Effect of an antibiotic and a probiotic on phage communities in the swine gut microbiota

Abstract

Objective

The impact of dietary treatments on the phage community of porcine intestinal microbiota is not well understood. An antibiotic (tylvalosin), a probiotic (Pediococcus acidilactici), and a combination of these were given to six cannulated pigs in a double crossover design study. Samples of ileal digesta and feces were collected and whole genome shotgun sequencing was performed. The variations in phage and bacterial communities were compared for each treatment and sample type.

Results

The bacteriophages present in the gut microbiome exhibited greater variations in both α- and β-diversity between sample types (digesta, feces) than between treatments. β-diversity and differential abundance showed that the effect of the combined antibiotic and probiotic treatment was the same as with the antibiotic alone. However, the effects of the probiotic and antibiotic treatments were statistically significantly different in the fecal samples. β-diversity was different in those two treatments, and differential abundance analysis identified multiple phages as markers for each treatment. No significant variations in relative abundance were found in phage lifestyle (i.e., virulent, temperate) between treatments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-025-07484-w.

Introduction

Bacteriophages, also called phages, are highly diverse viruses that infect bacteria. They are extremely abundant, with an estimated 10³¹ present on Earth [1]. Our understanding of phage genomic characteristics and variations is being furthered by metagenomic datasets and studies, which are revealing thousands of new phage genes and proteins [2].

As bacterial predators, phages create variations in bacterial populations [3]. Bacteria become resistant to phage infections through many strategies, including modifying the structure of the receptors used by phages, using the CRISPR-Cas system [4], and exhibiting higher mutation rates [5]. Phages have direct genomic interactions with bacteria through lysogenic conversion, transduction, and prophage integration [6, 7]. These interactions, as well as the prey–predator dynamic, affect both bacterial and phage evolution.

The gut microbiota of mammals contains mainly lysogenic bacteria, which can use genes from prophages originating from temperate phages [8]. Studies on the mammalian gut show evidence of frequent phage–bacteria interactions [9, 10] and the spread of virulence and antibiotic resistance genes by phages [11]; this results in both bacteria and phages developing resistance and counter-resistance mechanisms against each other [12].

Over the past decades, the swine gut microbiota has gained significant research attention, driven by the importance of pork as a key protein source for human consumption [13] and the numerous implications of gut microbiota for pork production. Research can provide a better understanding of the effect of different feeds [14], supplements, antibiotics [15], and various antimicrobial strategies [16] (e.g., probiotics) on gut microbiota [17–19]. Furthermore, differences in gut microbiota have been linked to better pig growth [20] and higher meat quality [21].

Swine are host to more than 20 bacterial species that are shared across most individuals; the most common genera are Prevotella, Ruminococcus, Lactobacillus, and Clostridium [22]. Hu et al. [23] characterized the gut phageome (i.e., the phage communities) of multiple pig breeds, and identified Ackermannviridae, Straboviridae, Peduoviridae, Zierdtviridae, Herelleviridae, and Mesyanzhinovviridae as the main families.

In this study, pigs were given an antibiotic (tylvalosin), a commercial probiotic (Pediococcus acidilactici), or a combination of the two in a crossover experimental design, with a 3-week resting period between each treatment where the pigs received a control diet without supplements. Samples of ileal digesta and feces were collected, and metagenomic sequencing was performed [24]. The results from the bacterial analysis are presented in Monger et al. [24]; this current study focused on the phages present in the same samples. The phage communities were characterized through bioinformatics tools, and were analyzed on their own and compared with the results from the previous bacterial analysis.

