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Effects of Lactiplantibacillus plantarum HNU082 intervention on fungi and bacteriophages in different intestinal segments of mice

BMC Microbiology volume 25, Article number: 69 (2025) Cite this article

Abstract

Background

Gut fungi and bacteriophages, as members of the gut microbiota, can affect the interactions between gut bacteria and the host, participate in host metabolism, and are associated with various diseases. Probiotics substantially influence gut fungi and bacteriophages, modulating their composition through both direct and indirect mechanisms, thereby influencing host health. Current research primarily focuses on the effects of probiotics on the intestinal bacterial community. However, the alterations in the compositions of gut fungi and bacteriophages following probiotic intervention are not yet fully understood. Therefore, this study used Lactiplantibacillus plantarum HNU082 (Lp082) as the research subject and aimed to investigate the changes of the gut fungi and bacteriophages in the small intestine and the large intestine after the gavage of Lp082.

Results

After probiotics entered the gut, the changes of the gut fungi and bacteriophages caused by the probiotics were more pronounced in the small intestine compared to the large intestine. The relative abundance of pathogenic fungi, such as Candida albicans, decreased in the small intestine. Furthermore, a strong positive correlation between the relative abundance of bacteriophages and their host bacteria in the gut was observed. The relative abundance of both Clostridia class bacteria and their bacteriophages increased.

Conclusions

In summary, the effects of probiotics on gut fungi and bacteriophages differed between the small intestine and the large intestine. This study contributed to a better understanding of the impact of probiotics on gut fungi and bacteriophages and provided data support for the association and dynamic changes between gut bacteria and their bacteriophages.

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Background

The gut microbiota plays an essential role in host health and is associated with various diseases [1]. The gut microbiota participates in many metabolic processes in the body and can affect host health by affecting metabolic regulation [2]. In addition to gut bacteria, fungi, and bacteriophages are also important components of gut microbiota. The gut contains a large number of fungi [3], primarily comprising genera such as Candida and Saccharomyces, and also includes a substantial number of fungi commonly found in food [4]. The gut fungi also contain several mold species, such as Aspergillus [5]. The gut fungi can influence the metabolic interactions between the host and the gut microbiota. Thermomyces and Saccharomyces were strongly associated with metabolic disturbance and weight gain [6]. Candida albicans, Candida lusitaniae, Kluyveromyces marxianus, and Cyberlindnera jadinii were all higher in pediatric patients with Crohn's disease than healthy people [7]. Besides, the gut fungus Candida parapsilosis was also identified as a critical commensal fungus related to diet-induced obesity [8]. Bacteriophages specifically infect, coexist, and coevolve with bacteria. The diversity of bacteriophages in the gut is extensive, with notable families including Myoviridae, Siphoviridae, and Podoviridae [9]. Bacteriophages not only influence the composition and evolution of the microbiota but also affect the interactions between bacteria and the host, thereby impacting host health [10]. A study evidenced that an increase in the abundance of Caudovirales bacteriophages was associated with Crohn's disease and ulcerative colitis [11]. Also, specific bacteriophages in the gut were associated with the presence and progression of colorectal cancer. Bacteriophages could also function as microbial biomarkers for colorectal cancer, making them valuable indicators for early detection, risk assessment, and monitoring of the disease [12]. Additionally, bacteriophages that infect pathogenic bacteria could also help the human body resist infections by these pathogenic bacteria [13]. Thus, analyzing fungi and bacteriophages is crucial for understanding the composition and function of the gut microbiota and for developing new therapeutic strategies.

Probiotics are a group of living microorganisms. When administered in adequate amounts, probiotics confer a health benefit on the host [14]. Probiotics can exert their effects by modulating the gut microbiota in several ways. First, probiotics can promote the growth of associated bacterial communities, thereby increasing the abundance of beneficial bacteria in the gut and improving the overall structure of the host's gut microbiota. After entering the human intestine, probiotic Lacticaseibacillus casei Zhang increased the abundance of other beneficial bacteria, such as Roseburia, Coprococcus, and Eubacterium rectale, which could have health benefits for healthy adults [15]. Gavage administration of Lactiplantibacillus plantarum HNU082 (Lp082) significantly increased the abundance of Bifidobacterium, Akkermansia, and Faecalibacterium in the mice gut, thereby improving lipid profiles and preventing hyperlipidemia [16]. Secondly, probiotics can reduce the abundance of pathogenic bacteria through competitive exclusion. After Bifidobacterium lactis V9 entered the gut of patients with polycystic ovary syndrome, the abundance of several bacterial genera decreased, including Collinsella, Coprococcus, Klebsiella, Clostridium, Actinomyces, Streptococcus, Eubacterium, and Ochrobactrum [17]. Additionally, probiotics can regulate the gut environment by secreting metabolic products. Probiotics produce short-chain fatty acids (SCFAs), which can lower the pH of the gut environment, inhibiting the growth of certain pH-sensitive gram-negative bacteria [18]. In summary, probiotics can modulate the gut microbiota and then influence host health. However, most studies focused on the impact of probiotics on the abundance of bacteria in the gut. There was less research on the effects of probiotics on fungi and bacteriophages in the gut.

