Next Article in Journal
Defining Short-Term Accommodation for Animals
Previous Article in Journal
Enteric Methane Emissions in Dairy Cows with Different Genetic Groups in the Humid Tropics of Costa Rica
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 13
Issue 4
10.3390/ani13040731
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Characteristics of Microbiota in Different Segments of the Digestive Tract of Lycodon rufozonatus

by
Yongquan Shang
1,†,
Huaming Zhong
2,†,
Gang Liu
1,
Xibao Wang
1,
Xiaoyang Wu
1,
Qinguo Wei
1,
Lupeng Shi
1 and
Honghai Zhang
1,*
1
College of Life Sciences, Qufu Normal University, Qufu 273165, China
2
College of Biology and Food, Shangqiu Normal University, Shangqiu 476000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Animals 2023, 13(4), 731; https://doi.org/10.3390/ani13040731
Submission received: 20 December 2022 / Revised: 14 February 2023 / Accepted: 15 February 2023 / Published: 17 February 2023
(This article belongs to the Section Herpetology)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Microorganisms are widely distributed in the skin, respiratory tract, digestive tract, external auditory tract, and urogenital tract of animals, with the highest microbiota abundance in the digestive tract. Considering the vital role of intestinal microbiota in nutrient metabolism, immunity, neuroregulation, and curing of disease, animal intestinal microbiota have become a research hotspot in conservation biology. The intestinal microbiota of mammals are dominated by Firmicutes and Bacteroidetes. The avian gut microbiota are similar to those of mammals at the phylum level and primarily consist of Bacteroidetes, Firmicutes, and Proteobacteria. Proteobacteria and Firmicutes dominated the bacterial communities of the insect gut. However, relatively little research has been conducted on reptiles, particularly snakes. In this study, the composition of the intestinal microbial community and its ecological adaptation were investigated in the different segments of the gastrointestinal tract of Lycodon rufozonatus specimens. We found that microbial diversity was higher in the stomach than that in the small and large intestines, and the microbes were involved in metabolic processes. Our results provide insights into the comprehensive understanding of the evolution and ecology of snakes and the formulation of conservation measures for these animals.

Abstract

The gastrointestinal tract of animals contains microbiota, forming a complex microecosystem. Gut microbes and their metabolites can regulate the development of host innate and adaptive immune systems. Animal immune systems maintain intestinal symbiotic microbiota homeostasis. However, relatively few studies have been published on reptiles, particularly snakes, and even fewer studies on different parts of the digestive tracts of these animals. Herein, we used 16S rRNA gene sequencing to investigate the microbial community composition and adaptability in the stomach and small and large intestines of Lycodon rufozonatus. Proteobacteria, Bacteroidetes, and Firmicutes were most abundant in the stomach; Fusobacteria in the small intestine; and Proteobacteria, Bacteroidetes, Fusobacteria, and Firmicutes in the large intestine. No dominant genus could be identified in the stomach; however, dominant genera were evident in the small and large intestines. The microbial diversity index was significantly higher in the stomach than in the small and large intestines. Moreover, the influence of the microbial community structure on function was clarified through function prediction. Collectively, the gut microbes in the different segments of the digestive tract revealed the unique features of the L. rufozonatus gut microbiome. Our results provide insights into the co-evolutionary relationship between reptile gut microbiota and their hosts.

