PJZ_58_2_501-512

Introduction

The gut microbiota plays a crucial role in nutrient breakdown and disease resistance, thereby contributing to the overall health of the host (Xiong et al., 2017). Infections of the intestinal flora by pathogenic bacteria can disrupt the balance of the microbiota, leading to health problems. Specifically, the presence of Listeria monocytogenes can significantly impact the microbial communities in the host gut, resulting in detrimental effects on overall health (Bernbom et al., 2006). L. monocytogenes is a gram-positive, rod-shaped bacterium that typically forms single short chains, and possesses the ability to withstand drying and freezing conditions (Theivagt et al., 2006). Due to its ability to grow at low temperatures, L. monocytogenes is considered one of the primary pathogens that pose a threat to human health, particularly in refrigerated food (Harvey et al., 2007). Listeriosis is a severe infection caused by consuming food contaminated with L. monocytogenes, with a mortality rate exceeding 25% (Sallami et al., 2006). People with a weak immune system like older, pregnant ladies, and newborns are most susceptible (Lagier et al., 1996). Recently, L. monocytogenes has also been isolated from the intestinal tract of small percentage of the apparently healthy human population (Guo et al., 2016). Emerging evidence over recent decades has stressed the importance of gut microbial diversity and richness and genes functioning in diseases. It is the fundamental element in the maintenance of the host’s health and immune system. The diversity and richness of the gut microbiome can increase the resistance against pathogenic microbes, and immunity of the host, thus, improving host survival chances (Lawley et al., 2013). Host health management during L. monocytogenes infection has received attention towards its therapy through the diversity and richness of the gut microbiome (Tran et al., 2013). Therefore, the study of the intestinal flora model for different pathogenic bacteria infections has become the focus of food and drug research.

Photodynamic germicidal technology is a new non-thermal germicidal method, which is used in disease treatment, food preservation, antibacterial material, and other fields (Ren et al., 2020). At present, photodynamic therapy can play a synergistic role with polyphenols or quinones to improve intestinal antibacterial ability and maintain the diversity and richness of intestinal microbiota (Sanhueza et al., 2017; Wang et al., 2017). This may be a new approach to preventing Listeriosis by improving the diversity and richness of the gut microbiome. Gallic acid is a kind of phenolic compound from plants, which has broad-spectrum antibacterial and antioxidant effects, several reports have declared that gallic acid (GA) in coordination with UV light can inhibit a variety of planktonic bacteria, biofilms, and fungi via producing reactive oxygen species (ROS) and decomposing bacterial membranes (Nohynek et al., 2006; Nakamura et al., 2015). Nakamura et al. (2015) and Wang et al. (2017) reported that GA solutions irradiated by UV rays can exhibit enhanced antibacterial properties. The number of L. monocytogenes and Escherichia coli on the treated surface of spinach was significantly higher in GA solutions without any radiation than that of GA solution irradiated by UV for 24 h (Ding et al., 2018; Gupta et al., 2007). In addition, applications of GA can improve gut bacterial richness and its diversity, which stimulates the host immune system, and ultimately increases the host’s tolerance (Inada et al., 2020). Rolhion et al. (2019) reported that L. monocytogenes could destroy the diversity and richness of host gut microbiota, resulting in the imbalance of gut bacteria, thus causing listeriosis. The beneficial gut microbes found in animals, along with GA, can inhibit L. monocytogenes infection and improve the growth and immunity of host (Bao et al., 2019). Navita demonstrated that GA application changes the intestinal microbiota profile, enriching beneficial microbes (Ding et al., 2018). However, it requires high concentration of GA for longer durations. GA cannot effectively kill L. monocytogenes due to the inactive stationary phase of bacteria and high-density colony-forming units (CFU) (Inada et al., 2020; Xu et al., 2014). Therefore, it is rare to use GA alone to inhibit pathogenic bacteria.

Currently, the responses of the microbiome and specific bacterial species to L. monocytogenes, as well as how UVC-GA acts on this pathogenic bacterium, are still being studied. Therefore, it is crucial to identify an effective treatment to enhance the host’s resistance to Listeriosis infection by promoting the diversity and richness of gut microbes. The objective of this study is to examine the alterations in the gut microbiome of L. monocytogenes-infected hosts (mice) and assess the impact of UVC-GA on the gut microbiome of these infected hosts.

