Bullfrog, Lithobates catesbeiana, is the most popular large edible frog found globally (Nori et al., 2011). The glands and the bile extracted are of economic value due to the nature of skin, oil, and hormones (Cathers et al., 1997), and play a key role in aquaculture, medicine, and other industries (Bury and Whelan, 1985; Silva et al., 2009). Farmers use various techniques to improve the yield during the rearing of bullfrogs. However, the water body has limited bearing capacity, and the occurrence of diseases is positively correlated with the stocking density. The continuous, large-scale, and hence high-density of bullfrog breeding caused many issues, such as a shortage of biologically healthy food, degradation of germplasm resources (Graves and Anderson, 1987), and deterioration of the breeding environment. The lack of breeding technology has complicated the aforementioned issues. The number of diseases encountered during bullfrog breeding is on the rise becoming more detrimental and resulting in frequent large-scale outbreaks seriously hindering industrial development. The pathogens of bullfrogs that are mainly responsible for diseases include bacteria, viruses, and parasites (Zhe et al., 2013). However, the strains used in aquaculture have usually been derived from wild strains (Kibenge et al., 2012). They may not have had enough time to adapt to high-density confinement in the aquaculture environment (Rodríguez-Ramilo et al., 2011) compared with terrestrial farmed animals. This chronic stress (Yada and Nakanishi, 2002) provides opportunities for the emergence of diseases caused by pathogens that may be harmless under natural conditions. Among several pathogens, Aeromonas hydrophila and A. salmonicida are considered the most common pathogens in freshwater fish, while Vibrio anguillarum and V. parahaemolyticus are the most familiar bacterial pathogens in a marine environment, causing different types of fish diseases such as ulcer disease, carp erythrodermatitis, motile Aeromonas septicemia, and so forth (Lightner, 1985; Martínez Cruz et al., 2012). In December 2021, a strange disease, commonly known as meningitis-like infectious disease (MID), broke out at a bullfrog farm (Figures 1A,B) in Ma’anshan City, Anhui Province. The sick frogs showed symptoms such as head tilt, loss of motor balance, opacity or hyperemia of eye lens, photophobia, and body edema, and even death (Figure 1D).

Most studies on the gut microbiome have been conducted in mammals, especially laboratory rodents and humans (Kohl and Yahn, 2016). However, studies on the gut microbiome of other vertebrates such as amphibians are lacking (Tong et al., 2019). The composition of frog gut microbiota is affected by season, temperature, and developmental state. Therefore, it is meaningful to study the gut and oral microbes of diseased frogs. The “ecosystem services perspective” treats the microbiota as an ecosystem that provides “services” to humans. Like any ecosystem, the host has some control over the structure of this system and the functional benefits it provides (Shreiner et al., 2015; Foster et al., 2017). The ecosystem services perspective treats the microbiome as an independent ecosystem that provides its host with services required for life and individual well-being (Calow, 1995; Arumugam et al., 2011). Gut microbial communities profoundly influence vertebrate physiology, impacting animal development, nutrition, immune function, and behavior. The gut microbiota are microorganisms (mainly bacteria but also viruses, protozoa, and fungi) and their collective genetic material present in the gastrointestinal tract (Falony et al., 2016). Besides the direct actions on the gut mucosa and enteric nervous system, many chemical mediators produced by the gut microbiome enter the bloodstream and communicate with distal organs such as the brain, heart, and liver (Evans et al., 2013), The symbiotic relationship between microbiota and the host is mutually beneficial. The host provides an important habitat and nutrients for the microbiome, and the gut microbiota support the development of the metabolic system and the maturation of the intestinal immune system by providing beneficial nutrients (D’Argenio and Salvatore, 2015), for example, by the synthesis of vitamins (Kau et al., 2011; Kitamoto et al., 2020) and short-chain fatty acids (Topping and Clifton, 2001; McDermott and Huffnagle, 2014). The oral cavity is a primary gateway to the human body and has the second-largest and most diverse microbiota after the gut, harboring > 770 species of bacteria (Kitamoto et al., 2020). A variety of microbial habitats in the oral cavity (e.g., teeth, buccal mucosa, soft and hard palate, and tongue) makes the ecologic system complex and attracts diverse microorganisms, called the oral microbiome, including bacteria, fungi, and viruses (Kilian, 2018). More than half of microbial species (e.g., Streptococcus and Veillonella) frequently detected in both sites showed evidence of oral–gut translocation, even in healthy individuals (Schmidt et al., 2019). Accumulating evidence indicates that resident oral bacteria can translocate to the gastrointestinal tract through hematogenous and enteral routes. The dissemination of oral microbes to the gut may exacerbate various gastrointestinal diseases, including irritable bowel syndrome, inflammatory bowel disease, and colorectal cancer (Kitamoto et al., 2020).