The ecological and physiological differences between the small intestine and the large intestine influence the composition and function of the local microbiota and impact the interaction between the local microbiota and the host, as well as the colonization potential of probiotics [19]. Chemical and nutritional gradients, along with the presence of antimicrobial peptides and physical characteristics of the gut, contributed to the differences in gut microbiota composition at different segments of the intestine [20]. There were differences in the interactions between probiotics and the gut microbiota across different regions of the intestine, and these differences cannot be distinguished by the presence of probiotics in feces [21]. Moreover, the response of gut microbiota to probiotics may be different at different time points [22]. However, there is less research on how the gut fungi and bacteriophages respond in different intestinal regions and at different time points after probiotics gavage.

Therefore, this study used Lp082 as the representative strain. After the gavage of Lp082, intestinal contents from different segments at different time points were collected, and shotgun metagenomic sequencing was performed. By analyzing the relative abundance of gut fungi and bacteriophages in the small intestine and the large intestine after the gavage of Lp082, the dynamic changes in the composition of the gut fungi and bacteriophages were investigated following the intake of Lp082. This study will help to further understand the interactions between probiotics and gut fungi and bacteriophages, providing theoretical support for future research on the relationship between probiotics and human health.

Methods

The study subjects and experimental design

Specific-pathogen-free (SPF) C57BL/6 J male mice were used in this study. These mice were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. at seven weeks old, weighing approximately 20 g each. The mice were housed at a temperature of 22 ± 2 °C. They were kept under a 12-h light/12-h dark cycle. The housing facility had 15 air changes per hour. Corncob bedding was used and changed every four days. The diet comprised standard chow, which was provided ad libitum. The standard chow fed to the mice consists of corn, wheat, imported fish meal, chicken meal, soybean meal, soybean oil, feed-grade sodium chloride, dicalcium phosphate, choline chloride, methionine, and a premix of trace elements and vitamins. Sterile water was also provided ad libitum.

The Lp082 used in this study was isolated from Yucha, a traditional fermented food from Hainan Province, China [23]. Previous studies on Lp082 have found that it has the potential to prevent hyperlipidemia and other conditions, indicating promising application prospects [16]. Before the experiment, the Lp082 culture was centrifuged at 3000 × g for 5 min to obtain the bacteria. After one week of adaptive feeding, the mice were randomly divided into the control group (Con, n = 118) and the Treatment group (Tre, n = 18). The Tre group mice were given a single gavage with 200 μL of Lp082 bacterial suspension containing 1010 CFU for one time, while the Con group mice were given a single gavage with 200 μL of 0.85% sterile saline [24, 25]. In previous studies, probiotics have been found to survive in the gut for more than seven days [26].On the first day (D1), the third day (D3), and the seventh day (D7) after gavage, six mice from each group were randomly selected at each time point. These mice were anaesthetized via intraperitoneal injection (i.p.) with tribromoethanol at a dose of approximately 240 mg/kg body weight, and then euthanized by cervical dislocation [27]. This procedure complies with the requirements of the AVMA Guidelines for the Euthanasia of Animals: 2020 Edition [28]. The contents of the small intestine and the large intestine were collected separately and were rapidly frozen in liquid nitrogen for metagenomic sequencing (Fig. 1a).

Fig. 1
figure 1

Initial Distribution of the Gut Fungi. a Schematic diagram of the animal experimental procedure. b The relative abundance of fungi at the genus level in the gut, displaying only the top 14 genera in terms of relative abundance. c Relative abundance of fungi at the species level in the gut, showing only the top 14 species in terms of relative abundance. d Distribution of fungi at the species level in the small intestine and the large intestine. The left panel shows the distribution in the small intestine, and the right panel shows the distribution in the large intestine, displaying only the top 14 species in terms of relative abundance. e,f α-diversity of fungi in the small intestine and the large intestine. The (e) panel shows the Shannon index, and the (f) panel shows the Simpson index. The green box represents the Shannon index in the small intestine, and the blue box represents the Simpson index in the large intestine. g PCoA of the large intestine and the small intestine fungal community structure based on Bray–Curtis dissimilarity. h Heatmap showing the relative abundance changes of fungi with significant differences (P < 0.05) between the small intestine and the large intestine. The darker colors indicate higher relative abundance. A total of 26 fungal species showed significant differences

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Shotgun metagenomic sequencing

The contents of the large intestine and the small intestine were performed shotgun metagenomic sequencing by Novogene Co., Ltd. The QIAamp Fast DNA Stool Mini Kit (Qiagen, cat. no. 51604) was used to extract DNA from the samples. The purity and concentration of the extracted DNA samples were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA), and the sequencing was performed using the Illumina HiSeq 2500 sequencing platform. The raw sequencing data consisted of paired-end DNA fragments of approximately 150 bp each, forming a sequencing library with read lengths of about 300 bp. Next, the sequencing data were quality-controlled using FastQC software [29], and host DNA fragments were removed by aligning the sequences to the host genome. The remaining sequences were used for further analysis.

