Effect of Mannan-rich fraction supplementation on commercial broiler intestinum tenue and cecum microbiota

Abstract

Background

The broiler gastrointestinal microbiome is a potent flock performance modulator yet may also serve as a reservoir for pathogen entry into the food chain. The goal of this project was to characterise the effect of mannan rich fraction (MRF) supplementation on microbiome diversity and composition of the intestinum tenue and cecum of commercial broilers. This study also aimed to address some of the intrinsic biases that exist in microbiome studies which arise due to the extensive disparity in 16S rRNA gene copy numbers between bacterial species and due to large intersample variation.

Results

We observed a divergent yet rich microbiome structure between different anatomical sites and observed the explicit effect MRF supplementation had on community structure, diversity, and pathogen modulation. Birds supplemented with MRF displayed significantly higher species richness in the cecum and significantly different bacterial community composition in each gastrointestinal (GI) tract section. Supplemented birds had lower levels of the zoonotic pathogens Escherichia coli and Clostridioides difficile across all three intestinum tenue sites highlighting the potential of MRF supplementation in maintaining food chain integrity. Higher levels of probiotic genera (eg. Lactobacillus and Blautia) were also noted in the MRF supplemented birds. Following MRF supplementation, the cecum displayed higher relative abundances of both short chain fatty acid (SFCA) synthesising bacteria and SCFA concentrations.

Conclusions

Mannan rich fraction addition has been observed to reduce the bioburden of pathogens in broilers and to promote greater intestinal tract microbial biodiversity. This study is the first, to our knowledge, to investigate the effect of mannan-rich fraction supplementation on the microbiome associated with different GI tract anatomical geographies. In addition to this novelty, this study also exploited machine learning and biostatistical techniques to correct the intrinsic biases associated with microbiome community studies to enable a more robust understanding of community structure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-022-00208-6.

Introduction

In recent years, the health impact of intestinal and cecal microbiome composition has become a prominent research focus in poultry science [29], 42]. Understanding and modulating the intrinsic and extrinsic interplay between differential microbial populations and their host environment has led to improved animal health and greater profitability in agricultural endeavours [23]. At present, broiler chickens (Gallus gallus subsp. domesticus; “broilers”) constitute the most consumed meat worldwide, with an approximate 100 million tons of poultry meat produced annually [67]. Due to their economic importance, high nutritive value, and accessibility of their meat, broilers have been extensively subjected to, and immensely benefitted from, intestinal microbiome composition and modulation analyses [11–13, 74]. The combined efforts of such research endeavours have reduced chick mortality, increased growth rates, and reduced the microbial load of major poultry and human pathogens [17, 52, 93]. Efforts of particular importance (and success) involve modulating microbiome composition using feed supplements [11].

The holobiont theory suggests that the health, metabolic prowess, and overall success and survivability of an organism is largely influenced by the composition, diversity, and complexity of their associated microbiomes [80]. Most previous studies of the chicken gut microbiome have focused on the ceca due to their dense bacterial populations which aid in digestion of otherwise indigestible residues remaining in chyme, bioconverting them to digestible metabolites for host absorption (eg. digestion of cellulose to glucose; [50, 69, 78, 84]). Many studies have found that differing microbiome compositions are strongly correlated with disease states across Metazoan lineages [22, 48, 78], and their modulation (via nutrient supplementation or transplantation) has resulted in profound improvements in human and animal health [11, 29, 52].

Due to increases in antimicrobial and metal or biocide resistance arising from their systematic use and misuse as livestock growth promoters and over-prescription in human medicine, alternative growth promotion techniques and supplements are being explored without using clinically relevant compounds [96]. One of the most promising poultry feed supplements are prebiotics containing mannan, such as mannan rich fraction (MRF) derived from Saccharomyces cerevisiae cell wall residues [8, 41]. These compounds display particular efficacy in binding to type-1 fimbriae in Gram-negative bacterial pathogens, specifically Enterobacteriaceae [28]. Reduction of such populations allows mutually symbiotic and commensal microbiota such as Lactobacillus to flourish [10, 29, 74, 93].

While 16S microbiome studies are highly informative, there may be some bias and lost significance due to the disparate number of 16S rRNA genes between species [95]. As these differences can be quite pronounced, we constructed a large 16S rRNA dataset from publicly available bacterial genomes and devised a simple weighting system, where each read from each taxon was divided by the number of 16S rRNA genes available in each taxon. The purpose of this procedure was to reduce bias caused by the widely uneven 16S rRNA gene counts commonly observed across Domain Bacteria.

Avian gut microbiome reports display considerable animal-to-animal variation which has the potential to incorrectly bias post hoc statistical comparisons [30, 99]. To counter this problem, we employed isolation forests (a common machine learning technique) and median imputation to each sample to remove and replace any outliers to decipher any previously unseen underlying trends [56]). The aim of this study was to investigate the impact of MRF addition on the microbial communities of the three main GI nutrient absorption sites (duodenum, jejunum, and ileum) of the intestinum tenue (“small intestine”) and the cecum in broilers. By removing intrinsic and extrinsic biases from 16S rRNA gene counts and cumulative community structures, we aim to highlight otherwise overlooked microbial taxa that may be of importance in food safety microbiology.

Methods

Sample collection and preservation

This broiler trial was performed at a commercial production site within the European Union. On the day of hatch, chicks were taken from a commercial hatchery and transported to an associated commercial farm. Approximately 35,000 birds were placed from the hatchery into each of two sheds where they received a control standard commercial wheat-soya diet or a standard diet plus MRF (Alltech Biotechnology) at the following inclusion rates; 1300:1000:600 g^t−1 starter, grower, and finisher rations respectively. Birds were raised and fed as per typical commercial production conditions receiving feed and water ad libitum. All other conditions were kept uniform for both sheds. At day 35 (post-hatch) the intact gastrointestinal tracts of 12 randomly caught birds per shed were excised immediately after humane euthanisation. Intestinal contents from the duodenum, jejunum, ileum, and cecum were massaged into individual sterile tubes, immediately frozen on dry ice, transported within 8 h and stored at −80 °C for downstream processing.

DNA extraction and 16S rRNA gene sequencing

DNA was extracted from intestinal contents using the QIAamp DNA Stool Mini Kit according to the manufacturer’s instructions using 0.05 g of intestinal content (QIAamp DNA Stool Mini Kit, Qiagen). Genomic DNA concentration was determined at a wavelength of 260 nm using a NanoDrop (NanoDrop). Isolated DNA was then used as a template in PCR amplification for construction of 16S rDNA libraries which were prepared and sequenced by BaseClear genomics. Sequencing libraries were prepared by amplification and barcoding of the 16S rRNA gene V3–V4 region and the resulting amplicons were sequenced on an Illumina MiSeq platform generating 10–50 k PE300 reads per sample. The mean library size used was 580 bp (inclusive of barcodes and adapters) and the insert size was approximately 460 bp (580–120 = 460 bp). A total 3,988,410 reads were achieved. In the control dataset, average reads for each of the duodenum, jejunum, ileum and cecum were observed to be 41,749.42 ± 6442.53, 45,074.75 ± 6468.97, 42,135.83 ± 7449.29, and 48,489.92 ± 4364.9 respectively. Comparatively, in the MRF-treated dataset average reads of 35,883.92 ± 4765.3, 35,644.5 ± 9590.25, 43,873.5 ± 6593.51, 39,495.67 ± 8224.7 for the duodenum, jejunum, ileum, and cecum were observed.