Materials and Methods

The experiment was conducted at the Aquatic Food Processing and Preservation Laboratory of Guangxi Academy of Fishery Sciences (Nanning, Guangxi, China). A mouse model infected with L. monocytogenes was created, and this was done by assessing blood parameters and analyzing the bacterial composition of the microbiome (Callahan et al., 2016).

Preparation of active L. monocytogenes

The commonly encountered strain of L. monocytogenes was obtained from the Guangxi Key Laboratory of the Aquatic Food Processing and Preservation (Guangxi Academy of Fishery Science) and the culture was stored in a glycerin solution at −80°C. At the start of the experiment, the frozen gel was placed in a sterile environment using a sterilized inoculation ring, following which inoculation on blood agar (Qingdao Haibo Bioengineering Company, Qingdao, Shangdong, China), and the plate was incubated at 24°C for 24 h. Typical individual colonies were selected and diluted using sterilized saline and linearly inoculated on blood agar for purification at 37°C for 24 h, and this procedure was repeated three times. Toxicity rejuvenation was conducted according to Gupta et al. (2007). To prepare bacterial suspensions, colonies were eluted on blood agar using 0.65% sterilized saline. A bacterial suspension volume of 0.5 mL was intraperitoneally injected into each AC57BL/6 J mouse. Mice were continuously monitored for evidence of disease for one week, during which symptoms of diseased individuals were recorded each day and dead ones were removed and sent to the laboratory. In the laboratory, the dead individuals were assessed for disease symptoms through visceral organs and blood agar was used for pathogen isolation and identification. ThF1: 5′ CTCTCCAATCAATGCCACTTCC 3′ and R1: 5′ CCCTTGTGGAGGTTCCTTGT 3′ diagnostic primer sets were used to verify infection in live individuals and tissues of the gut. Polymerase chain reaction and the settings of the thermal cycler were as outlined in Øvergård et al. (2010). Strain purification and culture were administered according to the above approach once L. monocytogenes was well affirmed, and bacterial concentration was quantified at a wavelength of 600 mm using an SH-254 spectrophotometer (Shanghai Bioengineering Company, Shanghai, China) according to Rolhion et al. (2019). Once prepared, the stain was immediately used in the infection experiments.

Animal experiment

Experimental protocols were granted by Guangxi Academy of Fishery Science for all animals (Grant number: GAFS2019018). Healthy AC57BL/6 J mouse (n=40, 16-weeks old male and 27.7±1.6 g weight) were purchased from Little Science Biotechnology Limited Company (Nanning, Guangxi, China). Normal mice without infected by L. monocytogenes were used as control cohort (CC) (n=10). Infected mice (n=30, 16-weeks old male and 27.7 ± 1.6 g weight) were evenly divided into three cohorts: (1) infected mice (n=10) orally fed with UVC-GA solution were taken as treated cohort (TC); (2) infected mice (n=10) not administered UVC-GA solution were taken as negative cohort (NC); (3) infected mice (n=10) orally fed with GA solution were taken as positive cohort (PC). All mice were reared a normal diet of 10% kcal/fat energy daily, simultaneously orally administered with sterile physiological saline (SPS, 300 μL). All mice were fed at 24 °C and a 12 h light/dark cycle in the animal room equipped with air-condition, and they freely foraged and drunk water. After the mice were acclimated for 7 days, PC- and TC-mice were orally fed with 5 ml/kg body weight UVC-GA or GA, respectively, whereas the CC- and NC-mice were administered SPS using the same strategy. Mice were fed UVC-GA or GA or SPS once a day according to the Km factor ratio of 3 and 37 for 20 g of mice and 60 kg of humans (Reagan et al., 2008). The mice were fed the diet twice daily (8:00 and 17:00). Food intake and body weight were recorded other five days, and the experiment period was 20 days.

Sample collection and processing

To investigate the impact of the administration of UVC-GA to the L. monocytogenes infected mice, routine sampling was conducted at day 0, 5, 10, 15, and 20 in all four cohorts to measure body weight and gut microbiome. Blood parameters and incidences of mortality in these cohorts were also recorded. Over the four sampling periods, all samples from CC-, NC-, PC- and TC-cohort representing a range of body weight were sequenced for further sample processing. The venous blood (2 mL from each mouse) of the selected mice were aseptically taken using a syringe injected into the bulbous arteriosus. Plasma was isolated from the blood sample using centrifugation at 1,000 g for 20 min at 4 °C and the supernatant was stored at −20 °C for subsequent biochemical analysis. The intestinal tract of each sampled mice was obtained using sterile cotton swabs inserted into anal area of 3 cm. Intestinal feces were placed in a 15 mL conical tube. Each swab sample was resuspended in 2 mL of Luria-Bertani broth and 2 mL of 30% glycerol, decanted into three tubes (2 mL) and frozen in an ultra-low temperature freezer at −80°C. All detected trials were done in three replicates.