Meningitis-like infectious disease is a highly contagious and lethal disease for bullfrogs. Once infected, it brings great losses to farmers. In the farmed frogs, the characteristics of MID were anorexia, slow movements, corneal opacity, and a series of neurological symptoms including the onset of torticollis, indifference to stimuli, intermittent motion, and curling up of toes of the limbs. The diseased frogs in this study were lethargic, and most died within a few days. Dissection revealed lesions in multiple internal organs. Histopathological lesions in the liver, spleen, kidney, heart, brain, and muscle of the diseased frogs showed cell degeneration and necrosis (Hu et al., 2017). In these studies about MID, the main pathogens were basically determined to be bacteria of the genus Elizabethkingia (Lei et al., 2019). Relevant cases have been reported in bullfrogs (Mauel et al., 2002), Chinese spiny frogs (Quasipaa spinosa; Lei et al., 2019), black-spotted frogs (Hu et al., 2017; Chang et al., 2021), tiger frogs (Xie et al., 2009), adult northern leopard frogs (Lithobates pipiens), chapa bug-eyed frogs (Theloderma bicolor), Vietnamese warty toads (Bombina microdeladigitora), and Sabana Surinam toads (Pipa parva; Trimpert et al., 2021). This experiment started with gut and oral microbes to examine the differences in the gut and oral microbes between normal and diseased individuals so as to explore the causes of disease outbreaks in bullfrogs raised in Dayutan. The pathogenic-related bacterial communities were revealed through the differences between the biological groups, providing a theoretical basis for the prevention and control of MID in the later stage.

In the study, a total of 10 bullfrogs from Dayutan in Anhui Province in 2021 were prospectively collected. Samples are generally normal, half are sick, and the samples were selected according to the criteria: (1) All samples are from the same culture pond; (2) about the similar size; (3) age < 1 year old; (4) Breeding conditions were the same before collection; The bullfrog was dissected in a sterile environment, and its gut and oral microbiome samples were taken, 10 cases (gut and oral) of sick group (MID), 10 cases (gut and oral) of healthy control group (HC) were finally included in the study and received 16S rDNA amplicon sequencing.

Ten bullfrogs were dissected in a sterile environment, the intestines and orals were carefully scraped with a sterile swab, the swabs after sampling were marked, and the water samples were drawn with a 0.22 μm filter membrane in a sterile environment. After filtration, placed the filter membrane in a sterile Ep tube for marking. The genomic DNA of the sample was extracted by CTAB method (Miaomiao et al., 2008), and then the purity and concentration of the extracted DNA were detected by agarose gel electrophoresis.

PCR amplification of 16S rRNA gene sequences was performed by targeting the V3–V4 regions by using a set of forward and reverse primers according to a previous study (Chen et al., 2022). The sequences of forward and reverse primers used in this experiment were as follows: Pro_341F (5′-ACTCCTACGGGAGGCAGCA-3′) and Pro_806R (5′-GGACTACHVGGGTWTCTAAT- 3′; Nossa et al., 2010). The PCR was carried out in a total volume of 25 μl that contains 2 × KAPA HiFi HotStart Ready Mix (12.5 μl), 400 nM of each primer, ddH₂O (7.5 μl) and 3 μl of template DNA. The PCR reaction was carried out in a thermocycler (Px2 Thermal Cycler, Thermo, United States) under the following conditions: initial denaturation at 95°C for 30 s followed by 28 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s and elongation at 72°C for 30 s. The amplicons were confirmed by electrophoresis with 1.5% agarose gel under 110 V for 30 min. After separation using agarose gel electrophoresis, PCR products with expected sizes were purified from the matrix. The amplicons were subjected to sequencing using the pair-end method with the MiSeq Illumina platform (Illumina Inc., San Diego, CA, United States; Quail et al., 2008), following manufacturer instructions. The Nextera XT DNA Library Preparation Kit (Illumina) was used to construct libraries from the isolated DNA.

Using Cutadapt (V1.9.1) cut the low quality part of reads; the sample data were separated from the obtained reads according to Barcode, and the Barcode and primer sequences were truncated to obtain raw reads. The Reads sequence was compared with the species annotation database, the chimera sequence was detected and removed, and the Clean Reads were obtained. All the Clean Reads of all samples were clustered by Uparse software (Uparse v7.0.1001; Edgar, 2013). The sequences were clustered into Operational Taxonomic Units (OTUs), with 97% identity and the sequences with the highest frequency were taken as the representative sequences of OTUs according to the principle of algorithm. Then the species annotation of OTUs sequence was analyzed by Mothur method (Schloss et al., 2009) and SILVA132 SSUrRNA database (Set the threshold to 0.8–1; Apprill et al., 2015), and the taxonomic information was obtained.