Dataset construction

Each sample was adapter and quality trimmed using TrimGalore! v.0.6.6 [54]under default settings and powered with cutadapt v.3.0 [64] and FastQC v.0.11.9 [7]. Between 14,044 and 54,118 reads were observed pre-quality-control and between 13,868 and 53,830 after, with an observed percentage read discard range between 0.325% and 9.66%. Chimeras were identified using UCHIME v.4.2.40 [36] and removed. Quality controlled reads were merged using the “–fastq_merge” function in VSEARCH v.2.14.2 [37, 79 to give a single entry for each read pair in FASTA format.

16S rRNA database construction

A database of 16S rRNA genes was constructed by downloading all bacterial genome assemblies (n = 274,268) from NCBI assembly [51] and extracting all 16S rRNA genes using Barrnap v.0.9 (as used for rRNA detection by Prokka v.1.1.14 [83]) with default settings. Taxonomic lineages were assigned to each genome (and their associated genes) using the “lineage” function in TaxonKit v.0.6.0 [87] and standardised to the seven ranks (Domain, Phylum, Class, Order, Family, Genus, and Species) using the TaxonKit “reformat” function. Sequences with length less than 1200 nucleotides (nt) were discarded to mirror the strict filtering methods employed during the construction of the SILVA database [75]. Remaining sequences were searched against all other remaining sequences using the “–usearch-global” function in VSEARCH v.2.14.2 with a minimal percentage identity stringency score of 0.97 (97%), self-hits were excluded, and, with the exception of Escherichia, Shigella, and Salmonella (ESS), top-hit pairs where sequences were observed to be from different genera were discarded. The ESS species were excluded from further filtration during this step due to the close evolutionary relatedness of these clinically relevant genera [38, 43, 91]. Finally, exact duplicates of 16S rRNA genes were removed resulting in a database of 68,724 16S rRNA genes from 21,928 species from 37 definite phyla and 70 candidate phyla/divisions (107 in total). This dataset is available for download at (https://github.com/RobLeighBioinformatics/Broiler_GI_microbiome).

Database weighting

Bacterial genomes are highly dynamic due to rapid gene duplication, loss, and horizontal transfer events which may result in varying numbers of 16S rRNA genes [95]. Alien and spurious 16S rRNA genes were removed during database construction, so it is anticipated that all genes in the database were chromosomal in origin. Species were weighted by the number of 16S rRNA genes remaining in each genome after the strict filtration steps during database construction. The median number of 16S rRNA genes was taken where multiple genomes from the same species were retained. Genera weighting was calculated by excluding all genomes not definitively identified to species level (eg. genomes labelled “Salmonella sp.” (as opposed to, for example, Salmonella enterica or “undefined Lactobacillaceae”) and assumed to be the median for all species in a given genus. For higher taxonomic ranks, the median of rank medians was taken (eg. for families, the median of all genera medians in each family was taken). This method was employed to prevent biasing from well sampled species in a genus compared to less common species (eg. Escherichia coli vs. Escherichia marmotae). This weighting table is available at https://github.com/RobLeighBioinformatics/Broiler_GI_microbiome.

Taxonomic assignment and weighting

Each read entry was searched against our 16S rRNA database using the “–usearch-global” function in VSEARCH and top hits with an alignment stringency cut-off of 0.97 (97%) were extracted (Additional file 1: Tables S1–S6). To mitigate taxonomic misassignment, the stringency cut off was increased to 0.99 (99%) for species level assignment. Read counts were then weighted using the 16S rRNA gene counts calculated above (Additional file 1: Tables S7–S12). The proportion of each weighted taxon in each sample was computed and normalised (closed) by dividing by a “closure constant” (CC) for each sample and dividing each weighted read count per taxa by the closure constant (Additional file 1: Tables S13–S18). This standardisation ensures all samples have the same number of reads for downstream comparative analysis. The standardisation constant was constructed using the formula:

$$\text{CC}=\frac{{\mathrm{\Sigma }}_x}{max\left({\mathrm{\Sigma }}_{x_1},{\mathrm{\Sigma }}_{x_2},...,{\mathrm{\Sigma }}_{x_n}\right)};\text{CC}\le 1$$

where x: Series of reads in a sample/replicate.

Outlier processing

Due to the extensive intersample variation observed in microbiome studies [99], as discussed previously, we endeavoured to remove extreme outliers to examine potential underlying trends that may be otherwise obfuscated. Outliers were removed and imputed with the median of the remaining inliers using uniForest v.1 with default parameters [56].

Fold changes

For all comparisons made below, median fold changes (η_FC) were calculated using the formula:

$${\eta }_{\text{FC}}=\frac{{\eta }_{(b)}-{\eta }_{(a)}}{{\eta }_{(a)}}$$

where η_(x): Median observation for group x.

Fold changes have a lower limit of −1 (complete depletion) and no change is represented by 0. A FC is incalculable if η_(a) = 0 as this represents a complete introduction.

Statistical analysis

Kolmogorov-Smironov tests [53, 92] using a Lilliefors’ distribution [59] were used to determine sample series distribution normality (H₀:X ~ N(μ,σ²;H_A:X≁N(μ,σ²); P > 0.05: X ~ N(μ,σ²)) and as all distributions were determined to follow a non-normal distribution, Brunner–Munzel tests [20] were used to compare taxa between the control and MRF treated datasets H₀:B = 0.5;H_A:B ≠ 0.5). A Brunner–Munzel test was used instead of a Mann–Whitney U test [62] as the data was assumed to have unequal variance due to the high level of variability usually observed in microbiome analyses [99]. A Bonferroni–Dunn (BD; P_BD) correction [16, 35] was applied to each test (P_BD = P × n_comparisons) and instances where P_BD ≤ 0.05 were considered to be statistically significant (Additional file 1: Table S19) and the FC (as described above) was used to indicate the trend changes. Different n_comparisons were used to calculate P_BD (by taxonomic rank) to strengthen confidence in results at lower taxonomic ranks, however, to restrict an overly stringent correction, statistical comparisons were only performed when η_Control or η_MRF > 20 (or η_site(a) or η_site(b) > 20).

Ecological statistics

A bias-corrected Chao1 richness estimator [24], Simpson’s D index [90], Simpson’s E index [90], and Shannon’s H index [85] was calculated for each anatomical site in each dataset at each taxonomic rank using the sklearn-bio (skbio) v.0.2.0 Python library (http://scikit-bio.org/). A Brunner–Munzel test (H₀:B = 0.5;H_A:B ≠ 0.5) was performed between diversity indices at each rank. A Bonferroni–Dunn correction was performed for each subset (n_comparisons = 4) and instances where P_BD ≤ 0.05 were considered statistically significant (Additional file  1: Table S20). Statistical trend changes were determined using the FC calculation described above.

A principal component analysis [47, 73] (PCA) was performed between all data subsets at each site using the “PCA” module in the “sklearn.decomposition” Python machine learning library. A permutational analysis of variance [4] (PERMANOVA) was used to compare control vs MRF treated samples. A PERMANOVA is used to compare the centroid and dispersion of two groups based on the 2 dimensional (2D) or 3D coordinates of their points using 999 iterations (i_n = 999). A Bonferroni–Dunn correction was applied (n_comparisons = 4) and a P_BD ≤ 0.05 was considered statistically significant (Additional file 11: Table S21).