Measurement of blood parameters of infected mice

The immune proteins to be measured included total protein (TP), immunoglobulin (IgM) and albumin (ALB), and were detected using a fluorescent spectrophotometer (Hitachi Ltd, Japan). Enzyme-linked immunoassay kits were obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China). IgM content in samples was measured by the orderly addition of horseradish peroxidase-labeled detection antibodies (Nanjing Jiancheng Bioengineering Institute) for a reaction of 10 min at room temperature. The substrate tetramethylbenzidine was then added to the above solution and observed for color change at room temperature for 5 min. The absorbed IgM was detected at a wavelength of 450 nm, and the content of IgM was determined by reference to the standard. The bicinchoninic acid method was used to measure TP. A mixture of 20 μL of the sample and 250 μL of bicinchoninic acid was incubated at 37 ℃ for 30 min. Finally, 30% ethanol was added to stop the reaction and the solution was left for 5 min at room temperature. Absorbed TP was then detected at a wavelength of 562 nm. ALB was measured by mixing 10 μL of the sample with 2.5 mL bromocresol green reagent, and the mixture was allowed to stand at room temperature for 10 min. ALB content was then measured at a wavelength of 628 nm.

Extraction and amplification of DNA and gene sequencing of 16S rRNA

DNA from intestinal cells of individual mice was collected using CTAB/phenol: Chloroform extraction as according to Xu et al. (2014). Molecular grade water was used to precipitate DNA, following which the DNA was fluorometrically quantified. The quality of DNA was analyzed through absorbance measurement at a wavelength of 260 nm with the use of a NanoDrop spectrophotometer (Thermo Scientific). Dilution of intestinal DNA to 1 ng µL−1 was implemented, following which it was transferred to two 96-well plates. Indexing primers and the one-step custom PCR protocol was used to generate the Amplicon libraries as outlined in Kozich et al., (2013) and the 341F: (5′ CCTAYGGGRBGCASCAG 3′) and 806B (5′ GGACTACNVGGGTWTCTAAT 3′) V4 primers. Amplification of all samples was conducted in triplicate to minimize PCR bias. The reactions were composed of 30 µL NEBNext PCR mix (New England BioLabs), 3 µL of forward and reverse primers (10 µM), 2 µL H2O and 10 µL of template DNA (1 ng/µL). Initial denaturation was conducted at 98 °C for 1 min, after which 30 cycles of denaturation for 10 s at 98 °C, 30 s of annealing at 50 °C and 30 s of extension at 72 °C was implemented. The final extension was conducted at 72 °C for 5 min. Triplicate PCR reactions were then pooled prior to purification.

Agenourt AMPure XP bead-based clean-up was used to purify amplicon libraries to remove primer dimers and free primers. After resuspension of the cleaned DNA in a buffer (Illumina), an assessment of length of the amplicon was implemented using the D1000 Screen Tape system (Agilent). A fragment size of approximately 400 bp was expected. The Promega Glomax kit was used to quantify libraries. Two independent library pools were made in consideration of the low yield of certain libraries. After dilution to 2 µM, these were mixed according to their ratio share of the samples. Determination of the concentration of the final pool was through quantitative PCR. Sequencing was conducted using 196 libraries, including two controls, 250 bp reads (v2 chemistry) and the Illumina Miseq Illumina HiSeq platform (Beijing Novogene Bio-information Science and Technology Co., Ltd., China). Raw reads were deposited in the National Center for Biotechnology Information (NCBI) sequence read archive under the BioProject PRJNA577421.

Bioinformatics analysis

The R DADA2 analysis package was used to process all reads (Callahan et al., 2016). Trimming of paired-end reads was according to visualized scores of quality and standard filtering parameters of DADA2’s: maxN= 2, truncQ= 2, rm. phix= TRUE, and maxEE= 2. The parametric error model of DADA2 was fitted using the initial 1×108 bases. Sequences were de-replicated and inference of sequence variants was achieved using pseudo-pooling and the associated error model. Merged filtered reads were applied to build the table of amplicon sequence variants. Trimming and removal of chimeras was then implemented on the denoised full length sequences. The Silva database (v.132) was used to assign taxonomy. The run accuracy was measured using a mock community of known samples sequenced with a negative control. No measurable DNA was contained in the negative control library and it produced more than 2% of sequences compared to the average read count.