Methods

Sample collection, processing, and sequencing

As described in Monger et al. [24], six Yorkshire-Landrace male pigs, each with an ileal-T canula, received three treatments in a double crossover design: antibiotic (250 g/ton of feed; Aivlosin^® containing 17% tylvalosin; ECO Animal Health Princeton, NJ, USA), a probiotic (Pediococcus acidilactici MA18/5 M [PA]; 10⁸ CFU/day; Biopower^® PA, Lallemand Animal Nutrition, Milwaukee, WI, USA), and a combination of the antibiotic and the probiotic. Each pig received the treatments in a different order, with resting periods (receiving a control diet without supplements) after canulation and after each treatment (Additional figure S1). The resting and the treatment periods were 3 weeks long. The experimental design was approved by the Animal Protection Committee of Université Laval prior to the experiments (2019057–1) in accordance with the guidelines established by the Canadian Council on Animal Care. At the end of each treatment and resting period, samples of digesta and feces were collected from each pig. The samples were stabilized with the PERFORMAbiome-GUT PB200 sampling kit (DNAgenotek, Ottawa, Ontario, Canada) and the DNA was extracted with the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Toronto, Canada). Shotgun sequencing libraries were generated from 50 ng of gDNA using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England BioLabs, Whitby, Canada) as per the manufacturer’s recommendations. Sequencing was performed with the Illumina NovaSeq 6000 by the Genome Quebec Centre of Expertise and Services (Montréal, Canada). Each sample had an average of 73 M paired-reads after cleaning. The reads were filtered and cleaned, then co-assembled with the SqueezeMeta pipeline version 1.5.1 [25].

Bacteriophage detection and characterization

The co-assembly performed by SqueezeMeta was used as the input for VirSorter2 version 2.2.3 [26] and CheckV version 1.0.1 [27], following the Sullivan lab’s standard protocol for viral sequence identification [28]. After quality assessment by CheckV, viral sequences were rejected from further analyses if their overall quality score was < 50%. Sequences identified as prophages were extracted from other phage sequences, and all subsequent analyses were performed separately. Phage taxonomic identification was performed with PhaGCN2 version 2.1 [29], and their bacterial hosts were predicted with CHERRY version 1.0 [30]. Phage lifestyle (virulent or temperate) was predicted with BACPHLIP version 1.0 [31]. ABRicate version 1.0.0 [32] was used to detect antibiotic resistance genes.

Statistical analysis

Statistical analyses were performed using R Statistical Software (version 4.3.0) and the GraphPad Prism software version 10.3.1. The α-diversity was calculated with the vegan R package version 2.6.4 [33]. Normality was assessed using a Kolmogorov–Smirnov test with the ks.test function. Because the data were not normally distributed, differences between groups were tested using the Kruskal–Wallis test, followed by Dunn’s post-hoc pairwise comparisons with Bonferroni correction to account for multiple testing. β-diversity was determined with phyloseq version 1.38.0 [34] and microbiomeMarker version 1.6.0 [35], with PERMANOVA tests performed through the function pairwise.adonis (Benjamini-Hochberg procedure). The differential abundance was used to identify metagenomic biomarkers for each treatment with MaAsLin2 version 1.14.1 [36], with p-values adjusted for multiple testing using the Benjamini–Hochberg procedure. Biomarkers for the antibiotic and probiotic groups were compared with the bacterial metagenomics data to identify significant similarity scores with the ccrepe R package version 1.30.0 [37]. Only correlations with a similarity score < − 0.50 or > 0.50, and a corrected p-value < 0.01 were considered significant.

Results

The aim of this study was to investigate the impact of an antibiotic, a probiotic, and a combination of these on the phage population in the swine gut microbiome. A shotgun metagenomics dataset from a previous study that assessed the impact of these treatments on the bacterial community was used [24]. The dataset contains 36 samples of feces (6 antibiotic treatments, 6 probiotic treatments, 6 combined treatments, 18 recovery periods) and 35 samples of ileal digesta (6 antibiotic treatments, 6 probiotic treatments, 6 combined treatments, 17 recovery periods). The digesta from pig number two at the Ctl2 period could not be collected as there was no digesta at the time of sampling.