A Bray–Curtis distance matrix [19] was constructed between control and MRF-treated datasets for each anatomical site using the “beta_diversity” driver function from the “skbio.diversity” Python library and a principal coordinate analysis (PCoA) was performed on each distance matrix using the “pcoa” function from the “skbio.stats.ordination” package. A PERMANOVA was used to compare control vs MRF treated PCoA groups using 999 iterations (i_n = 999) as is common practice. A Bonferroni–Dunn correction was applied (n_comparisons = 4) and a P_BD ≤ 0.05 was considered statistically significant (Additional file  1: Table S21).

Short chain fatty acid concentration analysis

The concentrations of three short chain fatty acids (SFCA; acetate, propionate, and butyrate) in cecal digesta was measured using gas chromatography after metaphosphoric acid derivation as previously described with minor modifications [77]. Briefly, 0.20 g of thawed sample was diluted with 2 mL double-distilled water in a sterile screw-capped tube, then homogenized, and centrifuged at 4000 × g for 10 min at 10 °C. A volume of 1 mL of supernatant was then transferred to another Eppendorf tube and mixed with 0.2 mL, 25% (wt/vol) ice-cold metaphosphoric acid solution. Subsequently, this solution was kept at − 20 °C for 4 h. Samples were then thawed, 0.1 mL 4 M sodium hydroxide solution added and centrifuged at 4000 × g for 10 min at 10 °C before analysis. The supernatant was then filtered with a 0.22 μm membrane, and an injection volume of 0.4 μL of sample solution was analyzed using a gas chromatography (Agilent 7890A system) coupled with a CP-Wax 58 FFAP CB column (Agilent) and flame ionization detector to determine SCFA concentrations in cecal content. The concentrations of acetate, propionate, and butyrate were calculated and expressed as μmol/g of wet cecal digesta.

Again, Kolmogorov-Smironov tests (using a Lilliefors’ distribution) were used to determine sample series distribution normality (H₀:X ~ N(μ,σ²);H_A:X≁N(μ,σ²); P > 0.05: X ~ N(μ,σ²)) for control and MRF-treated SFCA concentration series. Equivarience was assessed using a Levene’s test (H₀:σ²_a = σ²_b;σ²_a ≠ σ²_b) [57]. As equivariance was not observed between any pair and as one distribution (MRF-treated acetic acid) was determined to follow a non-Gaussian distribution, Brunner-Munzel tests were used to compare each taxon between the control and MRF treated datasets H₀:B = 0.5;H_A:B ≠ 0.5) (Additional file 1: Table S22).

Results

Broiler growth characteristics

The growth indices of the MRF supplemented broilers were compared with the control (Table 1). Feed conversion ratios and average live weights did not differ significantly between the two groups however, the MRF supplemented birds were on average 5 g heavier and finished 1 day earlier than the control group. Birds supplemented with MRF tended to have a greater European production efficiency factor (EPEF).

Table 1.

Comparison of growth indices of broiler commercial units with and without MRF dietary supplementation

	Mean live weight (kg)	Age (d)	EPEF	FCR
Control	1.964	35.60	341.622	1.589
MRF	1.968	34.77	347.702

Effect of diet and GI tract section on α- and β- diversity

A total 3,988,410 sequence reads were recovered from the 96 samples analysed. In the control dataset, average reads for each of the duodenum, jejunum, ileum, and cecum were observed to be 41,749.42 ± 6442.53, 45,074.75 ± 6468.97, 42,135.83 ± 7449.29, and 48,489.92 ± 4364.9, respectively. Comparatively, in the MRF supplemented dataset average reads of 35,883.92 ± 4765.3, 35,644.5 ± 9590.25, 43,873.5 ± 6593.51, 39,495.67 ± 8224.7 for the duodenum, jejunum, ileum, and cecum, respectively.

Microbial diversity at the four anatomical sites was estimated using α-diversity indices (Chao1 index, Simpson’s E (evenness), and Shannon’s H’ index). Chao1 was used to estimate richness (Fig. 1a), Shannon's H’ index was used to indicate diversity (Fig. 1(b..)) and Simpson’s E was used to indicate evenness (Fig. 1(c.); Additional file 1: Table S20). Richness was observed to be significantly increased in the MRF-treated ceca (Chao1:η_FC = 0.1311) and significantly lower in MRF-treated duodena (Chao1:η_FC = -0.3072) and jejuna (Chao1:η_FC = −0.2241) respectively. Evenness was not observed to be significantly affected by MRF-addition and the ileum was not observed to be modulated post-treatment.

Fig. 1.

a–c Four α-diversity metrics displayed for the four anatomical sites explored in this study. Statistically significant (P_BD ≤ 0.05) results are highlighted with an asterisk

Differences in β-diversity within the intestinal microbial population between groups and between intestinal sections within groups were assessed using PCoA (Figs. 2 and 3). The PCoA plots shown in Fig. 2a–d show that the bacterial community composition at the species level differed significantly (P_BD ≤ 0.05) as a result of diet in each intestinal section with PC1 accounting for 60.1%, 69.28%, 49.13% and 91.32% of the total variation; PC2 accounting for 18.61%, 8.36%, 17.78% and 3.17%; and PC3 accounting for 7.38%, 5.63%, 13.48%, and 1.74% in the duodenum, jejunum, ileum, and cecum respectively. The bacterial community composition between intestinal sections was also analysed for differences and showed that each intestinal section harboured a distinct bacterial community structure regardless of diet (Fig. 3a, b, P_BD ≤ 0.05).

Fig. 2.

a–d Species-level Bray–Curtis distance matrices (β-diversity) expressed as PCoA between control (red) and MRF-supplemented broilers (blue) at each anatomical site

Fig. 3.

a, b Species-level Bray–Curtis distance matrices (β-diversity) expressed as PCoA between duodenal (red), jejunal (orange), and ileal (grey) anatomical sites across the control (left) and MRF-supplemented (right) datasets

Effect of diet and GI tract section on bacterial community composition

To determine which bacterial taxa contributed to separating bacterial communities based on diet and intestinal section, the phylum level relative abundances of each GI tract were considered (Table 2). At the phylum level, four main bacterial phyla were identified within each gastrointestinal section, Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria (newly renamed as Actinomycetota Bacteroidota, Bacillota, and Pseudomonadota, respectively [72]). Phylum Firmicutes was the predominantly abundant phylum within each GI section. Following MRF supplementation, Firmicutes were significantly lower in the duodenum, and significantly greater in the cecum. Actinobacteria was identified as the second most abundant phylum in all control group anatomical sites but was significantly lower in the duodenum and cecum as a result of MRF supplementation. Proteobacteria were significantly greater in the duodenum and significantly lower in the ileum following MRF addition to the diet. Finally, Bacteroidetes was predominantly detected in the cecum compared to any other site.

Table 2.