The annotation of fungi and bacteriophage species

First, MEGAHIT was utilized to assemble the paired-end reads, generating preliminary genome drafts [30]. Then, Bowtie2 (v.2.1.0) software was used to annotate the abundance of fungi and bacteriophages [31]. For fungal annotation, the EU-Detect algorithm was employed to build a library as the reference genome database [32]. For bacteriophages annotation, the Gut Bacteriophages Database (GPD) was used to build the library [33]. During the alignment process, Bowtie2 software was used to map the metagenomic reads against the reference genome database. The alignment resulted in SAM (Sequence Alignment/Map) format files, which meticulously document the alignment positions and relevant information of each read on the reference genome. We then used Samtools software to convert the SAM files into BAM (Binary Alignment/Map) format, which facilitates efficient subsequent analysis and processing [34]. The BAM files were employed to calculate the coverage using the ‘jgi summarize bam contig depths’ script from MetaBAT2 (v.2.12.1) [35]. Based on this coverage data, we further utilized MetaBAT2 for contig binning, clustering contigs with similar composition and coverage together, thereby obtaining higher-quality genome assembly results. The identified reads belonging to the same fungal species were combined to determine the relative abundance of each fungal species. The identified bacteriophage sequences were then compared with bacteriophage-host information to obtain their corresponding host information. The identification of the fungal and bacteriophage genomes was completed. Finally, Kraken2 software was used to identify bacterial species in the samples [36], and Bracken software was employed to calculate their relative abundance [37].

Statistical analysis of health information

Statistical analysis was performed using GraphPad Prism, R, and Cytoscape software [38,39,40]. The line plot and the bar chart were plotted using GraphPad Prism software [38]. Creating a species stack plot using the “reshape2”, “colortools”, and “ggpubr” packages in R software [41,42,43]. Calculating α-diversity using the “vegan” package in R software [43]. Box plot was made using the “ggplot2”, “ggsignif”, and “ggpubr” packages in R software [42, 44, 45]. Calculation of Bray–Curtis dissimilarity using the “vegan” package in R software [43]. Scatter plots were created using the “ggplot2” package in R software [44]. Heatmaps were generated using the “pheatmap” and “RColorBrewer” packages within R software [46, 47]. Fitting curves were plotted using the “ggplot2” package in R software [44]. Linear regression and plotting were conducted using the “ggplot2” and “ggpubr” packages in R software [42, 44]. Kernel density plots were created using the “ggplot2”, “ggridges”, and “RColorBrewer” packages in R software [44, 47, 48]. The Venn diagram was created using the BioLadder website. The co-occurrence network was constructed using Cytoscape software [40]. The schematic diagram of the animal experimental procedure was created using the BioRender website [49]. Differences among multiple groups were analyzed using the Kruskal–Wallis test, and pairwise comparisons between groups were conducted using Tukey's Test. Comparisons between the two groups were performed using the Wilcoxon Rank-Sum Test. The Adonis analysis was performed to assess the differences in gut microbiota composition between the two samples. Spearman's rank correlation coefficients were calculated using the corPvalueStudent function from the “WGCNA” packages in R software [50].

Results

Initial distribution of the gut fungi in the small intestine and the large intestine of the mice

To investigate the structural changes in gut fungi and gut bacteriophages following the gavage of Lp082, shotgun metagenomic sequencing of intestinal contents on Days 1, 3, and 7 was performed. The relative abundance of fungi in the intestinal contents of the Con group on D1 was analyzed to establish the initial distribution of gut fungi. First, the relative abundance of fungal genera in the gut was calculated, and the composition and distribution of the gut fungi at the genus level was visualized (Fig. 1b). The gut fungi mainly consisted of Aspergillus, Pyricularia, Pochonia, and Saccharomyces. This predominance of Aspergillus may be associated with the mice's diet, which is primarily composed of corn and other plant-based materials [51]. The relative abundance of fungal species in the gut was counted (Fig. 1c). At the species level, the four fungal species with higher relative abundance were Aspergillus oryzae RIB40, Pochonia chlamydosporia 170, Saccharomyces eubayanus strain FM1318, and Pyricularia oryzae 70–15. These species, respectively, belonged to Aspergillus, Pochonia, Saccharomyces, and Pyricularia, which were consistent with the identification of the four dominant fungal genera. The distribution of fungal species in the small intestine and the large intestine was separately analyzed (Fig. 1d). The fungal communities in both the small intestine and the large intestine were mainly composed of Aspergillus oryzae RIB40, Pyricularia grisea strain NI907, and Saccharomyces eubayanus strain FM1318. However, the relative abundance of Saccharomyces eubayanus strain FM1318 in the large intestine was significantly higher than that in the small intestine.

The Shannon index and Simpson index of gut fungi in the small intestine and the large intestine were calculated (Fig. 1e and f), and Principal Coordinates Analysis (PCoA) based on Bray–Curtis dissimilarity was conducted (Fig. 1g). The differences in diversity between gut fungi in the small intestine and the large intestine were not significant. Fungal species with significantly different relative abundance between the small intestine and the large intestine were identified (P < 0.05) (Fig. 1h). A total of 26 fungal species showed significant differences in their relative abundance. The relative abundance of fungi such as Sporisorium graminicola strain CBS 10092 and Pyricularia grisea strain NI907 in the small intestine was significantly higher than the relative abundance in the large intestine. The relative abundance of fungi such as Candida orthopsilosis Co 90 125 and Fusarium verticillioides 7600 in the large intestine was significantly higher than the relative abundance in the small intestine. In summary, the composition of gut fungi in the small intestine and the large intestine was inconsistent.