The basic local alignment search tool was used to compare all reads with the full nr database using the DIAMOND v0.7.9 blastx function (Buchfink et al., 2015). Classified reads were visualized in MEGAN6 Community Edition v6.5.5 (Huson et al., 2016) and sequences not representing bacteria were removed. Non-applicable taxonomic assignments were labeled using the lowest characterized taxonomic rank. Matrices of alpha diversity were analyzed within the phyloseq package (McMurdie et al., 2013). Pruning of exact sequence variants (ESVs) was conducted before non-metric multidimensional scaling; ESVs not found in at least one sample were removed along with samples with more than 1,000 reads. Seed were set at 2,209. The packages Phyloseq and ggplot2 were used to visualize taxonomic profiles and diversity measures. The rgl package was used to visualize three-dimensional ordinations (Adler et al., 2003).

Statistical analyses

Each experiment has three replicates and the R statistical environment was used to conduct statistical analyses (R Core Team, 2011). Assessment of the normal distribution of variables was conducted using the Shapiro–Wilk’s test with the Shapiro test function. Before downstream analysis, non-normal values were log-transformed. A small numeric constant (half of the detection limit: 0.00003661) was added to all values before logarithm transformation to quantify the relative abundance of specific bacterial taxa not present in samples. Variation in the dataset was correlated using linear models containing interaction terms. Running of Permutational multivariate analysis of variance was through the adonis function of in the Vegan software package. The same package was used to calculate the multivariate homogeneity of group dispersions (Betadisper). A total of 999 permutations were used in both aforementioned analyses (Oksanen et al., 2013).

Results

The model of L. monocytogenes infected mice

Infected mice observed of 0–25 days showed symptoms of infection such as reduced appetite, abnormal movements, fully filled ascites in the abdominal cavity and larger liver size. The blood parameter measurements showed lower TP, IgM and ALB in the CGs over 25 days compared to the CC (healthy individuals of 0 day) (Fig. 1A, B, C), which confirmed viral infection in mice. The results indicated that the model of L. monocytogenes infected individuals was accurate, as reported by Harvey et al. (2007) and Guo et al. (2016).

Small-subunit (SSU)

A total of 11,254,317 bacterial ribosomal small-subunit (SSU) reads from the V4 region from 483 samples were left after filtering. Each sample was on average represented by 22,947±1,054 reads. Sequencing depth ranged from 1,167–162,489 reads across all samples. values exceeding 0.975 were obtained for the good’s coverage index in all filtered samples, showing that more than 2% of reads in each sample appear only once (Fig. 2A) and near-saturation of community coverage was indicated by rarefaction curves (Fig. 2B). However, singletons were only kept if they were in multiple samples so as to remove artefactual sequences. The SSU indicated that the data of samples could use to gut microbiome analysis.

Gut microbiome profile of mice

The ESVs of Listeria spp. identified in all TCs and all NCs increased over time, and those in the NCs were higher. How, there was not detected in CC. The ESVs of genera Enterococcus, Synechococcus, Prevotella, Roseovarius, Psychromonas, Clostridium, and Bacillus were detected in TCs and CC, whereas no detection of these was determined in NCs. For each of the genera Enterococcus, Synechococcus, Prevotella, and Bacillus, no significant difference in the ESV numbers between TCs and CC was found, whereas the ESVs of the genera Roseovarius, Psychromonas, and Clostridium in CC were higher than those of all TCs. Although there was detection of lower ESVs of Lactobacillus spp. and Photobacterium spp. in average profiles at 5–25 days in the NCs, it comprised substantial proportions of profiles in the TCs post oral administration. ESVs numbering 20 under this assignment of Lactobacillus spp. and Photobacterium spp. were shared across all TC profiles. In addition, sequence