1. Introduction

Interactions between the intestinal microbes and the genomes of vertebrate hosts facilitate adaptation to extreme or new environments [1,2,3]. The gut microbes and their respective animal hosts have co-evolved over a long period to achieve equilibrium and reciprocity [4,5,6]. However, most studies on the digestive tract microbiome have focused on mammals [7,8,9], fish [10], and birds [11,12]. For example, the clustering behavior of the steppe vole (Lasiopodomys brandtii) can increase the richness and diversity of its gut microbiota, reduce the abundance of harmful bacteria, and increase the content of beneficial bacteria and metabolites, such as short-chain fatty acids, which provide energy sources for the host. This suggests that the gut microbiota–gut-brain system can regulate the adaptability of animals to cold environments [8]. Zhang et al. (2016) studied the gut microbes of yak (Bos grunniens), Tibetan sheep (Ovis aries), cattle (Bos taurus), and sheep (Ovis aries) and found that gut microbes co-evolve to help their host adapt to the extreme plateau environment [13]. Xiao et al. (2021) revealed that zebrafish (Danio rerio) adapt to environmental changes by controlling the ecological succession of the gut microbiota during development [14,15]. However, studies on the microbes in the digestive tracts of reptiles are relatively limited. Zhang et al. (2021) found that Bacteroidetes, Firmicutes, and Proteobacteria were the three primary phyla in the gut microbiota [16] and that the diet and lifestyle could affect the gut microbiota in an oviparous lizard (Calotes versicolor) of different genders [17]. In addition, Montoya-Ciriaco et al. (2020) found that the diet had an effect on the intestinal microorganisms of the mesquite lizard (Sceloporus grammicus) (Wiegmann, 1828) at different altitudes [18].
Among vertebrates, the snake’s ability to endure food scarcity may be the key to its survival [19]. Snakes have extreme fasting cycles; some can fast for up to a year. They can simultaneously regulate energy supply and demand during food shortages, with extreme predation flexibility and a long hunger tolerance time [20]. After ingesting their prey, snakes can immediately recover their ability to digest and absorb food; this requires efficient adaptation on the part of the snake as well as its gut microbes. Studies have revealed that animals with different dietary habits have varied gut microbiota compositions [21]. The microbial diversity of herbivores, omnivores, and carnivores decreases from high to low [22]. In recent years, there has been considerable research on the gut flora of carnivores but relatively little research on snakes. Snakes are strict carnivores; their method for food processing is simple, that is, swallowing food wholly [23]. Therefore, the gastrointestinal microbes of snakes may have a unique community structure and function and co-operate with the host to adapt to their carnivorous feeding methods.
The characteristics of the intestinal microbial structure of animals can provide a theoretical basis for disease diagnosis, which is crucial for the conservation and management of endangered species [24,25,26]. Lycodon rufozonatus belongs to the subfamily Colubridae and genus Dinodon, which are widely distributed in most parts of China [27]. It is listed as a near-threatened and vulnerable species in the 2013 IUCN Red List of Threatened Species and the Chinese Biodiversity Red List [28,29]. Therefore, studying the composition, diversity, and phylogeny of the intestinal microbiota of this species is necessary to develop comprehensive protection measures. In addition, physiological changes, including the chemistry and nutrient levels, have different effects on bacterial community composition, which means that stool samples do not accurately reflect the entire microbial components of the digestive tract or the microbial structure and function in different segments of the digestive tract [30].
In the present study, we used the high-throughput sequencing to analyze the structure and potential functions of bacterial communities in the small and large intestines of L. rufozonatus. These results enrich our understanding of the snake gut microecology and their adaptive co-evolution with the host.

2. Materials and Methods

2.1. Sample Collection

L. rufozonatus was collected from the Beicheng snake and scorpion-breeding farm in Weifang, China, in November 2019. Fifteen healthy adult snakes of the same body length raised under the same environmental conditions were screened. No antibiotics or other drugs that can affect the intestinal microbes were administered to the snakes for 2 months before collection. After 30 days of fasting, a dose of lidocaine hydrochloride was injected into the brain for euthanasia, immediately after which an abdominal incision was made and the digestive tract was removed. The digestive tract contents were collected via sterile swabs, transferred into sterile vials, immediately frozen in liquid nitrogen, and stored at −80 °C until DNA extraction.

2.2. DNA Extraction and Polymerase Chain Reaction (PCR) Amplification

DNA was extracted using a QIAamp® Fast DNA Stool Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocol. We amplified the V3–V4 region of the bacterial 16S rRNA genes. PCR amplification was performed using the bacterial universal primers 341F and 806R. PCRs were performed in triplicate, with each 30 μL reaction mixture containing 15 µL of Phusion Master Mix (2×), 1.5 μL of each primer (2 μM), and 10 μL of template DNA. The PCR amplification conditions were 98 °C for 3 min, followed by 35 cycles of 98 °C for 45 s, 50 °C for 30 s, and 72 °C for 30 s, and a final extension at 72 °C for 7 min. To ensure a high efficiency and accuracy of amplification, the PCR products were detected using 2% agarose gel electrophoresis. According to the PCR products obtained, the target bands were recovered using a gel recovery kit provided by QIAquick® Gel Extraction Kit (QIAGEN).

2.3. Library Construction and High-Throughput Sequencing

A TruSeq DNA PCR-free sample preparation kit (Illumina, San Diego, CA, USA) was used to construct libraries, which were then quantified using Qubit and Q-PCR. Subsequently, Illumina NovaSeq 6000 (PE250; Illumina) was used for on-machine sequencing.

2.4. Sequencing Data Processing

Data from each sample were separated from the off-machine data according to the barcode sequence and PCR primer sequences. FLASH software (V1.2.7, http://ccb.jhu.edu/software/FLASH/, accessed on 24 February 2020) was used to splice the reads [31]. The sequences obtained were raw tags [32], which were processed via strict filtering to obtain high-quality tag data (clean tags) as described in QIIME (V1.9.1, http://qiime.org/scripts/split_libraries_fastq.html, accessed on 24 February 2020) [33]. The clean tag sequences were compared with the VSEARCH (https://github.com/torognes/vsearch/, accessed on 24 February 2020) database to detect chimeric sequences [34]. Finally, chimeric sequences were removed to obtain the final effective tags.