Changes in the relative abundance of fungi in the small intestine after the intake of Lp082

The intestinal contents of D1, D3, and D7 in the small intestine were collected, and the shotgun metagenomic sequencing was performed. The results of the shotgun metagenomic sequencing were annotated to identify the species and relative abundance of fungi. The changes in the relative abundance of fungi in the small intestine after the gavage of Lp082 were analyzed. The relative abundance of Lp082 in the small intestine was quantified (Fig. 2a). The relative abundance of Lp082 was higher on D1 and significantly decreased on D3 (P = 0.0179). The relative abundance of different fungal species was annotated, and the Shannon index and the Simpson index for each small intestinal sample were calculated to assess the α-diversity of the fungal communities (Fig. 2b and c). After Lp082 entered the small intestine, the fungal community changed on the first day, and α-diversity increased. On D3, the Shannon index had significantly decreased (P = 0.0103), and the Simpson index also showed a significant reduction (P = 0.0103). Bray–Curtis dissimilarity was calculated between each time point in the Con group and the Tre group (Fig. 2d) to assess the differences in gut microbiota composition between different time points. While the Con group showed no significant differences in composition among the three-time points, the Tre group exhibited significant differences. Specifically, the composition of the fungal community in the Tre group on D1 was significantly different from those on D3 (F = 12.4273, P = 0.009), and the composition on D1 was also significantly different from those on D7 (F = 5.8698, P = 0.01). These results indicated that after the intake of Lp082, the composition of the fungal community in the small intestine significantly changed. PCoA was then performed, and the explained variance for each PCoA axis was calculated (Fig. 2e and f). The PCoA1 values for the Tre group on D1 were significantly different from those on D3 (P = 0.0260), and the PCoA2 values for the Tre group on D1 were significantly different from those for the Con group on D7 (P = 0.0061). These demonstrated that the Tre group on D1 was different from the other groups.

Fig. 2
figure 2

Changes in the Relative Abundance of Fungi in the Small Intestine After the Intake of Lp082. a Changes in the relative abundance of Lp082 in the small intestine on D1, D3, and D7. b,c α-diversity of gut fungi in the small intestine at different time points. The (b) panel shows the Shannon index, and the (c) panel shows the Simpson index. d Bray–Curtis dissimilarity of the small intestine fungal community between the three-time points in the Con group and the Tre group. Green represents the Con group, and brown represents the Tre group. There is no significant difference among the three-time points in the Con group, while there is a significant difference between D1 and the other two time points in the Tre group. e, f PCoA1 and PCoA2 of small intestine fungal community structure based on Bray–Curtis dissimilarity. g Heatmap showing the changes in the relative abundance of fungi with significant differences (P < 0.05) between the Con group and the Tre group in the small intestine on D1, D3, and D7. An asterisk (*) indicates fungi species with significant differences on the respective day. h Venn diagram showing the number of fungal species with significantly different (P < 0.05) in the small intestine on D1, D3, and D7. Two strains overlap between D3 and D7. i Co-occurrence network of fungi and Lp082 in the small intestine based on Spearman correlation (P < 0.05, |R|> 0.4). In the network, the green nodes represent Lp082, the red nodes represent fungi positively correlated with Lp082, the blue nodes represent fungi negatively correlated with Lp082, the orange lines represent direct associations with Lp082, the gray lines represent indirect associations with Lp082, the solid lines represent positive correlations, and the dashed lines represent negative correlations. The thickness of the lines represents the strength of the correlation

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Subsequently, fungi with significant differences in relative abundance between the treatment and the Con group on D1, D3, and D7 were identified (P < 0.05) (Fig. 2g). Notably, the relative abundance of Candida albicans in the small intestine was significantly reduced in the Tre group compared to the Con group (P = 0.0345). The number of fungal species with significant differences at each time point was counted (P < 0.05) (Fig. 2h). A total of 14 fungal species showed significant differences in relative abundance between the Tre group and the Con group. It was observed that on D1, a larger number of fungal species, specifically 8, exhibited significant changes in relative abundance. Among those, two fungal species also showed significant differences on day 7. Spearman's rank correlation coefficients were calculated using the relative abundance of gut fungi and Lp082 at different time points. Four fungal species were found to be significantly correlated with Lp082 (P < 0.05, |R|> 0.4) and used to construct an interaction co-occurrence network (Fig. 2i). Among the identified interactions, it was observed that Brettanomyces nanus, Pyricularia grisea strain NI907, and Pyricularia pennisetigena strain Br36 showed a positive correlation with Lp082 (R = 0.4352, 0.4700, and 0.4701). Aspergillus oryzae RIB40 showed a negative correlation with Lp082 (R = -0.6284). After the intake of Lp082, there was a notable change in the fungal community of the small intestine, particularly evident on the first day. The relative abundance of related fungi, such as Candida albicans, decreased, while the relative abundance of Brettanomyces nanus and other associated fungi increased.