variants were aligned with these two genera in the Silva database with relatively low identity, ranging from 64.6% to 96.4%. For each of Psychrilyobacter spp., Phascolarctobacterium spp., and Vibrio spp., no significant difference in ESVs among them was obtained. However, in NCs, the ESVs of both genera Psychrilyobacter and Vibrio were detected at the first two sampling time points, and the ESVs of Phascolarctobacterium spp. was only found at 5 days. The ESVs of Bifidobacterium and Bacteroides genera in all TCs and CCs were higher than those of NCs. Although the ESVs of two genera in TCs was less than 20, it still comprised substantial proportions of profiles in the TCs post oral administration. In addition, there was not detection of Bifidobacterium spp. and Bacteroides spp. at final sampling time points. For Carboxylicivirga spp., it was not found in CC, NCs and TCs over sampling time, and no significant difference among them. Meanwhile, the Streptococcus genus was only detected in NC at 10 days (Fig. 3A, B, C and Table I). In total, there was more ESVs and genera in TCs compared to the NCs, and they were close to those of CC.

Table I. Exact sequence variant count of bacterial genera representing > 2% relative abundance.

Genus	Number of exact sequence variants (ESVs)
	NC (day)					CC (day)	TC (day)
	25	20	15	10	5	0	5	10	15	20	25
Listeria	25	24	25	20	17	-	8	10	11	11	12
Enterococcus	-	-	-	-	-	5	4	5	5	5	4
Bacillus	-	-	-	-	-	6	5	6	6	5	6
Bifidobacterium	-	-	6	7	6	13	11	10	10	9	9
Synechococcus	-	-	-	-	-	6	6	6	5	4	5
Vibrio	-	-	-	3	3	3	3	3	4	3	4
Bacteroides	-	-	5	7	8	18	15	11	12	14	12
Prevotella	-	-	-	-	-	3	3	4	3	2	4
Roseovarius	-	-	-	-	-	6	4	2	2	2	2
Psychromonas	-	-	-	-	-	7	3	3	4	3	-
Psychrilyobacter	-	-	-	2	2	2	3	2	2	3	3
Photobacterium	6	7	6	8	12	20	18	16	14	15	16
Streptococcus	-	-	2	-	-	-	-	-	-	-	-
Clostridium	-	-	-	-	-	11	7	8	10	-	-
Carboxylicivirga	-	-	-	-	2	2	3	2	-	-	-
Lactobacillus	3	4	4	5	10	22	16	18	16	20	21
Phascolarclobacterium	-	-	-	-	7	8	8	8	7	8	6

Bacterial richness (Chao1) and diversity (Shannon index) of the mice gut microbiome

The species richness (Chao1) and species diversity (Shannon’s diversity) averages of the gut biome were significantly higher in mice from the TC groups compared to NC groups (Chao1; P-value < 0.001. Shannon’s; P-value < 0.001). A linear model showed a significant loss in bacterial richness or diversity over time in the NCs, whereas in the TCs, species richness remained relatively constant with time (Chao1; P-value < 0.001. Shannon’s; P-value < 0.001) (Fig. 4A, B). It should be noted that an analysis of the results of the linear model shows that the NCs with no UVC-GA and TCs with UVC-GA explain all variability found in the data.

Survival rate

The survival curve of L. monocytogenes infected mice after 25 days showed the survival rate to be zero in the NCs within 10 days, whereas that of the TCs was > 60%, and survival in the TC groups showed an upward trend compared with the NC (Fig. 5). The results show that UVC-GA could effectively improve the survival of mice after infection with L. monocytogenes.

Discussion

The current research demonstrated that the microbial richness and diversity of the gut biomes of virus-infected mice were reduced compared to those of healthy individuals as the infection compromised intestinal structure and reduced appetite, thereby disrupting the homeostasis of the gut microbiome, and resulting in the disappearance of certain bacterial taxa (Nohynek et al., 2006). Bacterial infection correlates with dynamic changes in the microbial richness and diverse the gut microbiome, which ultimately impacts the growth of the hos (Nohynek et al., 2006). Several studies have claimed that bacterial diversity changes in the gut microbiome correlate with changes in the health of the host and incidences of enteric disease (Garcia-Gutierrez et al., 2019). The antagonistic potential within a microbiome with high bacterial diversity can perhaps increase pathogen resistance and could reduce microbiome susceptibility to incoming pathogens, thereby preventing the establishment of infection (Donaldson et al., 2016). A reduction in the diversity of the gut microbiome and the subsequent compromise of the ability of the microbiome to resist incoming pathogens could allow the proliferation of enteric pathogens such as L. monocytogenes (Garcia-Gutierrez et al., 2019). L. monocytogenes can colonize the host through the digestive tract, relying on entering through the intestinal epithelia (Rolhion et al., 2019). A possible explanation for the bacteria not being detected in CC mice whereas the virus proliferated NCs is that the diversity of the gut microbiome of mice in the NCs was compromised, and this impacted the gut microbiome’s resistance to colonization by the bacteria and subsequent infection. At the same time, all the mice in the NC group died, which was consistent with the low infection rate but high mortality of intestinal infection caused by L. monocytogenes. Furthermore, L. monocytogenes infection and the associated compromise of host immunity may result in lowered host selection pressures within the gut and result in the observed variations in the diversity and richness of the microbiota.