The differences between groups were compared by calculating the α index (Observed-species, Chao1, Shannon, Simpson) of different groups with t-test. The relative abundance at the genus level was calculated. A Bray–Curtis dissimilarity matrix was calculated based on relative abundance of OTUs and used to perform non-metric multidimensional scaling (NMDS). We used the linear discriminant analysis (LDA) effect size (LEfSe) to identify significant associations between bacterial taxa and different groups. LEfSe can determine the taxonomic units most likely to explain differences between classes by coupling standard tests for statistical signifcancewith additional tests encoding biological consistency and effect relevance (LDA score > 2.0, p < 0.05).

High-throughput sequencing of the 16S rDNA gene V3–V4 region was performed in 20 samples to examine the structure of the gut and oral microbiome. The basic data statistics after sequencing are shown in Tables 1, 2. The accumulation curve analysis showed that the rarefaction curve of the gut (Figure 2A) and oral cavity (Figure 2B) tended to be flat, indicating that the amount of sequencing data was gradually reasonable, and more data would only produce a small number of new species (OTUs). Therefore, the sample size of the study was sufficient, the sequencing depth was up to the standard, and an additional sample size was not required. In the gut cavity, 135 OTUs were obtained; of 78 in the two groups, 45 OTUs were unique to the MID group, and 12 were unique to the HC group (Figure 2C). Further, 299 OTUs were obtained in the oral cavity; of 191 in the two groups, 66 OTUs were unique to the MID group, and 42 were unique to the HC group (Figure 2D).

In the oral cavity, the α indexes, observed species (p = 6.014 × 10⁻⁵), Chao1 (p = 1.237 × 10⁻⁵), Shannon (p = 0.001), and Simpson (p = 0.0099; Figures 3A–D), had significant differences. No significant difference was observed in the abundance and evolutionary distance of microorganisms between the HC and MID groups, but a significant difference was observed in the richness and evenness. In the gut cavity, the α indexes were significantly different between the HC and MID groups, including observed species (p = 1.079 × 10⁻⁸), Chao1 (p = 2.439 × 10⁻⁶), Shannon (p = 0.00013), and Simpson (p = 0.0004; Figures 3E–H). Except for Simpson and Shannon, the α indexes had very significant differences. This indicated a large difference in the richness and evenness of microorganisms in the gut cavity between the HC and MID groups.

The top 15 bacterial phyla in the HC and MID groups were evaluated by examining the changes in the gut and oral microbiota. The results showed that Proteobacteria was the most abundant phylum in the oral cavity in the HC group (Figure 4B), followed by Bacteroidota and Firmicutes. However, the Firmicutes abundance decreased significantly and the Bacteroidota abundance increased significantly in the MID group compared with the HC group. Only eight phyla of microorganisms were found in the gut (Figure 4A). The Firmicutes abundance significantly decreased and the Bacteroidota abundance significantly increased in the MID group, which was consistent with those in the oral cavity. At the genus level, Lactococcus was the dominant bacteria in the gut in the HC group (Figure 4C). However, almost no Lactococcus was found in the MID group, while the relative abundance of Flavobacterium and Acinetobacter increased significantly. The relative abundance of Lactococcus decreased sharply and the relative abundance of Flavobacterium and Acinetobacter increased significantly in the MID group, which was consistent with the abundance in the oral cavity (Figure 4D). Different from the oral manifestations, Flavobacterium and Acinetobacter were the dominant bacteria in the intestinal tract in the MID group.

We considered the relative abundances of the top 35 genera of the samples to determine the microbiota of high abundance. The microbiota of high abundance analysis revealed that Gracilibacteria, Hydrogenophaga, Undibacterium, Aeromonas, Chryseobacterium, Shewanella, Bacteroides, Stenotrophomonas, Ideonella, Flavobacterium, Pedobacter, Anoxybacillus, Brevundimonas, Enhydrobacter, Acinetobacter, and Elizabethkingia were the microbiota of high abundance in the gut in the MID group (Figure 5A). The microbiota of high abundance were Flectobacillus, Cloacibacterium, Acinetobacter, Elizabethkingia, Enhydrobacter, Cetobacterium, Bacteroides, and Aeromonas in the oral cavity (Figure 5B) in the MID group. Elizabethkingia, Enhydrobacter, Bacteroides, and Aeromonas were found in the oral cavity and gut. It was observed that the oral flora structure had changed during the disease process, and excessive reproduction of some pathogenic bacteria had inhibited the structure of the original healthy flora. The structure of the intestinal flora also changed in the gut tract due to the excessive reproduction of pathogenic bacteria. Further, the abundance of pathogenic bacteria overexpressed in the intestinal tract was much higher than that in the oral cavity in the HC group.