2.5. Operational Taxonomic Unit Clustering and Species Annotation

Operational taxonomic units (OTUs) with ≥97% similarity were used to cluster the effective tags using the UPARSE (V7.0.1001) pipeline [35]. Mothur software was used for comparison with the SSU rRNA database of SILVA (http://www.arb-silva.de/, accessed on 25 February 2020) to obtain species annotation information [36,37]. Multiple sequence alignments of the OTUs were performed using MUSCLE (http://www.drive5.com/muscle/, accessed on 25 February 2020) software to generate evolutionary trees [38].

2.6. Diversity Analysis

Species diversity and complexity were calculated using QIIME software (V1.9.1) [33]. Rarefaction curves were drawn using R software (V2.15.3) (R Foundation for Statistical Computing, Vienna, Austria). For the alpha diversity analysis, the Wilcoxon rank-sum and Tukey’s tests were used to compare the significance of the differences between groups. The UPGMA sample clustering tree was constructed using the QIIME software (V1.9.1), and the nonmetric multidimensional scaling (NMDS) of the Bray–Curtis distances was generated using the vegan package (V2.15.3) of R software. The analysis of molecular variance tests was conducted using Mothur software. R software was used to perform t-tests between groups and plot the species with significant differences.

2.7. Indicator Species Analysis

Linear discriminant analysis (LDA) (effect size score > 4) and MetaStats analysis were used to explore the microbiota with significant differences among the groups [39]. Metastatic analysis was performed using R software at the phylum and genus levels. The p-values were obtained using a permutation test between groups and then modified using the Benjamini and Hochberg false discovery rate method to obtain the q-values [40].

2.8. Function Prediction

Trees based on the OTUs were generated using the Greengene database; Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to map the sequencing composition into a database to predict the metabolic function of the microbiota.

3. Results

3.1. Sequencing of the Microbiota in the Gastrointestinal Tract of L. rufozonatus

In the study, we collected 15 L. rufozonatus samples from three gut segments (stomach, small intestine, and large intestine) under fasting conditions. Owing to the low gastrointestinal content in snakes during sampling, 14 samples (four from the stomach, three from the small intestine, and seven from the large intestine) were successfully collected and sequenced using the Illumina NovaSeq 6000 sequencing platform. After data quality control, 1,062,719 clean tags were obtained (278,089 for the stomach, 218,229 for the small intestine, and 566,331 for the large intestine). After chimera filtering, 873,406 effective tags were obtained for subsequent analysis (including 244,838, 186,950, and 441,618 tags for the stomach, small intestine, and large intestine, respectively). The average sequence length was 409.4 bp, which covered the full length of the V3–V4 region. The effective sequence of all samples was 67.82 ± 4.75%, which met the criteria for the follow-up statistical analysis (Table 1). As shown by the rarefaction curve (Figure S1), when the amount of sequencing data reaches 30,000 reads, the sample curve gradually flattens and finally becomes stable; this indicates that the amount of sequencing reflects the diversity of the microbiome, thereby ensuring the reliability of the subsequent analysis.

3.2. Bacterial Diversity in the Gastrointestinal Tract

The alpha diversity indices of the different samples were calculated (Table S1, Figure 1). Among them, the goods-coverage results were 99%, indicating that sufficient 16S rRNA gene sequences were extracted from the intestine of L. rufozonatus to optimally evaluate bacterial diversity. The average number of observed species in the stomach (1689.00 ± 323.21) was significantly higher (p < 0.01) than that in the small (525.00 ± 202.01) and large intestines (429.14 ± 149.56). However, no significant difference was observed in species number between the small and large intestines (p > 0.05). The Shannon index of the stomach was significantly higher (p < 0.01) than that of the small and large intestines; similarly, no significant difference was found between the large and small intestines (p > 0.05). The Simpson’s index of the stomach and large intestine was significantly higher (p < 0.01) than that of the small intestine, indicating the highest microbial diversity in the stomach and the lowest in the small intestine. The average level of the phylogenetic diversity whole-tree index in the stomach was significantly higher (p < 0.01) than that in the small and large intestines.
The β diversity of gut microbial composition was evaluated by NMDS and Venn diagram of the gut microbiota (Figure 2). NMDS analysis revealed significant differences in the microbial composition among the different segments based on the Bray–Curtis distance (Figure 2a). To explore the commonality and particularity of the microbes, Venn diagrams were used to calculate the number of OTUs shared by the different segments. We found that the number of core OTUs shared by the three segments was 518, whereas the number of unique OTUs in the stomach group was the largest (2881) (Figure 2b).