From a total of 9,444,776 contigs, 24,215 were identified as putatively viral (0.2564%). Only contigs of medium, high, or complete quality (according to Virsorter2 criteria; 1,757 contigs) were used in the analyses. Of these 1,757 contigs, 214 were proviruses (viral sequences inside bacterial sequences) and 1,543 were viral. Of 1,543 phage sequences, 953 and 590 sequences were predicted to be from lytic and temperate phages, respectively. The potential fluctuations in lytic and temperate phage populations caused by the treatments were also investigated (Additional figure S2). For each phage sequence, the transcripts per million (TPM) value from the previous recovery period was subtracted from the value calculated for each treatment period to isolate the effect of the treatment. No significant differences were found.

The α-diversity, measured using the Shannon and Simpson diversity indexes, was evaluated for all samples. There were no significant differences between treatments for any of these indices (Fig. 1A-D). However, the digesta samples had significantly lower α-diversity than the feces samples (Fig. 1E-F, Shannon: p = 10^− 7; Simpson: p = 0.00075).

Fig. 1.

Comparison of α-diversity, represented by the Shannon index and the Simpson index, between treatments in the digesta (A, C), in feces (B, D), and between sample types (E, F)

For the digesta samples, β-diversity values were significantly different only between samples from animals receiving the antibiotic treatment and those collected at the beginning of the experiment (p = 0.0483; Fig. 2A; Additional file 1). However, fecal samples from treated pigs differed significantly (p < 0.05) in all treatments when compared with the initial samples (Fig. 2B; Additional file 1). Feces from animals treated with probiotics had different β-diversity values than feces from animals receiving the antibiotic treatment (p = 0.0483) or the combined treatment (p = 0.0483). However, no significant differences were observed between samples from animals treated with the antibiotic alone and those receiving the combination treatment (p = 0.9740). Additionally, no significant phage markers were identified for these two treatments. Because no differences were observed between the antibiotic and combined treatments, the combined treatment was not considered in further analyses. However, the fecal and digesta samples did have significantly different phage populations (p = 0.0013).

Fig. 2.

Principal coordinates analysis (PCoA) plots representing b-diversity using Bray–Curtis distance, comparing treatments in digesta (A) and fecal (B) samples, and comparing the digesta and feces (C)

Phages were significantly amplified in feces from animals treated with probiotics or antibiotic (Table 1), but not in digesta. A total of 5 phages were amplified in animals treated with the antibiotic and 5 were amplified from animals treated with the probiotic. When grouping phages by their predicted bacterial host, two phages infecting Escherichia coli and two infecting Clostridium sp. were amplified in the antibiotic group, whereas none were amplified in the probiotic group. In contrast, two marker phages infecting Vibrio cholerae and one infecting Burkholderia thailandensis were identified in probiotic-treated animals, with none detected in the antibiotic-treated group. Additionally, each group had a phage predicted to infect Glaesserella parasuis.

Table 1.

Marker phages in fecal samples from antibiotic and probiotic treatments

ID	Host taxonomy	Phage lifestyle	Phage taxonomy	Coefa	Stderr	P-value	Q-value
3,550,650	Clostridium perfringens	Virulent	N/Ab	-5,312	0,977	0,0002870	0,06551
3,880,130	Glaesserella parasuis	Virulent	N/A	-3,588	0,416	0,000006067	0,00554
5,643,192	Escherichia coli	Virulent	Peduoviridae	-2,650	0,635	0,0019076	0,19352
3,132,888	Clostridium botulinum	Temperate	Tybeckvirinae_like	-2,072	0,303	0,00004539	0,02072
1,918,879	Escherichia coli	Temperate	Tybeckvirinae_like	-1,750	0,441	0,002674	0,24413
4,660,000	N/A	Virulent	N/A	1,137	0,257	0,001299	0,14825
5,740,980	Burkholderia thailandensis	Temperate	Wizardvirus	1,303	0,292	0,001221	0,14825
4,505,317	Vibrio cholerae	Virulent	Zobellviridae	2,876	0,591	0,0006561	0,09984
4,414,452	Glaesserella parasuis	Temperate	Wizardvirus	3,135	0,609	0,0004346	0,07936
566,151	Vibrio cholerae	Virulent	Suoliviridae	4,319	0,676	0,0000793	0,02413