Non-metric multidimensional scaling (NMDS) showed the structural differences in the microbiota based on two groups. Bray–Curtis showed that gut (Figure 5A) and oral (Figure 5B) microbiota were differentially distributed in the MID and HC groups. Hence, the microbiota of both oral cavity and gut segments could be divided into two distinct groups, and the two groups displayed apparent differences (stress = 0.0012 and 0.0131, respectively).

In the gut, the LEfSe analysis showed that 16 microbial clades exhibited significant differences between the MID and HC groups (Figure 6A). In the HC group, Firmicutes and Bacilli were highly abundant. In the MID group, Bacteroidota had the highest LDA score. In the oral cavity, the LEfSe analysis showed that 14 microbial clades exhibited significant differences between the MID and HC groups (Figure 6B). In the HC group, Firmicutes and Bacilli were highly abundant. In the MID group, Bacteroidota had the highest LDA score (Figure 7).

In humans, there are many reports of disease and gut microbes. For example, chronic kidney disease (CKD) will reduce the richness, uniformity and uniformity of intestinal microorganisms (Loomba et al., 2017); Non-alcoholic fatty liver disease (NAFLD) will reduce the diversity of intestinal microorganisms α and β (Astbury et al., 2020). But we calculated the relative abundance of oral and gut microorganisms in normal and diseased frogs and we were found that the diversity, uniformity and richness of the flora in MID increased. It might be due to the decline in the immune function of the body declined after the pathogenic bacteria infected the body resulting in some conditional pathogenic bacteria in the water body, further infecting the body. Thus, the richness of the microbial community increased.

The 16S rRNA gene amplicon sequencing could be used to determine the genetic composition and community function of all microorganisms in environmental samples (Goldfeder et al., 2017). In our study, the results of 16S rRNA gene amplicon sequencing showed that the Chao1 indexes slightly increased in the HC group compared with the MID group, but with no significant difference. Previous studies showed that a balanced gut microbial structure was an important guarantee for the health of an organism. Once the gut microbiota is unbalanced, it causes a variety of diseases; the beneficial bacteria in the gut are the key to maintain the balance of the gut microbial population structure (Qi et al., 2019). In the HC group, the abundance of Bacteroidetes and Firmicutes accounted for 97% of the total bacteria. Lactococcus has been used in dairy fermentation for centuries. These Gram-positive, generally nonpathogenic, nonmotile, and nonsporulating bacteria are members of the Streptococcaceae family, which includes food, commensal, and virulent species. Many probiotics in this genus are beneficial to the survival of the host. Most widely used probiotic bacteria belong to the Lactobacillus and Bifidobacteria genera, but other microorganisms, such as Lactococcus and Enterococcus, are also used as the components of probiotic preparations (Kim et al., 2019).

Currently, the reports only clarified that Elizabethkingia was a pathogenic bacteria, but did not explain the structural and functional changes in microorganisms after infection. In this study, the relative abundance of Elizabethkingia was significantly higher in the MID group than in the HC group. Our study showed that the abundance of Elizabethkingia increased significantly in the MID group compared with the HC group. Many studies showed various pathogenic bacteria of the genus Elizabethkingia, which could cause diseases in amphibians, reptiles, and even humans. Elizabethkingia meningoseptica was detected in tiger frogs with cataracts (Monteagudo-Mera et al., 2011), suggesting that it could cause disease in tiger frogs. Its symptoms are the same as those of the sick frogs in this study. E. meningoseptica has been reported to cause severe sepsis in humans (Xie et al., 2009). Cases of E. meningoseptica causing patient shock have also been reported, suggesting that species in this genus cause disease in humans. The genus Elizabethkingia has six species, two of which have been reported to be pathogenic to organisms; meningitis-like symptoms occur in both cases. Amphibians and mammals are infected by the bacteria, but no infection in reptiles has been reported. It is believed that the bacteria may also infect species similar to crocodiles and other aquatic reptiles, which will be the focus of future investigations. However, in this study, the lack of basic information such as body weight and blood routine led to the inability to analyze the differences in physiological indicators between MID and HC. In future studies, we should pay attention to recording basic data to make the research content more complete.