Relative abundances of bacterial phyla obserbed in each anatomical site in both control and MRF supplemented broilers

	Duodenum			Jejunum			Ileum			Cecum
	Control (η%)	MRF (η%)	FC	Control (η%)	MRF (η%)	FC	Control (η%)	MRF (η%)	FC	Control (η%)	MRF (η%)	FC
Firmicutes	77.64	52.87	−0.319*	87.11	96.66	0.110	88.49	87.81	−0.008	74.76	91.17	0.220*
Actinobacteria	18.00	2.59	−0.856*	12.12	2.84	−0.766	9.28	11.96	0.288	13.89	1.88	−0.864*
Proteobacteria	4.08	44.45	9.902*	0.81	0.36	−0.553	2.55	0.23	−0.908*	1.75	3.34	0.908
Bacteroidetes	0.02	0.04	N/A	0.00	0.00	N/A	0.00	0.00	N/A	9.57	3.99	−0.583

Significant differences (P_BD ≤ 0.05) are denoted by a superscript asterisk (*) and emboldened for each row in each intestinal section. Data associated with significance are also emboldened. Increases are denoted by positive fold changes whereas decreases are denoted by negative fold changes

The top 10 most abundant bacterial genera and species for each GI tract section in control and MRF supplemented groups are shown in Tables 3 and 4 respectively. At the genus level the most abundant genera within the intestinum tenue in both control and MRF supplemented groups were Lactobacillus followed by Bifidobacterium (> 90% abundance combined). In the MRF supplemented birds the duodenum samples were dominated by Proteobacterial genera Pseudomonas, Halomonas, and Shewanella. For the control dataset the most abundant species within the intestinum tenue were Bifidobacterium animalis, Lactobacillus crispatus, and Lactobacillus salivarius (accounting for a η_% > 65%). Comparatively, in the MRF-treated sample dataset, each intestinum tenue site had a distinct set of predominant species (Bifidobacterium animalis, Lactobacillus aviarus, Lactobacillus crispatus, and Lactobacillus kitasatonis; (however these were observed in highly divergent η_% between sites)) and alongside other species (listed below) accounted for η_% < 60% in all sites. For the duodenum, Pseudomonas veronii, and Pseudomonas sp. TKP were highly observed, and for the ileum, Lactobacillus vaginalis was also highly observed. In the cecum the most abundant genus was Faecalibacterium in both control and MRF supplemented groups (> 50%) followed by Bifidobacterium and Blautia in the control group and Blautia and Lactobacillus in the MRF supplemented group. For the control cecal dataset, the most abundant observed species were Faecalibacterium sp. An122, Bifidobacterium gallinarum, and Bifidobacterium pullorum, (accounting for a η_% > 65%). In the cecal MRF supplemented dataset, the most prominent species (accounting for η_% > 69%) were Faecalibacterium sp. An122, Blautia sp. An81, and Eubacterium sp. An11.

Table 3.

The (ten) most prevalent bacterial genera observed at each anatomical site in both control and MRF-treated datasets

Site	Rank	Control (Genus)	η%	MRF (Genus)	η%
Duodenum	1	Lactobacillus	71.153	Lactobacillus	48.579
	2	Bifidobacterium	21.083	Pseudomonas	34.323
	3	Pseudomonas	3.670	Halomonas	7.813
	4	Clostridioides	2.305	Shewanella	4.285
	5	Halomonas	0.745	Bifidobacterium	2.223
	6	Shewanella	0.554	Faecalibacterium	0.673
	7	Staphylococcus	0.301	Blautia	0.651
	8	Faecalibacterium	0.238	Staphylococcus	0.234
	9	Escherichia	0.142	Corynebacterium	0.189
	10	Blautia	0.139	Ruminococcus	0.108
Jejunum	1	Lactobacillus	81.649	Lactobacillus	94.982
	2	Bifidobacterium	12.749	Bifidobacterium	3.564
	3	Clostridioides	1.785	Staphylococcus	0.218
	4	Escherichia	0.517	Pseudomonas	0.160
	5	Agarivorans	0.196	Corynebacterium	0.132
	6	Pseudomonas	0.146	Faecalibacterium	0.087
	7	Halomonas	0.045	Blautia	0.051
	8	Streptococcus	0.040	Halomonas	0.026
	9	Staphylococcus	0.036	Streptococcus	0.025
	10	Shigella	0.035	Weissella	0.022
Ileum	1	Lactobacillus	84.029	Lactobacillus	87.716
	2	Bifidobacterium	9.845	Bifidobacterium	9.994
	3	Escherichia	2.053	Ca. Arthromitus	1.015
	4	Ca. Arthromitus	1.501	Corynebacterium	0.173
	5	Clostridioides	0.443	Escherichia	0.094
	6	Streptococcus	0.429	Staphylococcus	0.082
	7	Shigella	0.258	Romboutsia	0.075
	8	Agarivorans	0.135	Pseudomonas	0.051
	9	Romboutsia	0.110	Agarivorans	0.021
	10	Corynebacterium	0.049	Jeotgalicoccus	0.017
Cecum	1	Faecalibacterium	51.748	Faecalibacterium	54.531
	2	Bifidobacterium	15.388	Blautia	11.548
	3	Blautia	5.372	Lactobacillus	7.422
	4	Barnesiella	3.716	Eubacterium	3.423
	5	Lachnoclostridium	3.588	Lachnoclostridium	3.232
	6	Ruminococcus	2.689	Ruminococcus	3.125
	7	Eubacterium	2.503	Bacteroides	2.364
	8	Bacteroides	2.364	Bifidobacterium	2.036
	9	Lactobacillus	2.117	Pseudoflavonifractor	1.823
	10	Escherichia	1.734	Barnesiella	1.675

Median relative abundances (η_%) were used to determine the rank of each taxon in each subset

Table 4.

The (ten) most prevalent bacterial species observed at each anatomical site in both control and MRF-treated datasets

Site	Rank	Control (Species)	η%	MRF (Species)	η%
Duodenum	1	Lactobacillus crispatus	26.636	Pseudomonas veronii	34.906
	2	Bifidobacterium animalis	21.394	Pseudomonas sp. TKP	14.440
	3	Lactobacillus salivarius	21.043	Lactobacillus aviarius	8.370
	4	Lactobacillus reuteri	7.874	Lactobacillus kitasatonis	6.143
	5	Pseudomonas veronii	2.766	Shewanella algae	6.010
	6	Lactobacillus kitasatonis	2.676	Halomonas chromatireducens	4.046
	7	Clostridioides difficile	2.447	Bifidobacterium animalis	3.062
	8	Lactobacillus acidophilus	2.386	Lactobacillus crispatus	2.621
	9	Lactobacillus aviarius	2.020	Halomonas sp. 1513	2.284
	10	Lactobacillus agilis	1.932	Halomonas sp. JCM 19,032	2.042
Jejunum	1	Lactobacillus crispatus	36.478	Lactobacillus aviarius	36.706
	2	Bifidobacterium animalis	16.176	Bifidobacterium animalis	13.624
	3	Lactobacillus salivarius	12.653	Lactobacillus crispatus	9.631
	4	Lactobacillus aviarius	5.401	Lactobacillus reuteri	8.691
	5	Lactobacillus reuteri	4.211	Lactobacillus kitasatonis	7.094
	6	Lactobacillus kitasatonis	3.877	Lactobacillus acidophilus	5.600
	7	Lactobacillus acidophilus	3.733	Lactobacillus vaginalis	4.961
	8	Clostridioides difficile	2.631	Lactobacillus frumenti	1.192
	9	Lactobacillus johnsonii	1.335	Lactobacillus johnsonii	0.815
	10	Lactobacillus agilis	1.227	Lactobacillus pontis	0.670
Ileum	1	Lactobacillus crispatus	36.279	Bifidobacterium animalis	23.394
	2	Lactobacillus salivarius	23.668	Lactobacillus crispatus	18.823
	3	Bifidobacterium animalis	13.321	Lactobacillus kitasatonis	17.042
	4	Lactobacillus acidophilus	3.406	Lactobacillus aviarius	8.292
	5	Lactobacillus aviarius	3.290	Lactobacillus reuteri	6.923
	6	Escherichia coli	2.986	Lactobacillus vaginalis	5.449
	7	Lactobacillus reuteri	2.938	Lactobacillus acidophilus	3.185
	8	Lactobacillus kitasatonis	2.446	Lactobacillus johnsonii	3.040
	9	Ca Arthromitus sp. SFB-rat-Yit	2.215	Ca. Arthromitus sp. SFB-rat-Yit	2.839
	10	Lactobacillus agilis	1.650	Lactobacillus frumenti	0.947
Cecum	1	Faecalibacterium sp. An122	53.414	Faecalibacterium sp. An122	57.393
	2	Bifidobacterium gallinarum	7.796	Blautia sp. An81	8.425
	3	Bifidobacterium pullorum	4.714	Eubacterium sp. An11	3.297
	4	Barnesiella intestinihominis	3.920	Blautia hansenii	2.432
	5	Blautia sp. An81	3.288	Ruminococcus lactaris	2.247
	6	Lachnoclostridium sp. An76	2.352	Bacteroides fragilis	2.213
	7	Eubacterium sp. An11	2.136	Lactobacillus crispatus	2.061
	8	Bacteroides fragilis	2.122	Lachnoclostridium sp. An76	2.045
	9	Escherichia coli	1.937	Barnesiella intestinihominis	1.784
	10	Blautia hansenii	1.925	Pseudoflavonifractor sp An184	1.385