a. A negative coefficient value indicates amplification in antibiotic-treated animals, while a positive value indicates amplification in probiotic-treated animals

b. N/A means not available

The correlation between the relative abundance of marker phages and the relative abundance of bacteria [24] was assessed (Additional file 2, Additional figure S3). In feces, 8 out of 10 phage markers correlated with at least one bacterium, 3 correlated with at least 100 bacteria, and 1 phage marker, predicted to infect Clostridium perfringens, correlated with more than 200 bacteria. Phages 3,550,650 and 4,660,000 showed no significant correlations. More than 760 bacteria had at least one significant correlation with a phage marker.

Prophages detected by VirSorter2 and verified by CheckV were investigated to obtain a fuller picture of the microbiota. A total of 215 proviruses were detected, with an average length of 70,979 bp. The taxonomic identification performed by SqueezeMeta identified 133 (61.86%) present in Firmicutes, and 81 (37.67%) in Clostridia (Additional file 3). No antibiotic resistance genes were found in the prophages.

Discussion

The goal of this study was to investigate the phage response in swine gut microbiota to an antibiotic, a probiotic, or a combined treatment. We compared these results with those of a previous study of bacteria in the same metagenomic dataset [24], providing a more complete understanding of the swine gut microbiota.

α-diversity, β-diversity values and differential abundance analyses showed that the antibiotic tylvalosin had the largest impact on the bacteriophage composition of the gut microbiome. Similar to observations in the previous bacterial study [24], adding probiotic to the antibiotic did not significantly change the bacteriophage composition of the gut microbiome. This was also indicated by the lack of markers for the antibiotic and combined treatments and a complete overlap in the PCoA results.

The phage communities were differently affected by the treatments based on their location in the digestive tract. The β-diversity values in the digesta were similar in all treatments, while the α-diversity and the number of markers were lower in the digesta than in the feces. These differences could be attributed to the more rapid passage of material through the small intestine than through the large intestine [38]. The significant difference of the phage communities in digesta and feces demonstrates that these sampling locations are not equivalent when investigating the phage community. This is supported by previous studies showing that the microbiota and environmental conditions change throughout the digestive tract [14, 39].

Overall, the phage community results were similar, but not identical, to the bacterial community results obtained from the same metagenomic dataset [24]. For example, the bacterial hosts of the phages identified as markers in the antibiotic and probiotic treatments of the fecal samples did not match the bacterial markers from the same samples. This highlights the complexity of bacteria-phage interactions and coevolution, as well as the need for further research.

Limitations

This animal study included a small sample size of six pigs because of physiological and financial constraints, as well as limitations on the number of surgeries to install T-cannulas that can be performed in a single day. Additionally, the viral identification tools relied heavily on publicly available databases, which often lack annotations for most phages, which reduces their precision and reliability. .

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CFU

Colony forming unit

PERMANOVA

Permutational multivariate analysis of variance

PCoA

Principal coordinates analysis

Author contributions

EL: Writing–original draft, Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Writing–review and editing. XCM: Conceptualization, Writing–original draft, Writing–review and editing. LS: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Writing–original draft, Writing–review and editing, Funding acquisition, Project administration, Resources, Supervision, Validation. SJC: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing. FG: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing. EP: Conceptualization, Writing–original draft, Writing–review and editing. SF: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing. ATV: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing.

Data availability

The datasets analysed during the current study are available in the NCBI Sequence Read Archive database under the BioProject ID PRJNA1049315.

Declarations

Competing interests

Authors EP and SF were employed by Olymel S.E.C./L.P. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.