.075 ± 0.010b	0.079 ± 0.020b	0.105 ± 0.020a	0.009	0.011
Bursa of Fabricius index	0.188 ± 0.037	0.164 ± 0.068	0.197 ± 0.045	0.011	0.466
Duodenum index	2.887 ± 0.193b	2.928 ± 0.300b	3.278 ± 0.273a	0.069	0.027
Jejunum index	7.552 ± 0.558b	7.955 ± 0.583ab	8.533 ± 0.679a	0.148	0.024
Ileum index	7.172 ± 0.615	7.441 ± 0.642	7.312 ± 0.750	0.138	0.743

Data are presented as the mean ± SD; SEM is provided separately (n = 8).

NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; SEM, standard error of the mean; d, day; g, gram; cm, centimeter; kg, kilogram; The p-values are obtained by comparing the NC group, EC group, and EM group within a specific characteristic.

^ab Means with different superscript within diet treatment differ significantly (P < 0.05).

Jejunal morphology

Table 6 indicates that most jejunal morphological measurements did not differ significantly among the groups. However, on day 18, the VH in the jejunum of the EM group was significantly higher than that in the EC group (P < 0.05).

Table 6.

Effect of dietary supplementation of MCFAs on intestinal morphology in broilers.

Item	NC	EC	EM	SEM	P value
14 d
VH (μm)	1089.137 ± 149.866	979.956 ± 89.956	994.945 ± 104.406	25.044	0.157
CD (μm)	184.959 ± 20.488	194.249 ± 26.638	177.071 ± 10.115	4.465	0.295
VCR	6.077 ± 0.924	5.141 ± 0.928	5.881 ± 0.871	0.204	0.140
18 d
VH (μm)	1132.780 ± 89.589a	1001.736 ± 110.071b	1134.069 ± 68.935a	24.402	0.032
CD (μm)	186.038 ± 20.310	198.748 ± 24.925	197.321 ± 12.539	4.229	0.461
VCR	5.530 ± 0.508	5.457 ± 0.826	5.948 ± 0.601	0.150	0.359

Data are presented as the mean ± SD; SEM is provided separately (n = 8).

NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; SEM, standard error of the mean; d, day; μm, micrometer; VH, villus height; CD, crypt depth; VCR, villus height-to-crypt depth ratio; The p-values are obtained by comparing the NC group, EC group, and EM group within a specific characteristic.

^ab Means with different superscript within diet treatment differ significantly (P < 0.05).

Jejunal mucosa immune molecule mRNA expression

Table 7 summarizes the mRNA expression levels of intestinal mucosal immune factors (TLR4, NF-κB, TNF-α, IL-1β, IL-6, and IL-10) in response to the E. coli O78 challenge.

Table 7.

Effect of dietary supplementation of MCFAs on gene expression of immunological factors in broiler jejunal mucosa.

Item	NC	EC	EM	SEM	P value
14 d
TLR4	1.035 ± 0.311c	7.580 ± 1.316a	4.639 ± 0.869b	0.761	< 0.001
NF-κB	1.004 ± 0.106b	4.775 ± 1.531a	4.609 ± 1.600a	0.579	0.001
TNF-α	1.052 ± 0.374b	2.334 ± 0.465a	2.158 ± 0.571a	0.202	0.006
IL-1β	1.001 ± 0.039b	2.687 ± 0.131a	2.762 ± 1.250a	0.310	0.012
IL-6	1.051 ± 0.404b	3.947 ± 1.065a	3.712 ± 1.055a	0.439	0.002
IL-10	1.014 ± 0.189b	2.629 ± 1.240a	2.882 ± 0.560a	0.291	0.006
18 d
TLR4	1.096 ± 0.227	1.247 ± 0.301	1.364 ± 0.382	0.080	0.438
NF-κB	1.012 ± 0.176b	1.810 ± 0.435a	1.795 ± 0.174a	0.135	0.002
TNF-α	1.032 ± 0.284	1.737 ± 0.552	1.587 ± 0.133	0.132	0.054
IL-1β	1.013 ± 0.194c	2.499 ± 0.495a	1.636 ± 0.444b	0.189	< 0.001
IL-6	1.001 ± 0.058b	2.315 ± 0.849a	1.309 ± 0.318b	0.195	0.005
IL-10	1.019 ± 0.228b	1.480 ± 0.346a	1.828 ± 0.313a	0.111	0.007

Data are presented as the mean ± SD; SEM is provided separately (n = 8).

NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; SEM, standard error of the mean; d, day; TLR4, Toll-Like Receptor 4; NF-κB, Nuclear Factor-kappa B; TNF-α, Tumor Necrosis Factor-alpha; IL-1β, Interleukin-1 beta; IL-6, Interleukin-6; IL-10, Interleukin-10; The p-values are obtained by comparing the NC group, EC group, and EM group within a specific characteristic.

^ab Means with different superscript within diet treatment differ significantly (P < 0.05).

On day 14, all measured immune factors were significantly upregulated in the infected groups (EC and EM) compared to the NC group (P < 0.05). Notably, TLR4 expression in the EM group was significantly lower than that in the EC group (P < 0.05). On day 18, both the EC and EM groups showed significantly higher expression of NF-κB and IL-10 compared to the NC group (P < 0.05). The EC group showed significantly increased IL-6 expression compared with both NC and EM (P < 0.05), and IL-1β expression followed the order: NC < EM < EC (P < 0.05). Although TNF-α expression was lower in the EM group compared to the EC group, this difference was not statistically significant.

Gut microbiota 16S rRNA sequencing

The 16S rRNA sequencing results of terminal ileum microbiota are summarized in various figures, with analyses covering α-diversity, β-diversity, species composition, differential abundance, and functional predictions. Rarefaction curves reached a plateau across groups, suggesting sufficient sequencing depth (Fig. 1A–C, and E). There were no significant differences among groups in α-diversity indices (Chao1, observed OTUs, and Shannon index) (P > 0.05) (Fig. 1F, G). Notably, a Venn diagram (Fig. 1D) indicates the highest number of unique species in the EC group.

Fig. 1.

Effect of dietary MCFAs supplementation on microbial α-diversity and β-diversity in broilers. A, B, C and E, Microbiota sequencing rarefaction curve. A and E, Shannon index; B, observed_OTUs index; C, faith_PD index. D, Venn diagram; F, Chao1 index and observed_OTUs index; G, shannon index; H, PCoA; I, PLS-DA; NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; Significance levels: ^–P > 0.1; * 0.5 < P < 0.1; ^⁎⁎ 0.01 < P < 0.05; ^⁎⁎⁎P < 0.01.

For β-diversity, PCoA using Bray-Curtis distances showed clustering along the X-axis, suggesting no significant differences in overall microbial community structure (P > 0.05) (Fig. 1H). In contrast, PLS-DA clustering suggested notable differences in microbial composition, as demonstrated by non-overlapping clusters (Fig. 1I).

In terms of species composition, the dominant phyla at day 21 were Proteobacteria, Bacteroidetes, Firmicutes, and Acidobacteria (Fig. 2A), with relative abundances as follows: Proteobacteria (NC: EC: EM = 55.36%: 57.28%: 41.46%), Bacteroidetes (NC: EC: EM = 21.58%: 22.51%: 18.75%), Firmicutes (NC: EC: EM = 13.66%: 11.15%: 32.94%), and Acidobacteria (NC: EC: EM = 5.58%: 5.31%: 4.08%). A higher relative abundance of Lactobacillaceae (NC: EC: EM = 3.29%: 3.82%: 10.50%) and Ruminococcaceae (NC: EC: EM = 3.00%: 1.98%: 6.67%) was observed in the EM group, particularly for the genera Lactobacillus and Ralstonia (Fig. 2B, C).

Fig. 2.

Microbiota composition at the phylum, family, and genus levels in broilers. A, phylum level; B, family level; C, genus level. NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection.