Changes in the relative abundance of fungi in the large intestine after the intake of Lp082

The intestinal contents of D1, D3, and D7 in the large intestine were collected. Then, the shotgun metagenomic sequencing was performed, and the results were used to annotate the species and relative abundance of fungi. The relative abundance of Lp082 in the large intestine was quantified (Fig. 3a). The relative abundance of Lp082 was higher on D1 and significantly decreased on D3 (P = 0.0031). The relative abundance of different fungal species was then calculated, and the Shannon index and the Simpson index for each large intestinal sample were analyzed to assess the α-diversity of the fungal communities (Fig. 3b and c). There was no significant difference in the α-diversity across the different time points. Bray–Curtis dissimilarity between each time point in the Con group and the Tre group was calculated (Fig. 3d). There were no significant differences in fungal composition between the different time points in the Con group, but the Tre group exhibited significant differences. Specifically, the composition of the fungal community in the Tre group on D3 was significantly different from that on D7 (F = 7.0083, P = 0.012), and the composition on D1 was also significantly different from that on D7 (F = 4.0680, P = 0.008). After the intake of Lp082, the composition of the fungal community in the large intestine significantly changed. PCoA was then performed, and the explained variance for each PCoA axis was calculated (Fig. 3e and f). The PCoA2 values for the Tre group on D7 were significantly different from those on D1 (P = 0.0322), while there were no significant differences among the three-time points in the Con group. It demonstrated that there were greater differences in the fungal composition in the Tre group than in the Con group, and the Tre group on D7 was different from the other groups.

Fig. 3
figure 3

Changes in the Relative Abundance of Fungi in the Large Intestine After the Intake of Lp082. a Changes in the relative abundance of Lp082 in the large intestine on D1, D3, and D7. b,c α-diversity of gut fungi in the large intestine at different time points. The left panel shows the Shannon index, and the right panel shows the Simpson index. d Bray–Curtis dissimilarity of the large intestine fungal community between the three-time points in the Con group and the Tre group. Blue represents the Con group, and red represents the Tre group. There is no significant difference among the three-time points in the Con group, while there is a significant difference between D7 and the other two time points in the Tre group. e, f PCoA1 and PCoA2 of large intestine fungal community structure based on Bray–Curtis dissimilarity. g The fitted curve of the relative abundance changes of the single strain in the large intestine showed significant differences in relative abundance between the Con group and the Tre group. This strain exhibited a significant difference on D7. h Venn diagram showing the number of fungal species with significantly different (P < 0.05) in the large intestine and the small intestine. There is no overlap between the fungi with significant differences in the small intestine and the large intestine (i) Co-occurrence network of fungi and Lp082 in the large intestine based on Spearman correlation (P < 0.05, |R|> 0.4). In the network, the green nodes represent Lp082, the red nodes represent fungi positively correlated with Lp082, the blue nodes represent fungi negatively correlated with Lp082, the orange lines represent direct associations with Lp082, the solid lines represent positive correlations, and the dashed lines represent negative correlations. The thickness of the lines represents the strength of the correlation

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Subsequently, fungi with significant differences in relative abundance between the Tre group and the Con group on D1, D3, and D7 were identified (P < 0.05), and only Pochonia chlamydosporia 170 showed a significant difference on D7(P = 0.0210) between the Tre group and the Con group (Fig. 3g). The number of fungi with significant differences between the small and the large intestines was calculated (Fig. 3h). There was only one fungus with significant differences in the large intestine, while the number in the small intestine was 14, with no overlap between the small intestine and the large intestine. Spearman's rank correlation coefficients (P < 0.05, |R|> 0.4) were calculated using the relative abundance of gut fungi and Lp082 at different samples (Fig. 3i). There were only two fungal species were significantly correlated with Lp082. Specifically, Botrytis cinerea B05 10 was positively correlated with Lp082 (R = 0.4363), Zygotorulaspora mrakii was negatively correlated with Lp082 (R = -0.4241). The changes caused by Lp082 in the large intestine were less than those in the small intestine.

Initial distribution of the gut bacteriophages in the small intestine and the large intestine of the mice

The relative abundance of gut bacteriophages in the intestinal contents of the Con group on D1 was calculated, aiming to analyze the initial distribution of the gut bacteriophages. First, the composition and distribution of the gut bacteriophages at the genus level was visualized (Fig. 4a). The gut bacteriophages mainly consisted of Aspergillus, Pyricularia, Pochonia, and Saccharomyces. The relative abundance of bacteriophage species in the gut was counted (Fig. 2b). At the species level, the four bacteriophage species with higher relative abundance were bacteriophages that infect bacteria Lactiplantibacillus B murinus, Limosilactobacillus B animalis, and Agathobacter rectalis. The distribution of bacteriophage species in the small intestine and the large intestine was separately analyzed (Fig. 4c). The top 14 bacteriophages in the small intestine and the large intestine were consistent with the top 14 bacteriophages ranked by their overall relative abundance in the gut. In both the small and the large intestines, the predominant bacteriophages were those infecting Lactiplantibacillus B murinus. The relative abundance of Lactiplantibacillus B murinus bacteriophages was higher in the large intestine compared to the small intestine. Conversely, the relative abundance of Limosilactobacillus B animalis bacteriophages was higher in the small intestine than in the large intestine.