Median relative abundances (η_%) were used to determine the rank of each taxon in each subset

The relative abundances of several bacterial genera and species were significantly different with MRF supplementation (Tables 5 and 6, respectively). Notably, the bacterial genus Escherichia was significantly lower in the duodenum and ileum (numerically lower in jejunum and cecum, Additional file 1: Table S19). Genus Shigella was significantly lowered in the ileum, while the genus Bifidobacterium was significantly lowered in the duodenum and cecum. Whilst the genus Lactobacillus was noted to be significantly lower in the duodenum it was significantly greater in the cecum in MRF supplemented birds. Similarly, the genera Anerostipes, Kineothrix, and Blautia were noted to be significantly greater whilst Alistipes was significantly lower in the cecum of MRF supplemented birds when compared to the control. Genus Clostridioides was noted to be significantly lowered while other genera including Shewanella, Pseudomonas, and Halomonas were greater in the duodenum. Genera Streptococcus and Agarivorans were also significantly lower in the ileum of broilers supplemented with MRF. At the species level, the relative abundances of several bacteria were significantly different with MRF supplementation (Table 6). Of note, Escherichia coli and Clostridoides difficile were significantly lower across all three intestinum tenue sites following MRF supplementation. In the duodenum and jejunum, Bifidobacterium gallinarum was significantly lower, whereas Bifidobacterium gallinarum and Bifidobacterium pullorum were significantly lower in the cecum. Modulations in Lactobacillus species were observed throughout the GI tract following MRF supplementation. Of interest, L. reuteri, was observed to be significantly lower in the duodenum but significantly greater in the ileum and cecum and L. salivarius, was observed to be lower across the entire GI tract. The species Barnesiella intestihominis was noted to be significantly lower in the caeca of MRF-treated birds (compared to control birds), whereas Blauta sp. An81, which is strongly associated with weight gain, was observed to be significantly greater in both the cecum and jejunum. As mentioned above, Escherichia coli and Clostridoides difficile were observed to be significantly lower in the duodenum whereas Pseudomonas veronii, Halomonas axialensis, and Shewanella algae were significantly greater. After MRF-treatment, Shigella flexneri was observed to be significantly lower in the ileum.

Table 5.

Significantly altered (increased or decreased) genera observed at each anatomical site

Site	Genus	ηControl (n)	ηMRF (n)	PBD	Change	FC
Duodenum	Clostridioides	936.993	0	0.0002	Decrease	Eradication
	Escherichia	57.812	5.770	0.0011	Decrease	−0.900
	Bifidobacterium	8571.620	903.799	1.49e−08	Decrease	−0.895
	Lactobacillus	28,928.722	19,750.855	5.43e−08	Decrease	−0.317
	Shewanella	225.335	1742.348	2.03e−08	Increase	6.732
	Pseudomonas	1492.102	13,954.595	9.60e−09	Increase	8.352
	Halomonas	302.917	3176.654	8.67e−07	Increase	9.487
Ileum	Streptococcus	174.335	4.979	0.0191	Decrease	−0.971
	Escherichia	834.585	38.234	0.0080	Decrease	−0.954
	Shigella	105.048	6.101	0.0007	Decrease	−0.942
	Agarivorans	54.887	8.581	0.0279	Decrease	−0.844
Cecum	Alistipes	116.939	2.955	0.0145	Decrease	−0.975
	Bifidobacterium	6256.469	827.671	0.0001	Decrease	−0.868
	Oscillospiraceae[is]	340.433	220.868	0.0009	Decrease	−0.351
	Eubacterium	1017.654	1391.518	0.0419	Increase	0.367
	Ruminococcaceae[is]	26.154	36.030	0.0451	Increase	0.378
	Anaerostipes	137.596	234.771	0.0125	Increase	0.706
	Firmicutes[is]	91.309	164.287	4.96e−05	Increase	0.799
	Blautia	2184.069	4695.135	3.97e−05	Increase	1.150
	Kineothrix	8.866	20.484	9.85e−12	Increase	1.310
	Lactobacillus	860.654	3017.700	0.0027	Increase	2.506

Standardised median read counts (n) are presented to illustrate the magnitude of the fold change.^[is] represents incertae sedis classifications

Table 6.

Significantly altered (increased or decreased) species observed at each anatomical site