Fig. 3 shows notable taxonomic differences across groups. Specifically, the EM group had a higher enrichment of Firmicutes and lower levels of OP8 (Fig. 3A). At the genus level, Lactobacillus, Ralstonia, and Candidatus Arthromitus were more enriched in the EM group (Fig. 3B). The Manhattan plot (Fig. 3C) depicts a macro-level pattern of genus-level abundance across the Firmicutes, Bacteroidetes, Proteobacteria, and Acidobacteria phyla within the EM group, indicating an increase in the abundance of genera belonging to the Firmicutes. Furthermore, the volcano plot (Fig. 3D) highlights a substantial enrichment of Candidatus_Arthromitus in the EM group. Additional genera, including Blautia, Clostridium, Dorea, Anaerotruncus, Ruminococcus, and Lactobacillus, also demonstrated enrichment, whereas the Turicibacter, Pleomorphomonas, Ruegeria, Desulfofrigus, and Desulfobacula exhibited a decrease.

Fig. 3.

Comparative analysis of microbiota composition in broilers. A and B, Heatmaps for microbiota abundance at phylum and genus levels, respectively, at day 21, with color intensity indicating relative abundance (warm colors for higher, cool colors for lower). C, Manhattan plot; This Manhattan plot represents the statistical significance of different genera within various phyla. Each point on the plot corresponds to a genus, with its phylum indicated on the x-axis and the y-axis showing the negative logarithm of the p-value (-log10(p-value)). Significant associations are highlighted by a threshold line, which denotes the level of significance. The shape of the points (triangle for enriched, circle for depleted, and square for not significant) and their size represent the mean abundance of the respective genera. D, Volcano Plot; The volcano plot visualizes the relationship between the significance (p-value) and the effect size (fold change or log fold change) of the differences in microbiota abundance. Points above the diagonal line indicate statistically significant differences, with the steepness of the line representing the threshold for significance. NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection.

Fig. 4 details the differences in microbial community composition among the groups. At the phylum level (Fig. 4A), the EM group exhibited a significant decrease in Proteobacteria and a significant increase in Firmicutes (P < 0.05), corresponding to a higher Firmicutes-to-Bacteroidetes (F/B) ratio in the EM group (Fig. 4B). However, this difference was not statistically significant (P > 0.05). At the genus level, Lactobacillus and Candidatus_Arthromitus were significantly enriched in the EM group compared to the other groups (P < 0.05) (Fig. 4C). The LEfSe analysis further indicated a significant enrichment of Proteobacteria in the EC group, while the EM group showed notable enrichment of Firmicutes and Acidobacteria. At the genus level, Lactobacillus, Candidatus_Arthromitus, and Candidatus_Solibacteres were significantly enriched in the EM group (Fig. 4D, E).

Fig. 4.

Effect of dietary MCFAs supplementation on Microbial community composition and diversity in broilers. A, Relative abundance differences of the phyla Proteobacteria, Bacteroidetes, and Firmicutes. The bars represent the mean relative abundances of each phylum, with error bars indicating standard deviation; B, F/B ratio; C, Relative abundance of Lactobacillus and Candidatus_Arthromitus; D, Cladogram from LEfSe analysis indicating taxonomic hierarchy and effect sizes (color intensity for larger effects); E, LDA scores plot for significantly different taxa between groups, with a threshold of 2.0 for significance. NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection; Significance levels: ^–P > 0.1; * 0.5 < P < 0.1; ^⁎⁎ 0.01 < P < 0.05; ^⁎⁎⁎P < 0.01.

Fig. 5 illustrates the functional prediction of the microbiota. The results of Constrained Principal Coordinates Analysis (CPCoA) (Fig. 5A) show distinct clustering of microbial functional composition in low-dimensional space, with the X-axis (CPCoA1) and Y-axis (CPCoA2) explaining 63.58% and 36.42% of the variation, respectively. Environmental variables were strongly correlated with the variation in CPCoA1 (X-axis). The results of Permutational Multivariate Analysis of Variance (PERMANOVA) indicated significant differences in microbial function between groups (P = 0.023), suggesting that the addition of MCFAs was the primary factor driving these differences. The heatmap illustrates the macro-level differences in microbial communities between the groups. At level 1 (L1) of the KEGG pathways (Fig. 5B), the EM group showed significant enrichment of microbes related to genetic information processing pathways (P = 0.008) (Fig. 5D). At level 3 (L3), carbohydrate metabolism and other amino acid metabolism pathways were enriched in the EM group (Fig. 5C). Additionally, MetaCyc pathway analysis revealed a significant reduction in microbial pathway activity associated with PWY-5941 pathway in the EM group compared to the other groups (P = 0.0013) (Fig. 5E).