Fig. 4
figure 4

Initial Distribution of the Gut Bacteriophages. a The relative abundance of bacteriophages at the genus level in the gut, displaying only the top 14 genera in terms of relative abundance. b Relative abundance of bacteriophages in the gut, showing only the top 14 species in terms of relative abundance. The names here refer to the hosts of the bacteriophages. c Distribution of bacteriophages at the species level in the small intestine and the large intestine. The left panel shows the distribution in the small intestine, and the right panel shows the distribution in the large intestine, displaying only the top 14 species in terms of relative abundance. The names here refer to hosts of the bacteriophages. d, e α-diversity of bacteriophages in the small intestine and the large intestine. The (d) panel shows the Shannon index, and the (e) panel shows the Simpson index. The green box represents the Shannon index in the small intestine, and the blue box represents the Simpson index in the large intestine. Both the Shannon index and Simpson index revealed significant differences between the small intestine and the large intestine. f PCoA of large intestine and small intestine bacteriophage community structure based on Bray–Curtis dissimilarity. g Heatmap showing the relative abundance changes of bacteriophages with significant differences (P < 0.05) between the small intestine and the large intestine. The darker colors indicate higher relative abundance. A total of 51 bacteriophage species showed significant differences

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The Shannon index and Simpson index of gut bacteriophages in the small intestine and the large intestine were calculated (Fig. 4d and e). In the large intestine, the Shannon and Simpson index were significantly higher than in the small intestine (P = 0.0022). PCoA based on Bray–Curtis distances (Fig. 4f) showed that the compositions of the small intestine and the large intestine were largely separated (P = 0.004). The number of bacteriophage species with significantly different relative abundance between the small intestine and the large intestine was 51. (P < 0.05) (Fig. 4g). Bacteriophages such as those infecting Roseburia intestinalis and Parabacteroides distasonis had significantly lower relative abundance in the small intestine compared to the large intestine. In contrast, bacteriophages such as those infecting Enterococcus faecium and Limosilactobacillus salivarius had significantly higher relative abundance in the small intestine compared to the large intestine. At the initial state, there was a significant difference in the composition of gut bacteriophages between the large intestine and the small intestine.

Changes in the relative abundance of bacteriophages in the small intestine after the intake of Lp082

The species and relative abundance of bacteriophages in the small intestine on D1, D3, and D7 were annotated. The Shannon and Simpson index for each time point were calculated (Fig. 5a and b). After Lp082 entered the small intestine, the Shannon and Simpson index of the bacteriophages in the small intestine increased on the first day, but the increase was not statistically significant. Bray–Curtis dissimilarity between each time point in the Con group and the Tre group was calculated (Fig. 5c). It was found that the Con group showed no significant differences in composition among the three-time points, but there was a significant difference in the composition between D1 and D3 in the Tre group (P = 0.045). After the gavage of Lp082, the composition of the bacteriophages in the small intestine significantly changed. The explained variance for each PCoA axis showed that the PCoA1 value for the Tre group on D1 was significantly different from that for the Con group on D1 (P = 0.0394), and the PCoA2 value for the Tre group on D1 was significantly different from that for the Con group on D7 (P = 0.0101) (Fig. 5d and e). It was evident that the composition in the Tre group on D1 differed significantly from those in the other samples.

Fig. 5
figure 5

Changes in the Relative Abundance of Bacteriophages in the Small Intestine After the Intake of Lp082. a, b α-diversity of gut bacteriophages in the small intestine at different time points. The (a) panel shows the Shannon index, and the (b) panel shows the Simpson index. c Bray–Curtis dissimilarity of the small intestine bacteriophage community between the three-time points in the Con group and the Tre group. Green represents the Con group, and brown represents the Tre group. There is no significant difference among the three-time points in the Con group, while there is a significant difference between D1 and D7 in the Tre group. d, e PCoA1 and PCoA2 of small intestine bacteriophage community structure based on Bray–Curtis dissimilarity. f, g Venn diagram showing the number of bacteriophage species with significant differences in the small intestine on D1, D3, and D7. The (f) panel is the Venn diagram of bacteriophage species with significantly different relative abundance at P < 0.01, and the (g) panel is the Venn diagram of bacteriophage species with significantly different relative abundance at P < 0.05. h Heatmap showing the changes in the relative abundance of bacteriophages with significant differences (P < 0.01) between the Con group and the Tre group in the small intestine on D1, D3, and D7. An asterisk (*) indicates bacteriophage species with significant differences on the respective day. i A line plot and linear regression of the relative abundance changes of Lp082 and its bacteriophages, R = 0.55, indicating a positive correlation between the relative abundance of Lp082 and its bacteriophages. j, k The host bacteria corresponding to the bacteriophage with significantly different relative abundance were identified, and TWO strains with significantly different relative abundance were found, including Anaerostipes hadrus and Blautia hansenii. Line plots showing the relative abundance at three-time points were constructed, and linear regression analysis was performed. Each group of bacteria and their bacteriophages exhibited a highly positive correlation

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Bacteriophages with significant differences in relative abundance between the Tre group and the Con group were identified. The number of bacteriophage species with significant differences at each time point was counted (Fig. 5f and g). There was a notable overlap in the bacteriophage species between D1 and D7, with 20 different bacteriophage species showing significant differences (P < 0.05) among these, Clostridium Q symbiosum and Coprococcus eutactus were highly significant (P < 0.01). Bacteriophages with significantly different relative abundance at each time point were collected (P < 0.01) (Fig. 5h). It was found that the hosts of all the bacteriophages with significantly different relative abundance belonged to the class Clostridia. To investigate the abundance correlation between host bacteria and their bacteriophages, the relative abundance of the host bacteria in the small intestine was analyzed. First, the relative abundance of Lp082 and its bacteriophages was used to perform a linear regression analysis (Fig. 5i). The analysis showed that the trends in the relative abundance of Lp082 and its bacteriophages were similar, with a positive correlation (R = 0.55). Additionally, two bacterial strains corresponding to the bacteriophages with significantly different relative abundance were identified. These strains were Anaerostipes hadrus and Blautia hansenii. The relative abundance of the host bacteria and their bacteriophages was also used to perform a linear regression analysis (Fig. 5j and k). The analysis revealed that the relative abundance trends of the host bacteria and their bacteriophages were consistent, and the relative abundance showed high positive correlations (R = 0.81 and 0.85). Lp082 caused a significant change in the gut bacteriophages in the small intestine, which was primarily characterized by an increase in the relative abundance of both Clostridia bacteria and their bacteriophages.