3.3. Bacterial Composition and Structure of the Gastrointestinal Tract

To study the species composition of each sample, the effective tags of all samples were clustered by OTU with 97% similarity. The OTU sequences were annotated with 36 phyla, 55 classes, 118 orders, 223 families, 517 genera, and 390 species. At the phylum level, all segments had high abundances of Proteobacteria, Firmicutes, Fusobacteria, Bacteroidetes, and Actinobacteria [41] (Figure 3a). Among the different segments, the stomach had a relatively high abundance of Proteobacteria (39.75 ± 14.56%), Bacteroidetes (20.32 ± 8.32%), Firmicutes (15.92 ± 4.53%), Actinomycetes (9.18 ± 9.90%), and Fusobacteria (3.21 ± 2.76%). The small intestine had a high abundance of Fusobacteria (63.41 ± 3.77%), Bacteroidetes (16.04 ± 11.27%), Proteobacteria (13.18 ± 5.23%), Firmicutes (6.25 ± 2.57%), and Actinomycetes (0.18 ± 0.12%%). Finally, the large intestine had a high abundance of Proteobacteria (38.90 ± 9.28%), Bacteroidetes (27.56 ± 12.15%), Fusobacteria (20.63 ± 7.11%), Firmicutes (10.72 ± 3.05%), and Actinomycetes (1.05 ± 0.50%). The bacterial abundance in the different sections of the gastrointestinal tract varied greatly, showing spatial heterogeneity (Figure 3b). As shown in Figure 3, the dominant microbiota of the stomach and large intestine were similar, primarily from the phyla Proteobacteria, Bacteroidetes, and Firmicutes. The stomach was specifically enriched with Actinomycetes compared to the other two segments; however, the small intestine contained more Fusobacterium species than the other segments.
At the genus level, 517 genera were identified in all three segments (Figure 4). The top 10 bacterial genera with the highest abundance in the stomach were as follows: other (34.74 ± 13.18%) Bacteroides (12.50 ± 11.00%), Delftia (9.85 ± 13.16%), Sphingomonas (7.03 ± 3.26%), Cetobacterium (4.81 ± 2.74%), unidentified Clostridiales (4.14 ± 2.82%), Aeromonas (3.88 ± 2.22%), Chryseobacterium (2.69 ± 1.96%), Lactobacillus (2.45 ± 0.48%), and Methanosaeta (2.16 ± 4.29%). The top 10 most abundant genera in the small intestine were Cetobacterium (61.15 ± 4.79%), Bacteroides (12.65 ± 10.75), other (9.38 ± 4.30%), Citrobacter (2.32 ± 3.61%), Fusobacterium (2.26 ± 1.04%), Aeromonas (1.72 ± 0.84), Odoribacter (1.62 ± 0.95%), unidentified Enterobacteriaceae (1.59 ± 2.30%), unidentified Erysipelotrichaceae (1.58 ± 0.89%), and Brevundimonas (0.85 ± 1.37%). The top 10 most abundant genera in the large intestine were Bacteroides (25.01 ± 13.58%), Cetobacterium (19.85 ± 6.99%), other (17.71 ± 6.95%), Citrobacter (13.94 ± 8.35%), Aeromonas (11.54 ± 4.08%), Providencia (3.89 ± 2.06%), unidentified Erysipelotrichaceae (2.71 ± 1.46%), Helicobacter (0.78 ± 0.76%), Shewanella (0.77 ± 0.51%), and Fusobacterium (0.77 ± 0.31%) (Table 2). The above data show, according to abundance, that the dominant genera in the stomach were not apparent; however, those in the intestines were more evident. Specifically, Cetobacterium and Bacteroides were dominant in the small intestine, whereas Bacteroides, Cetobacterium, Citrobacter, and Aeromonas were abundant in the large intestine.

3.4. Differences in the Gastrointestinal Microbiome across Segments

The MetaStat method was used to draw a heat map of species abundance data between groups at the phylum and genus levels to show the abundance and significance of different species in the different segments of the gastrointestinal tract. The abundances of Choroflexi, Proteobacteria, and Fusobacteria in the large intestine were significantly higher than those in the small intestine (p < 0.05); the abundance of Choroflexi in the stomach was significantly higher than that in the large intestine (p < 0.01); the abundance of Fusobacteria in the small intestine was significantly higher than in the stomach and large intestine (p < 0.01); and the abundance of Bacteroidetes in the large intestine was significantly higher than that in the stomach and small intestine (p < 0.05) (Figure 5a). At the genus level, Chryseobacterium, Sphingomonas, unidentified Clostridiales, Lactobacillus, and Caulobacter were significantly more abundant in the small and large intestines (p < 0.05) than in the stomach in this instance. Odoribacter, Fusobacterium, and Cetobacterium were significantly more abundant in the small intestine than in the stomach and large intestines (p < 0.05). The abundance of Citrobacter, Providencia, Aeromonas, and Erysipelothrix in the large intestine was significantly higher than that in the stomach and small intestine (p < 0.05). The abundance of Helicobacter and Erysipelotrichaceae in the large intestine was significantly higher than that in the stomach, whereas that of Microvirgula, Shewanella, and Plesiomonas in the large intestine was significantly higher than that in the small intestine (Figure 5b).
LDA was used to compare the communities that could be considered different biomarkers. Specifically, 38 biomarkers were identified for the stomach, 7 for the small intestine, and 11 for the large intestine (Figure 6). The most differentially abundant bacteria in the stomach were Lactobacillus and Clostridia in Firmicutes, Sphingomonas and Delftia in Proteobacteria, Corynebacterium and Micrococcaceae in Actinomycetes, and Methanosaeta in Euryarchaeota. In contrast, those in the small intestine were Cetobacterium and Fusobacterium spp. The species biomarkers in the large intestine primarily comprise the genera Aeromonas, Citrobacter, Providencia, and those belonging to the family Lachnospiraceae.