Fig. 5.

Effect of dietary MCFAs supplementation on functional pathways in broilers. A, PCoA of Bray-Curtis distances showing clustering of microbial communities in NC, EC, and EM groups, with CPCoA1 and CPCoA2 explaining 63.58% and 19.42% of the variation, respectively, and a total variance of 14.5% among groups (P = 0.023); B, Heatmap of functional categories based on KEGG pathway analysis; C, Heatmap of specific metabolic pathways by group; D, Bar chart of relative abundances in genetic information processing pathways (P = 0.008, groups with same letter are not significantly different); E, Violin plot for PWY-5941 abundance across groups; NC, basal diet without E. coli infection; EC, basal diet with E. coli infection; EM, MCFAs diet with E. coli injection. Significance levels: ^–P > 0.1; * 0.5 < P < 0.1; ^⁎⁎ 0.01 < P < 0.05; ^⁎⁎⁎P < 0.01.

Discussion

APEC negatively affects poultry growth and health by inducing intestinal inflammation. Our research confirmed E. coli O78 infection reduced growth performance in broilers. Similarly, research has shown that the immune response to infection requires substantial energy, diverting resources from growth and ultimately reducing production performance (Faas and deVos, 2020). Interestingly, different supplements are promising for combating various disease pathogens in broiler production through mechanisms such as decreased nutritional competition, reduced inflammation, and enhanced digestion. In this context, MCFAs have a promising effect on broiler production due to their high solubility and ability to be transported to the liver via the portal circulation, where they undergo β-oxidation, allowing for rapid absorption and energy replenishment (Ferreira et al., 2014). This may explain the significantly higher BW at day 11 and improved FCR during days 8-18 in infected chicks supplemented with MCFAs compared to those without supplementation, consistent with findings in pig models (Gebhardt et al., 2020).

MCFAs play a role in modulating the immune response, as observed in organ indices, morphology, and the immune protein expression. In this study, broilers with MCFAs diets maintained stable intestinal indices during infection. Following the resolution of the inflammatory response, both duodenum and jejunum indices increased, suggesting that MCFAs might protect the intestinal integrity during the acute infection phase and subsequently promote the repair of villi and crypt cells. Studies in piglets have shown that MCFAs increase jejunal villus height and crypt depth (Marchetti et al., 2024). The significant increase in jejunum villus height observed in the EM group further supports this hypothesis, as increased villus height is associated with an expanded intestinal surface area, thereby enhancing nutrient absorption efficiency. The immune organ indices provide insight into the immune response during infections. The spleen, as a central immune organ, is essential for pathogen filtration from the bloodstream and initiating B and T cell activation (Mebius and Kraal, 2005). Spleen enlargement is commonly associated with immune activation. In this study, the spleen index in EC group showed a significantly increase. However, no significant change in spleen index was noted in the EM group compared to NC group, which suggests that MCFAs might exert an immunomodulatory effect. After the infection, we observed a significant increase in the spleen index in the EM group, which may indicate the recovery and enhancement of the immune system or the formation of immune memory. Meanwhile, feeding LPS-infected mice with black soldier fly oil rich in MCFAs also alleviated spleen enlargement (Richter et al., 2023). This observation suggests that MCFAs help regulate the immune response.

MCFAs appear to exert effects through direct antimicrobial action and immune modulation. Firstly, MCFAs may inhibit bacterial proliferation, limiting bacterial translocation into the bloodstream. Mechanistically, MCFAs could disrupt bacterial membranes, leading to cell lysis (Yoon et al., 2018). For example, caprylic acid monoglyceride disrupts Helicobacter pylori membranes (Sheu et al., 2006), while lauric acid monoglyceride targets Candida albicans (Bergsson et al., 2001), and interferes with cell wall synthesis in Staphylococcus aureus (Schlievert and Peterson, 2012) and Enterococcus faecalis (Hess et al., 2015). Lauric acid monoglyceride has also been shown to reduce bacterial spore heat resistance (Yang et al., 2017). This bacteriostatic effect might be supported by the lower serum DAO concentrations observed in the EM group. DAO is a key marker of intestinal damage and permeability; its reduction suggests enhanced intestinal integrity and reduced bacterial translocation (Cheng et al., 2024).