Changes in the relative abundance of bacteriophages in the large intestine after the intake of Lp082

The species and relative abundance of bacteriophages in the large intestine on D1, D3, and D7 were annotated. The Shannon and Simpson index for each time point were calculated (Fig. 6a and b), and the differences in α-diversity between each sample were not statistically significant. The Bray-Curtis dissimilarity between each time point in the Con group and the Tre group was calculated (Fig. 6c). In the Con group, no significant differences were observed between D1 and D3 or between D3 and D7. There was only a significant difference between D1 and D7 (P =0.017). However, in the Tre group, significant differences were observed between all the time points. Specifically, the P-value between D1 and D3 was 0.049 (F = 2.2925), the P-value between D3 and D7 was 0.017 (F = 2.9155), and the P-value between D1 and D7 was 0.031 (F = 3.4328). These results proved that the intake of Lp082 influenced the bacteriophage community in the large intestine. Then, PCoA was performed, and the explained variance for each PCoA axis was calculated (Fig. 6d and e). In the Tre group, the PCoA2 value on D3 was significantly different than that on D7 (P = 0.0137). The compositional differences in the large intestine between different time points in the Tre group were greater than those in the Con group.

Fig. 6
figure 6

Changes in the Relative Abundance of Bacteriophages in the Large Intestine After the Intake of Lp082. a, b α-diversity of gut bacteriophages in the large intestine at different time points. The left panel shows the Shannon index, and the right panel shows the Simpson index. c Bray–Curtis dissimilarity of the large intestine bacteriophage community between the three-time points in the Con group and the Tre group. Blue represents the Con group, and red represents the Tre group. There is only a significant difference between D3 and D7 in the Con group. However, in the Tre group, significant differences were observed between all the time points. d, e PCoA1 and PCoA2 of large intestine bacteriophage community structure based on Bray–Curtis dissimilarity. f, g Venn diagram showing the number of bacteriophage species with significant differences in the large intestine on D1, D3, and D7. The (f) panel is the Venn diagram of bacteriophage species with significantly different relative abundance at P < 0.01, and the (g) panel is the Venn diagram of bacteriophage species with significantly different relative abundance at P < 0.05. h Venn diagram showing the number of bacteriophage species with significantly different (P < 0.01) in the large intestine and the small intestine. There were four overlaps between the bacteriophage species with significantly different relative abundance in the small intestine and the large intestine. i Heatmap showing the changes in the relative abundance of bacteriophages with significant differences (P < 0.01) between the Con group and the Tre group in the large intestine on D1, D3, and D7. An asterisk (*) indicates bacteriophage species with significant differences on the respective day. j, k, l The host bacteria corresponding to the bacteriophage with significantly different relative abundance were identified, and three strains with significantly different relative abundance were found, including Lactiplantibacillus plantarum, Achromobacter xylosoxidans, and Blautia hansenii. Line plots showing the relative abundance at three-time points were constructed, and linear regression analysis was performed. Each group of bacteria and their bacteriophages exhibited a positive correlation

Full size image

Then, we identified bacteriophages with significant differences in relative abundance between the Tre group and the Con group and counted the number of bacteriophage species at each time point (Fig. 6f and g). Among the bacteriophages with significantly different relative abundance (P < 0.05), there were two overlapping bacteriophage species between D3 and D7, and 11 overlapping bacteriophage species between D1 and D7. Bacteriophage species with significantly different relative abundance at each time point were collected (P < 0.01) (Fig. 6i). Notably, the relative abundance of bacteriophage that infected Lactiplantibacillus plantarum significantly increased (P = 0.0033). The number of bacteriophage species with significantly different relative abundance was calculated between the small intestine and the large intestine (Fig. 6h). In the large intestine, a total of 19 bacteriophage species with significantly different relative abundance were identified, which is fewer than the number of bacteriophage species with significantly different relative abundance in the small intestine. The number of bacteriophage species with significantly different relative abundance between the small intestine and the large intestine was 4. Then, three bacterial strains corresponding to the bacteriophages with significantly different relative abundance were identified. These strains were Lactiplantibacillus plantarum, Achromobacter xylosoxidans, and Blautia hansenii. The relative abundance of the host bacteria and its bacteriophages was also used to perform a linear regression analysis (Fig. 6j, k, and l). It was found that the relative abundance trends of the host bacteria and their bacteriophages were consistent, and the relative abundance showed positive correlations (R = 0.65, 0.57, and 0.7). The gavage of Lp082 influenced the gut bacteriophages of the large intestine, though this change was less pronounced compared to that in the small intestine.