3.5. Microbial Function Prediction

To study the possible functional pathways of bacterial microbiota in various segments of the gastrointestinal tract, functional prediction was carried out using the PICRUSt software (version (1.0.0), created by Lingille et al., in 2013, USA) based on the KEGG database. A heat map of functional clustering at level two is shown in Figure 7. The most abundant functions in the stomach include the metabolism of terpenoids and polyketides, cancers, metabolism of other amino acids, xenobiotic biodegradation and metabolism, amino acid metabolism, lipid metabolism, neurodegenerative diseases, transport, and catabolism. The most common functions in the small intestine include signaling molecules and interactions, the nervous system, metabolism of cofactors and vitamins, environmental adaptation, translation, nucleotide metabolism, replication, and repair. The most common functions in the large intestine involve transcription, infectious diseases, cellular processes and signaling, and membrane transport. The small and large intestines were observed to cluster together, as their functional pathways have partially similar color modules. The corresponding functions of these modules, such as glycan biosynthesis and metabolism, immunity, metabolism, and enzyme families, had similar positions in the two segments of the intestine. The functional pathways of the microbiota in the stomach were remarkably different from those in the small and large intestines.
The significance of the functional differences at level two of the gastrointestinal tract was detected using t-tests based on the database annotation results (Figure 8). The results showed, at level two, 18 pathways significantly differed among the stomach, small intestine, and large intestine, among which 8 and 5 pathways were involved in metabolism, respectively. In total, 3 of the 12 functional pathways with significant differences between the small and large intestines were related to metabolism.