Site	Species	ηControl (n)	ηMRF (n)	PBD	Change	FC
Duodenum	Lactobacillus salivarius	21,043.166	773.255	0	Decrease	−0.9633
	Lactobacillus crispatus	26,636.067	2621.188	0	Decrease	−0.9016
	Lactobacillus johnsonii	1087.965	116.768	0	Decrease	−0.8927
	Lactobacillus paragasseri	153.621	19.226	0	Decrease	−0.8748
	Bifidobacterium animalis	21,393.561	3061.820	0	Decrease	−0.8569
	Escherichia coli	142.779	20.447	0.0045	Decrease	−0.8568
	Lactobacillus helveticus	37.155	6.869	0	Decrease	−0.8151
	Bifidobacterium gallinarum	84.895	16.604	8.04e−09	Decrease	−0.8044
	Lactobacillus reuteri	7873.725	1679.717	0	Decrease	−0.7867
	Gardnerella vaginalis	31.085	7.168	0	Decrease	−0.7694
	Lactobacillus gallinarum	186.486	50.006	7.70e−15	Decrease	−0.7318
	Agarivorans sp. Toyoura001	33.629	0	9.82e−05	Decrease	Eradication
	Ca. Paraburkholderia calva	15.977	0	0.0046	Decrease	Eradication
	Chlamydia trachomatis	3.859	0	0	Decrease	Eradication
	Clostridia sp UC5.1-1D1	22.984	0	0.0046	Decrease	Eradication
	Clostridioides difficile	2446.530	0	0	Decrease	Eradication
	Intestinibacter bartlettii	3.533	0	0.0449	Decrease	Eradication
	Lactobacillus agilis	1932.350	0	0	Decrease	Eradication
	Lactobacillus hominis	113.136	0	0	Decrease	Eradication
	Lactobacillus psittaci	23.226	0	0	Decrease	Eradication
	Lactobacillus taiwanensis	7.718	0	0	Decrease	Eradication
	Lactobacillus ultunensis	3.859	0	0	Decrease	Eradication
	Pseudoflavonifractor sp. An184	3.831	0	0.0449	Decrease	Eradication
	Streptococcus macedonicus	36.659	0	0.0216	Decrease	Eradication
	Halomonas beimenensis	3.859	13.737	0	Increase	2.5600
	Pseudomonas marginalis	7.803	43.309	0	Increase	4.5506
	Shewanella chilikensis	81.365	601.961	0	Increase	6.3982
	Halomonas axialensis	19.294	167.753	0	Increase	7.6944
	Halomonas sp. JCM 19,032	212.926	2042.185	0	Increase	8.5910
	Halomonas meridiana	174.590	1812.993	0	Increase	9.3843
	Halomonas sp. JCM 19,031	3.901	41.212	0	Increase	9.5639
	Shewanella algae	534.688	6009.597	0	Increase	10.2395
	Pseudomonas sp. KG01	11.339	130.232	0	Increase	10.4850
	Pseudomonas veronii	2765.991	34,906.046	0	Increase	11.6197
	Pseudomonas sp. TKP	1131.681	14,439.803	0	Increase	11.7596
	Halomonas stevensii	61.936	870.978	0	Increase	13.0625
	Halomonas sp. 1513	158.856	2283.586	0	Increase	13.3752
	Pseudomonas sp.	3.901	76.956	0	Increase	18.7259
	Halomonas chromatireducens	203.414	4045.677	0	Increase	18.8889
	Halomonas boliviensis	0	9.079	0	Increase	Introduction
Jejunum	Clostridioides difficile	2630.701	13.765	0	Decrease	−0.9948
	Lactobacillus salivarius	12,653.447	314.352	0	Decrease	−0.9752
	Lactobacillus paragasseri	928.751	69.853	0.0067	Decrease	−0.9248
	Lactobacillus helveticus	156.084	15.977	0	Decrease	−0.8976
	Lactobacillus crispatus	36,478.154	9630.854	3.35e−14	Decrease	−0.7360
	Lactobacillus gallinarum	390.921	206.510	0.0004	Decrease	−0.4717
	Agarivorans sp. Toyoura001	249.521	0	0.0003	Decrease	Eradication
	Bifidobacterium gallinarum	10.182	0	5.30e−05	Decrease	Eradication
	Chlamydia trachomatis	9.051	0	0	Decrease	Eradication
	Curtobacterium sp. PhB136	4.738	0	0	Decrease	Eradication
	Intestinibacter bartlettii	1.556	0	0.0046	Decrease	Eradication
	Lactobacillus psittaci	255.802	0	9.82e−05	Decrease	Eradication
	Streptococcus macedonicus	50.920	0	0	Decrease	Eradication
	Lactobacillus hamsteri	4.738	15.977	1.29e−08	Increase	2.3721
	Lactobacillus oris	120.946	547.410	0	Increase	3.5261
	Lactobacillus vaginalis	715.005	4961.468	1.23e−10	Increase	5.9391
	Lactobacillus coleohominis	17.771	152.119	0	Increase	7.5599
	Lactobacillus frumenti	44.772	1191.867	0	Increase	25.6207
	Blautia sp. An81	4.525	212.011	0	Increase	45.8504
	Corynebacterium nuruki	0	51.722	0.0225	Increase	Introduction
	Ruminococcus lactaris	0	22.171	0.0449	Increase	Introduction
Ileum	Lactobacillus agilis	1649.818	6.529	2.61e−07	Decrease	−0.9960
	Lactobacillus salivarius	23,668.295	214.878	0	Decrease	−0.9909
	Clostridioides difficile	571.044	6.340	0	Decrease	−0.9889
	Streptococcus macedonicus	593.510	7.163	0	Decrease	−0.9879
	Escherichia coli	2985.827	206.367	8.04e−09	Decrease	−0.9309
	Shigella flexneri	330.087	23.150	1.23e−10	Decrease	−0.9299
	Lactobacillus helveticus	232.948	54.550	3.06e−10	Decrease	−0.7658
	Agarivorans sp. Toyoura001	176.291	46.799	1.45e−09	Decrease	−0.7345
	Lactobacillus hominis	877.530	322.551	0.0131	Decrease	−0.6324
	Lactobacillus paragasseri	903.394	371.092	0.0013	Decrease	−0.5892
	Lactobacillus hamsteri	10.340	5.338	2.44e−05	Decrease	−0.4838
	Bifidobacterium pullorum	20.590	0	0.0449	Decrease	Eradication
	Curtobacterium sp. PhB136	5.170	0	0	Decrease	Eradication
	Sanguibacter keddieii	2.585	0	0	Decrease	Eradication
	Shigella dysenteriae	5.442	0	0.0449	Decrease	Eradication
	Lactobacillus johnsonii	1491.369	3040.351	1.23e−10	Increase	1.0386
	Lactobacillus reuteri	2938.173	6922.904	0	Increase	1.3562
	Lactobacillus oris	60.589	400.865	1.23e−10	Increase	5.6161
	Lactobacillus kitasatonis	2446.266	17,042.197	5.16e−07	Increase	5.9666
	Lactobacillus coleohominis	12.934	124.808	0	Increase	8.6492
	Lactobacillus vaginalis	344.435	5449.077	7.70e−14	Increase	14.8203
	Corynebacterium sp. J010B-136	5.442	90.274	2.23e−07	Increase	15.5880
	Lactobacillus frumenti	25.941	947.442	1.11e−06	Increase	35.5225
	Blautia hansenii	0	17.352	0	Increase	Introduction
	Corynebacterium provencense	0	6.529	0	Increase	Introduction
	Corynebacterium variabile	0	47.163	3.06e−10	Increase	Introduction
	Halomonas chromatireducens	0	17.328	0	Increase	Introduction
	Lactobacillus secaliphilus	0	5.784	0.0449	Increase	Introduction
	Lactobacillus taiwanensis	0	13.058	0	Increase	Introduction
	Ruminococcus sp. OM05-10BH	0	6.529	0	Increase	Introduction
	Streptococcus equi	0	18.831	0.0232	Increase	Introduction
Cecum	Bifidobacterium gallinarum	7795.660	583.668	0	Decrease	−0.9251
	Lactobacillus salivarius	383.137	33.494	4.24e−06	Decrease	−0.9126
	Alistipes putredinis	144.043	12.770	0	Decrease	−0.9113
	Bifidobacterium pullorum	4713.817	530.608	0	Decrease	−0.8874
	Streptococcus macedonicus	70.588	17.664	0.0068	Decrease	−0.7498
	Ruminococcus sp. N15.MGS-57	24.697	8.918	0.0112	Decrease	−0.6389
	Barnesiella intestinihominis	3919.930	1784.091	0.0006	Decrease	−0.5449
	Shigella dysenteriae	16.465	8.105	0	Decrease	−0.5077
	Ruminococcaceae sp. D16	16.911	8.640	9.70e−05	Decrease	−0.4891
	Lachnospiraceae sp. OF09-33XD	67.408	38.218	2.25e−05	Decrease	−0.4330
	Oscillospiraceae sp. VE202-24	962.770	580.671	7.70e−15	Decrease	−0.3969
	Flavonifractor sp. An100	25.766	16.747	0.0259	Decrease	−0.3500
	Blautia sp. aa 0143	41.155	28.604	5.41e−05	Decrease	−0.3050
	Bifidobacterium saeculare	79.899	0	0	Decrease	Eradication
	Bilophila wadsworthia	16.465	0	0	Decrease	Eradication
	Clostridium sp. M62/1	16.465	0	0	Decrease	Eradication
	Gordonibacter urolithinfaciens	8.232	0	0	Decrease	Eradication
	Staphylococcus cohnii	8.232	0	0	Decrease	Eradication
	Streptococcus gallolyticus	8.232	0	0	Decrease	Eradication
	Eubacterium sp. An11	2135.550	3297.052	1.64e−07	Increase	0.54388855
	Eubacterium ramulus	32.081	50.966	6.45e−05	Increase	0.5887
	Anaerostipes sp. 494a	364.093	595.732	0.0010	Increase	0.6362
	Ruminococcaceae sp. AM07-15	25.766	50.241	0.0072	Increase	0.9499
	Firmicutes sp AF16-15	133.334	276.899	0	Increase	1.0767
	Kineothrix alysoides	23.010	52.783	0	Increase	1.2939
	Blautia sp. An81	3288.129	8424.566	0	Increase	1.5621
	Ruminococcus sp. 1xD21-23	5.882	15.572	0	Increase	1.6472
	Ruminococcus sp. Zagget7	39.210	106.628	0	Increase	1.7194
	Lactobacillus gallinarum	20.581	66.358	2.61e−07	Increase	2.2243
	Blautia sp. KGMB01111	14.315	50.617	3.73e−05	Increase	2.5360
	Acutalibacter sp. 1XD8-33	9.421	33.494	1.23e−10	Increase	2.5552
	Lactobacillus crispatus	532.710	2060.556	0.0003	Increase	2.8681
	Lactobacillus johnsonii	20.581	96.311	0	Increase	3.6797
	Lactobacillus reuteri	80.809	506.430	0	Increase	5.2670
	Anaerostipes hadrus	8.589	54.266	0	Increase	5.3182
	Lactobacillus vaginalis	24.162	287.730	1.23e−10	Increase	10.9086
	Anaerofustis stercorihominis	0	16.210	0	Increase	Introduction
	Bacteroides sp. D22	0	23.989	0.0449	Increase	Introduction
	Blautia hominis	0	8.105	0	Increase	Introduction
	Blautia obeum	0	125.604	0	Increase	Introduction
	Blautia sp. An249	0	16.872	2.25e−05	Increase	Introduction
	Clostridium sp. AM29-11AC	0	76.449	0	Increase	Introduction
	Clostridium sp. OF09-36	0	19.109	0	Increase	Introduction
	Firmicutes bacterium AM29-6AC	0	8.105	0	Increase	Introduction
	Firmicutes bacterium AM41-5BH	0	15.572	0	Increase	Introduction
	Lachnoclostridium sp. SNUG30386	0	8.105	0	Increase	Introduction
	Lachnospiraceae bacterium OF09-6	0	8.105	0	Increase	Introduction
	Lactobacillus coleohominis	0	8.374	0.0449	Increase	Introduction
	Lactobacillus frumenti	0	7.786	0.0449	Increase	Introduction
	Lactobacillus helveticus	0	16.210	0.0068	Increase	Introduction
	Lactobacillus oris	0	109.678	0	Increase	Introduction
	Lactobacillus paragasseri	0	23.257	0	Increase	Introduction
	Lactobacillus psittaci	0	17.279	0	Increase	Introduction
	Ruminococcus sp. A254.MGS-108	0	5.403	0	Increase	Introduction
	Ruminococcus sp. AF17-22AC	0	22.142	0	Increase	Introduction