Secondly, MCFAs seem to enhance the activity of immune proteins such as immunoglobulins and LZM. In this study, MCFAs significantly increased levels of LZM and immunoglobulins, which is consistent with You in the piglet experiment (You et al., 2023). Lysozymes hydrolyze peptidoglycans in bacterial cell walls, rendering bacteria more vulnerable to immune responses such as complement activation and phagocytosis (Ragland and Criss, 2017). Immunoglobulins are also critical to pathogen defense and innate immunity: IgG, the most abundant antibody in the adaptive immune system, neutralizes pathogens by binding to specific antigens and facilitates antibody-dependent cellular phagocytosis. IgM is particularly effective at complement activation, promoting pathogen lysis and enhancing phagocytosis (Keyt et al., 2020), while IgA binds toxins and antigens, preventing pathogen adherence to mucosal surfaces (Moor et al., 2017). Additionally, the significantly elevated bursa of the Fabricius index in the EM group further underscores the role of MCFAs in modulating adaptive immunity. The bursa of Fabricius is the primary site for B cell generation and maturation in chicks (Glick, 1991). The B cell responsible to produce antibodies such as IgM, IgG, and IgA, which are essential for robust immune defense against pathogens.

To further investigate the effects of MCFAs on poultry immunity, we examined the mRNA expression of key immune proteins in the jejunal mucosa of broilers. Our results showed that MCFAs reduced the mRNA expression of TLR4, IL-1β and IL-6 in the jejunal mucosa, suggests that MCFAs play a crucial role in modulating intestinal immune responses. This finding is consistent with research conducted on medium-chain triglycerides (MCTs) in piglets (Zhang et al., 2018). TLR4 detects lipopolysaccharides (LPS) from Gram-negative bacteria (Akira et al., 2006). Upon recognition, TLR4 signaling activates the NF-κB transcription factor, which promotes the expression of pro-inflammatory genes such as TNF-α, IL-1β, and IL-6 (Gay et al., 2014; Kerr et al., 2022; Liu et al., 2017). Acute E. coli infections often lead to severe intestinal inflammation, causing structural damage, impaired barrier function, and reduced nutrient absorption, which stifles growth and reduces production performance in poultry. Our findings indicate that MCFAs may reduce TLR4-mediated inflammation, thereby limiting intestinal damage and preserving barrier function under E. coli infection. The regulatory mechanism of MCFAs on TLR4 expression may involve the interference with LPS binding to TLR4. Normally, LPS first binds to LPS-binding protein and membrane CD14 (mCD14), which then facilitate its binding to TLR4, triggering TLR4 dimerization and subsequent signaling. Wong et al. reported that MCFAs can inhibit TLR4 dimerization, thus modulating the TLR4 signaling pathway (Wong et al., 2009). However, other studies have indicated that MCFAs do not affect the TLR4 pathway (Olthof et al., 2015). Furthermore, Sam demonstrated that MCFAs may exert their effects through the Toll-like receptor 2 (TLR2) pathway not TLR4 pathway (Sam et al., 2021). However, further research is needed to confirm this mechanism.

The intestinal microbiota plays a crucial role in supporting immunity and preventing E. coli colonization. On one hand, the gut microbiota competes with E. coli, reducing its chances of colonization. On the other hand, it regulates the intestinal environment and activates the immune system through interactions with the habitat filtering (Rogers et al., 2020). In this study, the MCFAs influenced both immune status and digestion in poultry by modulating the structure and abundance of the microbiota, thereby mitigating the growth suppression caused by E. coli infection.