4. Discussion

Vertebrates have a close and complex relationship with the microbial communities in their gastrointestinal tract [42,43,44,45]. The typical composition and structure of the microbiota can reflect the function of the gastrointestinal tract and can be used to evaluate the health state or diagnose [46]. Previous studies have revealed that the gut microbiota of wild mice promote host fitness and improve disease resistance [47]. As in other vertebrates, microbes in different parts of the digestive tract play an important role in the growth of snakes. Therefore, studying snake intestinal microbes has become an important element in snake conservation research. In this study, we conducted the first comprehensive analysis of the gastrointestinal microbiota of L. rufozonatus using high-throughput sequencing technology and revealed changes in the community structure and function of the intestinal microbiota in the different segments of its digestive tract.
The dominant gut microbiota in snakes vary greatly across species. Previous studies have revealed that the dominant phyla in the gut of Crotalus horridus are Proteobacteria and Firmicutes; however, the abundance of Proteobacteria in Crotalus scredus is as high as 85.0% [48]. Bacteroidetes, Firmicutes, and Proteobacteria are the dominant bacterial phyla in the intestinal tract of Agkistrodon piscivorus [49]. The five most abundant phyla in Deinagkistrodon acutus are Bacteroidetes, Proteobacteria, Firmicutes, Fusobacteria, and Actinobacteria [50]. Naja atra, Ptyas mucosus, and Elaphe carinata have the same dominant microbiota as Deinagkistrodon acutus, which were Bacteroidetes, Proteobacteria, Firmicutes, Fusobacteria, and Actinobacteria [50]. Similar microbiota structures have been found in the oviparous lizard (Calotes versicolor) and marine iguana (Amblyrhynchus cristatus), with Firmicutes, Bacteroidetes, and Proteobacteria as the dominant phyla in their gut microbiota [16,51]. In this study, we found that the phyla with high abundance in all the segments of the gastrointestinal tract of L. rufozonatus were Proteobacteria, Firmicutes, Fusobacteria, Bacteroidetes, and Actinobacteria. Specifically, Proteobacteria, Bacteroidetes, and Firmicutes were predominant in the stomach; Fusobacteria was the most abundant in the small intestine; and Proteobacteria, Bacteroidetes, Fusobacteria, and Firmicutes were dominant in the large intestine. Although there are differences in the core phyla found in the different segments, the results of studies on the dominant intestinal microbiota of Crotalus horridus, Agkistrodon piscivorus, Naja atra, Ptyas mucosus, Calotes versicolor, and Amblyrhynchus cristatus are similar [16,48,49,50,51]. However, Bacteroidetes and Firmicutes are the dominant phyla in the intestinal tract of mammals; however, the dominant phyla in non-passerine birds are Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes, reflecting the influence of phylogenetic relationships on the intestinal microorganisms in animals [21,52].
The different segments of the gastrointestinal tract of snakes are highly specialized regions in which the symbiotic bacteria of the stomach and small intestine play an important role in digestion and absorption, respectively. Significant differences were observed in the distribution and structure of the bacterial communities between the stomach and small and large intestines. The alpha diversity index showed that the bacterial diversity and abundance in the stomach were significantly higher than those in the large and small intestines. Snakes are carnivores that devour whole prey, including fur, feathers, bones, and even undigested food from their prey’s gut [53]. As digestion in snakes is relatively slow, the food remains in their stomach for a long time. The abundance of bacteria in the stomach of L. rufozonatus was higher than that in the intestinal segments, consistent with a study on herbivorous Bactrian camels [54]. Camels retain food particles in the rumen considerably longer than other large herbivores. Therefore, we speculated that the alpha diversity of the digestive tract microbes is related to the rate of digestion.
Previous studies have revealed that Proteobacteria and Firmicutes are dominant in the stomach, small intestine, and colon of C. horridus, and their primary metabolic pathway is carbohydrate metabolism [48]. In A. piscivorus, Bacteroidetes is the dominant phylum in the large intestine, whereas Proteobacteria is dominant in the small intestine and cloaca [49]. In contrast, the gut microbiota of Burmese pythons primarily consist of the members of Bacteroidetes and Firmicutes [55]. In addition, Bacteroides are predominant in the large intestine of Burmese pythons during fasting; however, Firmicutes show an overall increase in richness and diversity during digestion [55]. In this study, we found significant differences in the structure of the microbiota in different segments of the gastrointestinal tract, which we speculate was caused by predation on different species. Burmese pythons feed on large prey, such as small mammals and rodents, whereas L. rufozonatus feed on frogs, lizards, and fish. Therefore, the type of food plays an important role in intestinal microbe composition.
Proteobacteria is the dominant phylum in the stomach and large intestine but is also present in the small intestine. Proteobacteria accounts for a large proportion of the intestinal microbiota in reptiles. The abundance of Proteobacteria in the guts of Amblyrhynchus cristatus, Liolaemus parvus, Liolaemus ruibali, and Phymaturus williamsi ranges from 19.1% to 56.4% [51,56]. The abundance of Proteobacteria in the gut of Burmese pythons is 10.1% [55]. The predominance of Proteobacteria in the gut has also been observed in red-crowned cranes (Grus japonensis) [57]. Based on this, some studies have proposed that the gut microbiota of snakes are more similar to those of birds than mammals, which indicates that phylogenetic relationships may play a role in shaping the gut microbiota [49].
The composition of intestinal microbiota plays an important role in the digestion and absorption of L. rufozonatus. Proteobacteria are facultative anaerobic bacteria that typically degrade and ferment complex sugars. Among these, Escherichia may also help the host obtain vitamins [58]. In a healthy gut, Proteobacteria fight infection or inflammation and play a protective role in the immune response [59]. However, Proteobacteria enrichment in the human gut is an indicator of gut microbiota imbalance and is associated with host disease [59]. In addition, the enrichment of Proteobacteria in the gut may indicate an unbalanced and unstable microbial community structure or a diseased state [59]. Aeromonas, a genus of Proteobacteria, is found in the stomach and small and large intestines. The genus Aeromonas comprises strict aerobes or facultatively anaerobic bacteria that produce acids by breaking down carbohydrates [60]. This genus is typically associated with aquatic environments and is spread via food, people, and animals that come into contact with water [60]. We speculate that the widespread colonization of Aeromonas in the gastrointestinal tract of L. rufozonatus is consistent with its ecological niche, which is aquatic, as it feeds on frogs, lizards, and fish [61].
In addition, Citrobacter, another genus in Proteobacteria, accounts for a proportion of bacteria in the small (2.32%) and large intestines (13.94%). This group of bacteria comprises facultative anaerobes, which are pathogenic to animals and can cause enteritis [62]. Citrobacter belongs to the Enterobacteriaceae family, and its enrichment is related to the carnivorous diet of the host [63]. Studies have found that common kestrels (Falco tinnunculus), which are strict carnivores, have a high abundance of Enterobacteriaceae in their gut [64]. The food structure of carnivores is high in fat and protein; therefore, as an obligate carnivore, L. rufozonatus also has a microbial lineage unique to carnivores.
Bacteroidetes was the second most abundant phylum (16.04–27.56%) in each segment in the gastrointestinal tract of L. rufozonatus. Bacteroides is the most abundant bacterial genus in the stomach and large intestine and the second most abundant in the small intestine. Bacteroides is the most common species of microbial communities in the intestines of timber rattlesnakes (Crotalus horridus), cottonmouths (Agkistrodon piscivorus), and American freshwater alligators (Alligator mississippiensis) [48,49,65]. The genus Bacteroides comprises strict anaerobes that can decompose polysaccharides and improve nutrient utilization, thereby promoting digestion and increasing the utilization of complex carbohydrates [66]. It may also contribute to the development of the host intestinal mucosa and immune system and maintain the balance of the intestinal microecology [58,67]. Therefore, Bacteroides species is essential for both carnivorous and herbivorous diets.
Fusobacteria is highly abundant in the small intestine of L. rufozonatus, similar to that in the American freshwater alligators (Alligator mississippiensis) [65]. In contrast, those abundant in other vertebrates are Bacteroidetes, Firmicutes, and Proteobacteria [21,55,68]. Fusobacteria comprises predominantly anaerobic Gram-negative bacilli that metabolize carbohydrates to produce butyrate and provide energy to the intestinal cells [69,70]. Fusobacteria is abundant in humans, and it plays a key role in biofilm development [71]. In addition, Fusobacteria is also involved in the metabolism of amino acids, which may affect the host’s ability to degrade proteins, as reported in the studies of the gut microbiota of alligators (Alligator mississippiensis) [65], black vultures (Coragyps atratus), turkey vulture (Cathartes aura) [72], catfish (Ictalurus punctatus), largemouth bass (Micropterus salmoides), and bluegills (Lepomis macrochirus) [73]. Studies on microbes in the digestive tract of alligators suggest that Fusobacteria may function during the development of digestive organs and the acquisition of nutrients, whereas, in mammals, Firmicutes and Bacteroidetes perform similar functions [65]. Among the Fusobacteria, Cetobacterium (61.15%) has the highest abundance in the small intestine and the second highest abundance in the large intestine (19.85%). This is similar to the dominant genera in the microbiomes of carnivorous fish [74], as studies have revealed that Cetobacterium and Halomonas are the main microflora of carnivorous fish [74,75]. These results further indicate that the structure of the intestinal microbiota of L. rufozonatus may be closely related to its diet. We speculated that the accumulation of microbes in the snake’s stomach is mainly related to the position of the snakes in the natural food chain. L. rufozonatus preys on fish, leading to the accumulation of specific microbes in the gut of fish.
By comparing the microbial functions of the different segments of the digestive tract, we found that the microorganisms in the stomach are involved in more metabolic processes than those in the intestine. Specifically, the significantly enriched pathways are almost all involved in metabolism, typically those related to amino acids, lipids, terpenoids, and polyketides, and xenobiotic biodegradation. This suggests that the microbiota are important in the construction of the metabolic capacity of the host and are most pronounced in the stomach. These results are also similar to previous studies showing that different intestinal morphologies and physiological and biochemical environments are more likely to influence metabolic function than the diet [22,76]. Although the PICRUSt algorithm has high accuracy, it does not represent absolute accuracy in predicting gene function. Therefore, it will be necessary to use a metagenomic approach in subsequent studies to explore the metabolic effects of the microbiota in the different segments of the digestive tract of L. rufozonatus.