Standardised median read counts (n) are presented to illustrate the magnitude of the fold change

To investigate the gut microbial community in different GI tract sections analysis of the common and unique OTUs was conducted, shown in the Venn diagrams (Fig. 4). A total of just 22 OTUs were shared by all 4 chicken gut sections in both the control and MRF supplemented groups. The number of OTUs observed in only one chicken gut section varied from 1 to 84, with the jejunum having the least amount of unique OTUs in both control (2) and MRF (1) supplemented groups and the cecum having the greatest amount of unique OTUs in both control (66) and MRF (84) supplemented groups. Neighbouring GI tract sections shared very few common OTUs with duodenum-jejunum sharing 8 and 4 OTUs, jejunum-ileum sharing 4 and 9 OTUs and ileum-cecum sharing 2 and 4 OTUs in control and MRF supplemented groups, respectively.

Fig. 4.

a, b Venn diagram showing common and shared species-level OTUs within each GI tract section for both control and MRF supplemented broilers (99% sequence identity)

Effect of diet on cecal short chain fatty acids

Cecal propionate was significantly greater (η_FC = 0.176) and cecal butyrate was numerically greater (η_FC = 0.009; P_BD = 1) in MRF supplemented birds when compared to the control (Fig. 5). No significant statistical differences in the concentrations of cecal acetate or total SCFA concentrations were observed between the control and MRF supplemented birds (P_BD > 0.05).

Fig. 5.

Short-chain fatty acid (SCFA) concentration in broiler ceca. Statistical significance is denoted using an asterisk

Discussion

A large and diverse microbial community inhabits the broiler GI tract and contributes to overall health and growth efficiency by controlling pathogens, enhancing nutrient availability, and modulating immunological pathways (Borda-Molina, Seifert and Camarinha-Silva, 2018). Gastrointestinal microbiome composition and diversity is influenced by many external factors (eg. environment, age, breed, antibiotic use or dietary supplementation) which may yield beneficial or maleficial consequence [102]. In this study, the impact of MRF dietary supplementation on broiler GI tract microbiota (across the intestinum tenue and ceca) was explored. Supplemented birds were observed to finish one day earlier with higher average weight (5 g) and EPEF than their control counterparts (Table 1; indicating improved bird health and producer economic potential.

Bacterial species α-diversity indices of richness, diversity and evenness are scalable metrics of health status with higher diversity negatively correlated with dysbiosis [31, 52, 98]. Comparatively, β-diversity metrics are also measures of health, where low values are expected between samples and higher values are expected between treatment groups [26, 27]. Increased α-diversity and lower β-diversity in broilers can be achieved using pre- and probiotics, and such strategies positively correlate with improved FCR and feed efficiency [2, 46, 49, 94]. The results from this study agree with previous studies, whereby α- and β-diversity differ between anatomical site [25, 86, 101]. In particular, the cecum was observed to be most diverse, and the ileum to be least diverse of the four sites, and MRF impacted cecum α-diversity more than any intestinum tenue site (Fig. 1(a.-c.)). Despite the lack of intersectional paries, each section of the unidirectional intestinum tenue displays differential absorptive properties, yields dynamic environmental conditions (e.g. pH, water content, chemical profiles, and available O₂ content [60]) and microbial compositional profiles [65]. As the intestinum tenue maintains a continual flow, perhaps it is not surprising that α-diversity is less impacted than the cecum which displays a cul-de-sac architecture.

Abiotic stressors or infection can reduce α-diversity, leading to dysbiosis [23, 45]; broiler cecal α-diversity reduction typically coincides with reductions in Lactobacillaceae and an increase in Enterobacteriaceae [21, 39]. While MRF supplementation effect on the intestinum tenue has not been explored prior to this study, the observed cecal results (highlighting the dysbiotic amelioration effect of MRF via community composition alteration and increases in α-diversity) are in agreement with previously published cecal studies [26, 27]. Additionally, diversity metric trends between control group anatomical sites are also in agreement with previously published results [42, 101].