Changes in microbiota due to dietary alterations can be assessed through α-diversity and β-diversity. In this study, although there was no significant difference in α-diversity among the groups due to the limitation of the possible test time scale, the PLS-DA results revealed distinct microbial community compositions. Distinct microbial community composition in the EM group may reflect adaptive changes that support resilience against E. coli infection through altered nutrient utilization and immune support. These differences may be attributed to both E. coli infection and the presence of MCFAs in the broiler diet. Specifically, while the F/B ratio exhibited only a non-significant upward trend, MCFAs significantly decreased Proteobacteria abundance and increased Firmicutes abundance. The abundance of Proteobacteria and Firmicutes is often considered an ecological indicator of gut health. Proteobacteria include many potential pathogens such as Salmonella (Foley et al., 2013), Vibrio (Ayala et al., 2023), Yersinia (Fang et al., 2023) and E. coli (Paroni et al., 2023), and their presence is often associated with intestinal inflammation, microbial imbalance, and chronic diseases. A significant reduction in Proteobacteria abundance may therefore imply a decrease in pathogenic microorganisms and their toxic metabolites, helping to reduce intestinal inflammation and promote gut health and homeostasis. Firmicutes, on the other hand, include microbiota that utilize complex polysaccharides and cellulose to produce beneficial metabolites such as short-chain fatty acids (SCFAs) (Sun et al., 2023). These SCFAs support intestinal epithelial health, provide energy, and modulate host immune responses (Tan et al., 2014). An increase in Firmicutes abundance may be associated with positive regulation of the intestinal environment, including the occupation of probiotics that inhibit harmful microorganisms and promote microbial balance and stability. We speculate that the MCFAs diet modified the microbial community structure in broilers for two reasons. Firstly, MCFAs, as antimicrobial fatty acids, likely contributed to the reduction in Proteobacteria abundance by directly killing pathogenic bacteria. Secondly, some Firmicutes members possess a high capacity to metabolize fatty acids, including MCFAs, to produce beneficial metabolites like SCFAs. In turn, these metabolites alter the intestinal environment by lowering oxygen levels and pH, creating conditions more favorable for the growth of Firmicutes. Thus, MCFAs may enhance immunity by modifying the microbial community structure that positively influence the E coli O78-infected chicks.

The impact of MCFAs on microbial community structure was demonstrated at the genus level. In our study, the EM group exhibited a significant increase in the abundance of Candidatus Arthromitus, a bacterium known for its direct interaction with intestinal epithelial cells. This interaction enhances epithelial cell proliferation and adhesion, strengthens mucosal barrier integrity, and activates T helper 17 (Th17) cells (Hedblom et al., 2018). In addition, the EM group showed a notable increase in acid-producing bacteria, including Lactobacillus and members of the Clostridiaceae family. These bacteria produce lactic acid and SCFAs (Claesson et al., 2007), acidify the intestinal environment, inhibit the growth of pathogenic bacteria, and improve gut health (Bron et al., 2011; Sheu et al., 2006). Butyric acid also promotes anti-inflammatory immune responses by enhancing regulatory T cell activity (Zhu et al., 2023) and modulating macrophage polarization (Wu et al., 2022), contributing to immune balance during infection. Lactic acid enhances the secretion of antimicrobial peptides (Sablon et al., 2000) and secretory IgA (sIgA) (Ashraf et al., 2014), strengthening the intestinal barrier function. Additionally, SCFAs stimulate the enteric nervous system, increasing intestinal motility and promoting the absorption of essential minerals like calcium and magnesium, further supporting a healthier gut environment and enhanced resistance to infections (Fan et al., 2022).

Notably, the observed enrichment of Candidatus_Arthromitus, Lactobacillus spp., and other acid-producing genera in the EM group suggests that MCFAs may indirectly modulate immune signaling via microbial shifts. Metabolites produced by Lactobacillus spp., including lactic acid and SCFAs, can lower intestinal pH, inhibit pathogen proliferation, and regulate TLR4-mediated pro-inflammatory signaling (Thu et al., 2010; Gao et al., 2022; Xie et al., 2024). Mechanistically, SCFAs act as histone deacetylase inhibitors, enhancing inhibitor of nuclear factor kappa-B alpha (IκBα) expression and thereby attenuating NF-κB nuclear translocation, which suppresses the transcription of pro-inflammatory cytokines (Park et al., 2015). In parallel, SCFAs engage G-protein coupled receptors, such as G-protein-coupled receptor 43 (GPR43) and G-protein-coupled receptor 109a (GPR109a), can modulate mitogen-activated protein kinase (MAPK) and NF-κB pathways, further fine-tuning epithelial and immune cell responses to TLR4 stimulation (Hong et al., 2022; Zhao et al., 2025). Similarly, Candidatus_Arthromitus directly interacts with intestinal epithelial cells, promoting Th17 differentiation via serum amyloid A-mediated signaling; Th17-derived IL-17 enhances epithelial barrier integrity through upregulation of tight junction proteins, which may limit pathogen-derived LPS-TLR4 interactions (Farkas et al., 2015). Collectively, these findings support a model in which MCFA-induced microbial shifts and their metabolites orchestrate a “microbiota-metabolite-TLR4 axis”, attenuating pathogen-triggered TLR4 activation while reinforcing mucosal barrier function and adaptive immunity.