5. Conclusions

To understand the relationship between digestive tract microbiota and the nutrition and health of L. rufozonatus, we aimed to provide a reference for conserving this species. In this study, we used high-throughput sequencing technology and 16S rRNA sequencing to reveal the structure and distribution of bacterial microbiota in different segments of the gastrointestinal tract of L. rufozonatus for the first time. The results showed that the microbiota structure was spatially heterogeneous across the different segments of the gastrointestinal tract. At the phylum level, Proteobacteria, Bacteroidetes, and Firmicutes were predominant in the stomach; Fusobacteria was predominant in the small intestine; and Bacteroidetes and Firmicutes were predominant in the large intestine. The dynamic distribution of Proteobacteria and Fusobacteria was evident in the different segments of the digestive tract of L. rufozonatus. In addition, the prevalence of Aeromonas in the gastrointestinal tract may be related to the fact that L. rufozonatus lives in or near aquatic environments and preys on fish. Functional prediction indicated that the microbiota are important in the metabolic capacity of the host. Overall, our findings provide a basis for understanding the complex co-evolutionary relationship between gut microbes and their hosts and will provide an important reference for a comprehensive understanding of the evolution and ecology of snakes and in formulating measures to conserve them.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani13040731/s1. Figure S1: Rarefaction analysis for the assessment of OTU coverage. Rarefaction curves for different samples (a) and segments (b). Table S1: Statistical analyses of the alpha diversity.

Author Contributions

Data curation, Y.S.; Formal analysis, Y.S., H.Z. (Huaming Zhong), Q.W. and L.S.; Writing—original draft, Y.S. and H.Z. (Huaming Zhong); Writing—review and editing, Y.S.; Project administration, Y.S., H.Z. (Huaming Zhong), G.L., X.W. (Xibao Wang), X.W. (Xiaoyang Wu) and H.Z. (Honghai Zhang); Funding acquisition, H.Z. (Honghai Zhang). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32270444, 32001228, 31872242, and 32070405), the Youth Innovation Team of Shandong Universities (2022KJ177), the China Postdoctoral Science Foundation (2022M711884), the Shandong Postdoctoral Innovation Project (SDCX-ZG-202203027), the Shandong Provincial Natural Science Foundation (ZR2021YQ22), and the Shandong Province Colleges and Universities Youth Innovation Science and Technology Teams Support Program (2021KJ015).