The results of the present study showed significant differences in the composition of gut microbiota between the NC and TC groups, and while L. monocytogenes dominated the NC groups by impeding other bacteria. The gut microbiota profiles in the TC groups were improved by the oral administration of UVC-GA solution. The colonization and infection by the bacteria are dependent on microbes present in the gut, and the diversity and species richness of the gut microbiome can account for differences in the resistance of individuals in a population to bacterial infection and may explain the higher incidence of bacterial infection in aged individuals (Rhoades et al., 2019; Sadiq, 2020).

Several genera attracted our attention, such as Vibrio spp. The Vibrio genus is often reported as the predominant genus found in the gut microbiomes of animals (Kim et al., 2019; Suginta et al., 2000). Several Vibrio spp. are well known to be human pathogens. As an example, Vibrio harveyi infection can disrupt the digestive tract epidermal tissue of organisms (Lee et al., 2015; Austin and Zhang, 2006). The observation of fewer ESVs of the Vibrio genus in TCs compared to NCs groups may be because the beneficial Vibrio genus was inhibited by the virus L. monocytogenes, and the oral administration of UVC-GA solution in the diet of infected mice resulted in the introduced microorganism outcompeting the Vibrio genus in the capture of nutrients and thereby impeded Vibrio reproduction.

The genera Photobacterium and Lactobacillus were identified from the gut of animals, and they are thought that it has a symbiotic relationship with dace by facilitating these animals to survive on the food of low quality (Xu et al., 2014). The foods of dace are predominantly of low nutrients such as decaying plant matter, and therefore a symbiotic association allowing them to more efficiently sequester nutrition from ingested food would benefit them. It is confirmed that these sorts of symbiotic relationships may have allowed dace to colonize terrestrial environments as these bacteria have also been found in the gut of other animals (Xiong et al., 2017), for example, this bacteria was also found in the cow. An interesting result is that considerable amounts detected in TCs in the current study were also found in NCs, implying a competitive relationship between L. monocytogenes and this bacterial species. Furthermore, this implies that Photobacterium and Lactobacillus may outcompete L. monocytogenes for intestinal tract nutrients. An additionally observed phenomenon was that the oral administration of UVC-GA solution could maintain considerable levels of the two bacterial species.

Many of the species in the large family Bacteroides and Bifidobacterium are important animal bacteria (Sugahara et al., 2015; Ceccaldi, 1989). The ESVs detected in the present study that was annotated as Bacteroides match several bacteria isolated from the microbiomes of human (Brook, 1989). Bacteroides have been identified in humans afflicted with the commonly-occurring bacterium that result in damage to the respiratory passage in the body (Brook, 1989). Bifidobacterium was identified in the gut microbiome of mice in the present study, and it was detected in all TC groups but only in NCs at 20-15 days. The presence of higher amounts of these genera in the CC compared to the TC groups might be because infection by L. monocytogenes and/or the oral administration of UVC-GA solution has no bearing on the presence of this genus. More research is needed to determine why this genus disappears in the NC groups over time.

The genera Phascolarctobacterium and Carboxylicivirga comprised considerable substantial fractions of the microbiomes of infected mice in the NC groups at 5 days. The Carboxylicivirga ESV is very similar to those of clones of Roseobacter associated with toxic algal blooms (KY277241 and KY277569), and those identified in the human gut (Wang et al., 2015). However, the function of this relatively recently recognized genus in any of its host species remains largely unknown (Zhang et al., 2016). Several Phascolarctobacterium species have been related to particular animal species, and have been identified in both humans and mice. In addition, multiple species are recognized as emerging pathogens of animals and have been linked to intestinal disease (Tran et al., 2019; Wu et al., 2017). The ESVs assigned to Phascolarctobacterium identified from the gut microbiomes of mice matched those isolated from the gut microbiomes of humans (Tran et al., 2019). The oral administration of UVC-GA solution restrained the colonization of the Phascolarctobacterium in TC-mice over time, but the Carboxylicivirga disappeared. and this observation could be explained by the Carboxylicivirga genera were sensitive to the UVC-GA solution.