The major bacterial phyla identified in each of the four GI tract sections included Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria, with Firmicutes being most dominant throughout each section (Table 2). Bacteroidetes was lowly represented in the intestinum tenue and was found in most abundance in the cecum, mirroring observations in previous studies [18, 101]. The major bacterial genera across the intestinum tenue were Lactobacillus and Bifidobacterium, with Lactobacillus accounting for 48%-92% across these intestinal sections. Early studies [15, 34] also reported that the intestinum tenue microbiota was dominated by Lactobacillus and their conclusions have been independently confirmed using metagenomic analyses [18, 58]. Interestingly, the most abundant species within the intestinum tenue were distinct between control and MRF supplemented groups. Bifidobacterium animalis, Lactobacillus crispatus, and Lactobacillus salivarius dominated the control dataset throughout; comparatively, in the MRF-treated dataset, each intestinum tenue site had a distinct set of predominant species (Table 3). Through efficient carbohydrate fermentation, Lactobacillus are known to provide substantial aid to host metabolism, yielding improved feed conversion ratios and reduced mortality in broilers [76],Lactobacillus also deter pathogen adhesion to the lumen walls [61, 81]. Previous studies have shown that Lactobacillus can positively influence villus height (VH), crypt depth (CD) and VH:CD in broiler intestines [6, 58]. Increased VH and VH:CD are thought to provide a larger surface area and enhance ability of nutrient absorption [32].

Short-chain fatty acids (SCFAs) play an important role in gut physiology. Increased intestinal butyrate in broilers has been shown to have many positive effects including improved energy supply, intestinal villi development, microbiome modulation, anti-inflammatory properties, and enteric pathogen control [9]. In this study, the cecum was shown to be dominated by the bacterial families Ruminococcaceae, Lachnospiraceae, and Bifidobacteriaceae in the control group and Ruminococcaceae, Lachnospiraceae and Lactobacillaceae in the MRF supplemented group, with the genera Faecalibacterium, Bifidobacterium, Blautia, and Lactobacillus being most prominent. Cecal microbiota are generally dominated by strict anaerobes with many of these bacteria belonging to SCFA producing families Lachnospiraceae and Ruminococacceae [81]. The genus Faecalibacterium is a prominent butyrate producer and is correlated with enhanced epithelial health and reduced intestinal inflammation [69, 70, 100]. Prebiotic genera Bifidobacterium, Blautia and Lactobacillus also bioconvert complex carbohydrates to SFCA for host energy utilisation [14]. Increased SFCA concentration results in a lower gastrointestinal tract pH and de-conjugated bile acids, which aid in pathogen control [9, 63], 55]. While an insignificant butyrate increase (+ 0.95%) was observed post MRF-treatment, propionate (+ 21.41%) and SFCA producing Blautia were significantly increased in the cecum (+ 69%). These results corroborate previous suggestions that increased abundance of Blautia and Faecalibacterium abundances may be related to improved growth performance [103].

Potential foodborne pathogens Escherichia coli and Clostridioides difficile were significantly lower across the intestinum tenue and Shigella flexneri in the ileum. Mannan rich fraction binds type-1 fimbriae of Enterobacteraceae, and has been shown to lower the prevalence of these pathogens in the intestine of animals [1, 8, 41]. Reducing foodborne pathogens (from any source) promotes food chain integrity, with Escherichia and Clostridioides reported as being amongst the most concerning from a One Health perspective [82, 88]. Additionally, as these species are potentially toxicogenic, synthesised toxins may travel to distal sites of the host organism and remain in meat products postprocessing [5, 44, 68, 71]. As such, any reduction in their prevalence should be viewed as a positive outcome.

The probiotic Bifidobacterium spp. were also shown to be significantly lower in the jejunum, ileum, and cecum of MRF supplemented broilers and was noted previously in the broiler cecum [27]. An interesting result observed in this dataset was a significantly greater relative abundance of Lactobacillus reuteri in the ileum and cecum. When supplemented with L. reuteri, both mammalian and poultry models were observed to have considerably reduced Enterobacteriaceae, specifically Salmonella enterica, compared to non-supplemented controls [33, 97]. In addition to bacteriological protection, L. reuteri supplementation is observed to confer antiprotozoal activity against Eimeria spp. in turkeys [33] and against another Eimeriorinan (Apicomplexan) parasite, Cryptosporidium parvum, in immunodeficient mice [3]. In previous studies, L. reuteri was strongly associated with weight gain whereas L. salivarius was strongly associated with lean maintenance [33, 89, 97]. Interestingly, L. reuteri was increased and L. salivarius was decreased in MRF supplemented birds.

Dietary MRF supplementation was observed to yield significantly greater relative abundances of cecal bacterial genera from families Lachnospiraceae, Ruminococcaceae and Lactobacillaceae. Whilst these are typical of the main bacterial families found in the broiler cecum, modulating their abundances can have profound health impacts, such as reduced inflammation, reduced intestinal atrophy, and improved mucosal barrier function [66, 81]. The significantly higher relative abundances of probiotic genera Lactobacillus and Blautia in the cecum, alongside higher relative abundances of jejunal and ileal Lactobacillus indicate MRF prebiotic action [40]. In essence, the comprehensive impact of prebiotics have important host health benefits beyond that of simple microbiota modulation.

Conclusion

This manuscript aimed to address the bird-to-bird (intersample) variation associated with microbiome studies and is the first to apply such corrections to a comparative supplementation study across intestinal geographies. Each GI tract section presented a distinct bacterial community composition which were altered as a result of MRF supplementation. Results from the present study indicated that Lactobacillus was the most abundant genus in the intentinum tenue and that the cecum was most bacterially divergent. Birds supplemented with MRF had significantly higher species richness in the cecum and significantly different bacterial community composition in each GI tract section. MRF supplemented birds had lower levels of the zoonotic pathogens Escherichia, Clostridioides, and Shigella which are of particular importance for food chain integrity. Higher levels of probiotic related bacteria, such as Lactobacillus and Blautia, were observed following MRF supplementation. Higher relative abundances of known SCFA producing bacteria (and SCFA concentrations) were also attributed to MRF supplementation. These bacterial and metabolite alterations highlight a protective role for dietary MRF inclusion to support broiler GI health and may allow safer meat to be produced.

Abbreviations

16S rRNA

16 Svedbard ribosomal ribonucleic acid

2D/3D

2 Dimensional/3 dimensional

ESS

Escherichia–Salmonella–Shigella

CC

Closure constant

IgA

Immunoglobulin A

n_x

Number/count of x

PCA

Principal component analysis

PCoA

Principal coordinate analysis

PERMANOVA

Permutational analysis of variance

TR

Transformed reads (SI data)

TA

Relative abundance from transformed reads (SI data)

SI

Supplementary information

SFCA

Short chain fatty acid

Subsp.

Subspecies

v.

Version

List of symbols

Δ

Difference

μ

Mean

σ

Standard deviation

σ²

Variance

η

Median

 ~ 

Approximal to

≁

Not approximal to

BD

Bonferroni–Dunn

FC

Fold change

H₀

Null hypothesis

H_A

Alternative hypothesis

i_n

Number of iterations

N(μ,σ²)

Normal (Gaussian) distribution

P

P-value

P_BD

Bonferroni–Dunn corrected P-value

X

Sample distribution

Author contributions

RL performed all data scientific analyses, statistical analyses, and image processing. AC coordinated 16S rRNA sequencing and other laboratory experiments, RM and FW provided project direction. All authors wrote and reviewed the final manuscript.

Funding

This study was funded by Alltech.

Availability of data and materials

Data used for this study is available at https://github.com/RobLeighBioinformatics/Broiler_GI_microbiome. Sequence reads (fastQ files) will be deposited at NCBI SRA upon publication.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have reviewed and consented to the publication of this manuscript.

Competing interests

RL was in receipt of a Postdoctoral Fellowship from Alltech during the course of this study. AC and RM also received salaries from Alltech during the course of this study. Alltech is a manufacturer and supplier of animal supplementary products.