Functional prediction of the gut microbiota revealed key microbial contributions to poultry physiological processes by analyzing the genomic and metabolic potential of the microbiota. Enrichment in the genetic information processing pathway suggests that E. coli infection may trigger a stress response in the gut microbiome, increasing the need for gene replication, repair, and transcription. This indicates that the gut microbiota, under MCFAs supplementation, is highly active in gene replication and repair, reflecting strong adaptability to environmental changes, such as dietary shifts and bacterial infections. The enrichment of carbohydrate metabolism and amino acid metabolism pathways suggests that the microbiota is actively involved in digesting and utilizing carbohydrates and amino acids (Oliphant and Allen-Vercoe, 2019). In response to E. coli infection, the gut microbiota may enhance these metabolic pathways to compensate for energy loss and regulate the metabolic disturbances caused by the infection (Agus et al., 2021). Furthermore, a reduction in the PWY-5941 pathway may indicate selective inhibition of certain microbial metabolic functions by MCFAs, reflecting their modulatory effect on the gut microbial community. Together, these metabolic changes highlight how the microbiota adjusts its functions to maintain host health and homeostasis under varying conditions.

Therefore, these findings support our hypothesis that dietary MCFAs alleviate intestinal inflammation in E. coli O78-infected broilers by modulating the TLR4-NF-κB pathway and reshaping the gut microbiota. Specifically, MCFAs promoted immunoregulatory and acid-producing bacteria, enhanced nutrient metabolism, and improved intestinal immunity and barrier function, thereby reducing inflammation and E. coli colonization. Further research should integrate microbial metabolomics to elucidate the underlying metabolic mechanisms.

Conclusions

In conclusion, dietary supplementation with 700 mg/kg MCFAs significantly mitigates the adverse effects of E. coli O78 infection in broilers by enhancing both systemic and mucosal immune responses. This protective mechanism is associated with the promotion of beneficial bacterial taxa, including Lactobacillus spp. and Candidatus_Arthromitus, coupled with the downregulation of the TLR4-mediated inflammatory pathway. These synergistic effects contribute to improved intestinal morphology, serum immunity, and growth performance following E. coli O78 challenge. Collectively, the findings confirm our central hypothesis that MCFAs represent an effective nutritional intervention for enhancing broiler health and disease resilience through concurrent immunomodulatory and microbiota‐targeted actions.

Data and model availability statement

Bacterial 16S rRNA gene sequencing data have been uploaded to the NCBI SRA database under accession number PRJNA1272804. These data are publicly accessible and can also available upon request from the first author.

Declaration of generative AI and AI-assisted technologies in the writing process

The authors did not use any generative AI or AI-assisted technologies in the preparation of this manuscript.

Financial support statement

This work was supported by the Agricultural Science and Technology Innovation Program (ASTIP) of the Chinese Academy of Agricultural Sciences (CAAS-IFR-2023-01), the National Key R&D Program of China (2021YFD1300404), and Vitamex Trading (Shanghai) Co., Ltd. The authors gratefully acknowledge this financial support.

CRediT authorship contribution statement

Changchun Xu: Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Qiongge Liu: Writing – review & editing. Mengjie Xu: Writing – review & editing. Habtamu Ayalew: Writing – review & editing. Waqar Iqbal: Writing – review & editing. Jing Lin: Methodology. Jingyu Song: Methodology. Linghong Jin: Resources. Zhigang Song: Supervision. Haijun Zhang: Project administration.

Disclosures

The authors declare no competing interests or any conflicts that could influence the results or discussion presented in this paper.