For some diseases, the health of the host and the incidence of the disease is related to changes in the species diversity and richness of the gut microbiome, and the administration of microorganisms in the diet can improve bacterial species richness and diversity within the gut tract, thereby increasing the commensal bacterial community and the production of antimicrobial peptides, stimulating the host immune system and ultimately affecting the resistance of the host to other potential pathogenic microbes in the gut (Donaldson et al., 2016; Garcia-Gutierrez et al.,2019). GA can inhibit a variety of planktonic bacteria, biofilms, and fungi via produce ROS and decompose bacterial membranes (Nohynek et al., 2006; Nakamura et al., 2015). Administration of GA can improve gut bacteria richness and diversity, consequently promoting the microbiome health and host immune system stimulation, and ultimately increasing the host’s tolerance to other microbes in the gut (Gupta et al., 2007). Previous studies also have shown that animals who have been administered GA maintained a low incidence of L. monocytogenes infection by greater species diversity and richness of the gut microbiome, which improved the resistance of the gut microbiome to virus colonization (McMurdie and Holmes, 2013). The immunity of a host to L. monocytogenes infection can be enhanced by improving the species richness and diversity of the gut microbiome, which provides an increase in host selection pressures (Becattini et al., 2017). In the present study, the TC groups showed greater bacterial species richness and diversity in the gut microbiome of infected mice compared to the NC groups. A possible explanation for this is that the higher bacterial species diversity and richness in the gut microbiomes of mice in the TC group resulting from the administration of GA irradiated by UV-C enhanced the resistance of the mice to L. monocytogenes infection by increasing selection pressures and inhibiting the proliferation and establishment of this enteric pathogen. However, it is essential to investigate whether the effectiveness of UVC-GA is higher than GA.

The results of the current study show how L. monocytogenes infection in mice can correlate with changes to the gut microbiome and how the oral administration of UVC-GA solution can enhance the ability of infected individuals to resist this infection by strengthening the gut microbiome. The high blood parameters detected in the CC-mice compared to the NC demonstrates the accuracy of the model of L. monocytogenes infected individuals constructed in the present study. Results showed that the gut microbiome of infected mice improved through the oral administration of UVC-GA solution. Infected mice that were orally administered UVC-GA solution had more microbially rich and diverse microbiomes of the gut, in contrast to infected individuals in the NC in which the UVC-GA solution was not administered. Therefore, the administration of UVC-GA solution to fed mice could potentially enhance the resistance against L. monocytogenes. Also, this study indicated that the UVC-GA solution could be applied as a potential probiotic for beneficial microorganisms.

Furthermore, extended monitoring with increased numbers of samples is a prerequisite for analyzing the relationship between L. monocytogenes infection and the gut microbiome of mice under the administration of UVC-GA solution, to confirm the observations of the current study. Transcriptomic or metagenomic analysis can provide more clarity on the potential of administration of UVC-GA solution to the normal diet of mice for changing the phylogenetic community structure and host metabolic processes. Considered collectively, this knowledge could be applied to the design of new and effective potential probiotics for beneficial microorganisms to improve the cultivation of their species. However, the effective dosage and time needs to be further investigated.

Declarations

Acknowledgments

This project was funded by Science and Technology Fund of Guangxi Province (AB16380074), Science and Technology Major Project of Guangxi (AB21196020), the Guangxi key R & D projects (AB19245013), and National Natural Science Foundation of China (81960164). We also thank Philip Creed, PhD for editing the English text of a draft of this manuscript.

Funding

The Major Science and Technology Fund of Guangxi Province (AB21220048, AB23026063 and AB23026134). National Natural Science Foundation (32360628).

IRB approval

The research project was approved by the Guangxi Academy of Fishery Sciences, Guangxi, China.

Ethics statement

All the rules and regulations approved by ethical committee of Fishery Breeding and Processing Research Laboratory were followed.

Data availability

All data generated or analyzed during this study are included in this published article.

Statement of conflict of interest

The authors have declared no conflict of interest.

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