Discussion

In this study, the changes in gut fungi and bacteriophages after the gavage of Lp082 were identified by performing shotgun metagenomic sequencing. Initially, the local fungi and bacteriophage communities in the small intestine and the large intestine were different, and the effects of the probiotic on these communities were different. After Lp082 entered the gut, the diversity of fungi and bacteriophages increased. The effects of probiotic Lp082 on the fungal and bacteriophage communities in the small intestine were greater than those in the large intestine. In the small intestine, the abundance of Candida albicans decreased, while changes in bacteriophages were mainly observed in the Clostridia class. We also found a positive correlation between the relative abundance of bacteriophages and their host bacteria.

The compositional changes in fungi and bacteriophages in the small intestine were more pronounced than those in the large intestine. This result may be related to the distinct environmental conditions in the small intestine and the large intestine. The pH in the small intestine typically ranges from 6.0 to 7.5, while the pH in the large intestine usually ranges from 5.7 to 6.7, which is more suitable for the survival of Lp082 [52]. The more challenging environment in the small intestine may intensify competition among microbiota, leading to more intense interactions between probiotics and the gut microbiota [53]. In addition, the gut microbiota structure in the large intestine is more stable [54], with higher microbial diversity, which makes it less likely to be affected. In summary, the more intense competition in the small intestine and the more stable gut microbiota structure in the large intestine may be the reasons why probiotics cause greater changes in the small intestine.

The mice's gut was predominantly colonized by the fungus Aspergillus. Aspergillus was also found to be one of the dominant fungi in the human gut microbiota [55]. Aspergillus is widely found in natural environments, such as soil, plant residues, and air. Moreover, Aspergillus is also very common in corn and wheat. Therefore, the predominance of Aspergillus in the mice's gut may be related to the mice's diet, which primarily consists of corn, wheat, and other plant-based feed. Previous studies have also found that diet is a major factor influencing gut fungi, and the dominant fungal species are associated with dietary preferences [51]. After the gavage of Lp082, the relative abundance of Candida albicans decreased. Candida albicans is highly pathogenic and can drive mucosal dysbiosis by affecting the mucosal microbiota. Candida albicans can increase the permeability of the oral epithelial barrier by increasing Enterococci's degradation of epithelial junction protein E-cadherin [56] and can inhibit host immunity by blocking type I interferon (IFN-I) signaling via translocating the effector protein Cmi1 into host cells [57]. The abundance of Candida albicans is also higher in Crohn's disease [58]. The reduction in Candida albicans may effectively prevent the harm caused by Candida species, which has beneficial implications for human health [59].

It was found that the relative abundance changes of bacteria and bacteriophages were consistent. Traditionally, it is known that bacteriophages infect bacteria, and an increase in the abundance of bacteriophages may cause a decrease in the abundance of their host bacteria [14]. However, our study revealed a positive correlation between the relative abundance of bacteriophages and their host bacteria. Since most of the bacteriophages in the gut are prophages carried by the bacteria themselves and do not cause bacterial lysis [60, 61], the bacteriophages annotated in our study were likely derived from prophage sequences within the bacterial genomes rather than from the gut environment. In future studies, we will perform metagenomic sequencing of the gut bacteriophages and increase sequencing depth to more comprehensively investigate the impact of probiotics on the gut microbiome.

This study innovatively analyzed the responses of gut fungi and bacteriophages in different intestinal segments at different time points, investigating the specific changes in the abundance of gut fungi and bacteriophages after the intake of Lp082 and their potential impacts on host health. This study contributed to a deeper understanding of the impact of probiotics on gut fungi and bacteriophages and provided data support for the association and dynamic changes between gut bacteria and their infecting bacteriophages.

Data availability

All the metagenomic sequencing data reported in this paper have been deposited in the NCBI database, the project number is: PRJNA1035164.

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China (32160545 and 32222066).

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Author notes

  1. Shuaiming Jiang and Jiachao Zhang are joint corresponding authors.

Authors and Affiliations

  1. School of Food Science and Engineering, Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Hainan University, Haikou, 570228, Hainan, China

    Xinlei Liu, Zhe Han, Wenyao Ma, Weipeng Cui, Dongyu Zhen, Shuaiming Jiang & Jiachao Zhang

  2. Collaborative Innovation Center of One Health, Hainan University, Haikou, 570228, Hainan, China

    Jiachao Zhang

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Contributions

L.X.: Methodology, Formal analysis, Investigation, Writing—Original Draft, Visualization. H.Z: Methodology, Investigation, Data Curation. M.W and C.W: Methodology, Investigation. Z.D: Investigation. J.S: Writing—Review & Editing. Z.J: Conceptualization, Resources, Project administration, Funding acquisition.

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Correspondence to Shuaiming Jiang or Jiachao Zhang.

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Animal experiments were conducted according to the guidelines of the Declaration of Helsinki and approved by the Animal Ethics Committee of Hainan University (AWER Issue No. HNUAUCC-2023–00174).

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The authors declare no competing interests.

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Liu, X., Han, Z., Ma, W. et al. Effects of Lactiplantibacillus plantarum HNU082 intervention on fungi and bacteriophages in different intestinal segments of mice. BMC Microbiol 25, 69 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03784-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03784-0

Keywords

BMC Microbiology

ISSN: 1471-2180

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