Institutional Review Board Statement

The animal study protocol was approved by the Qufu Normal University Institutional Animal Care and Use Committee (protocol code: QFNU2019-005, approved on 15 May 2019).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated for this study can be found in the SRA database of the NCBI database (Accession Number: PRJNA892747).

Acknowledgments

We thank the staff of the Beicheng snake and scorpion breeding farm for helping with sample collection. We also thank Novogene Co., Ltd. for their assistance in sequencing.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, W.; Qi, Y.; Wang, X.; Shi, X.; Chang, L.; Liu, J.; Zhu, L.; Jiang, J. Multi-Omics Approaches Revealed the Associations of Host Metabolism and Gut Microbiome with Phylogeny and Environmental Adaptation in Mountain Dragons. Front. Microbiol. 2022, 13, 913700. [Google Scholar] [CrossRef] [PubMed]
  2. Zhu, L.; Wang, J.; Bahrndorff, S. Editorial: The Wildlife Gut Microbiome and Its Implication for Conservation Biology. Front. Microbiol. 2021, 12, 697499. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, X.; Shang, Y.; Wei, Q.; Wu, X.; Dou, H.; Zhang, H.; Zhou, S.; Sha, W.; Sun, G.; Ma, S.; et al. Comparative Analyses of the Gut Microbiome of Two Fox Species, the Red Fox (Vulpes vulpes) and Corsac Fox (Vulpes corsac), that Occupy Different Ecological Niches. Microb. Ecol. 2022, 83, 753–765. [Google Scholar] [CrossRef] [PubMed]
  4. Ji, Y.; Sun, S.; Goodrich, J.K.; Kim, H.; Poole, A.C.; Duhamel, G.E.; Ley, R.E.; Qi, L. Diet-induced alterations in gut microflora contribute to lethal pulmonary damage in TLR2/TLR4-deficient mice. Cell Rep. 2014, 8, 137–149. [Google Scholar] [CrossRef] [Green Version]
  5. Zhu, J.; Li, H.; Jing, Z.Z.; Zheng, W.; Luo, Y.R.; Chen, S.X.; Guo, F. Robust host source tracking building on the divergent and non-stochastic assembly of gut microbiomes in wild and farmed large yellow croaker. Microbiome 2022, 10, 18. [Google Scholar] [CrossRef]
  6. Wang, X.; Wu, X.; Shang, Y.; Gao, Y.; Li, Y.; Wei, Q.; Dong, Y.; Mei, X.; Zhou, S.; Sun, G.; et al. High-Altitude Drives the Convergent Evolution of Alpha Diversity and Indicator Microbiota in the Gut Microbiomes of Ungulates. Front. Microbiol. 2022, 13, 953234. [Google Scholar] [CrossRef]
  7. Wei, F.; Wang, X.; Wu, Q. The giant panda gut microbiome. Trends Microbiol. 2015, 23, 450–452. [Google Scholar] [CrossRef]
  8. Bo, T.B.; Zhang, X.Y.; Wen, J.; Deng, K.; Qin, X.W.; Wang, D.H. The microbiota-gut-brain interaction in regulating host metabolic adaptation to cold in male Brandt’s voles (Lasiopodomys brandtii). ISME J. 2019, 13, 3037–3053. [Google Scholar] [CrossRef]
  9. Li, G.; Yin, B.; Li, J.; Wang, J.; Wei, W.; Bolnick, D.I.; Wan, X.; Zhu, B.; Zhang, Z. Host-microbiota interaction helps to explain the bottom-up effects of climate change on a small rodent species. ISME J. 2020, 14, 1795–1808. [Google Scholar] [CrossRef] [Green Version]
  10. Kim, P.S.; Shin, N.R.; Lee, J.B.; Kim, M.S.; Whon, T.W.; Hyun, D.W.; Yun, J.H.; Jung, M.J.; Kim, J.Y.; Bae, J.W. Host habitat is the major determinant of the gut microbiome of fish. Microbiome 2021, 9, 166. [Google Scholar] [CrossRef]
  11. Waite, D.W.; Taylor, M.W. Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front. Microbiol. 2014, 5, 223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Cao, J.; Hu, Y.; Liu, F.; Wang, Y.; Bi, Y.; Lv, N.; Li, J.; Zhu, B.; Gao, G.F. Metagenomic analysis reveals the microbiome and resistome in migratory birds. Microbiome 2020, 8, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Zhang, Z.; Xu, D.; Wang, L.; Hao, J.; Wang, J.; Zhou, X.; Wang, W.; Qiu, Q.; Huang, X.; Zhou, J.; et al. Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals. Curr. Biol. CB 2016, 26, 1873–1879. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Xiao, F.; Zhu, W.; Yu, Y.; He, Z.; Wu, B.; Wang, C.; Shu, L.; Li, X.; Yin, H.; Wang, J.; et al. Host development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota. NPJ Biofilms Microbiomes 2021, 7, 5. [Google Scholar] [CrossRef] [PubMed]
  15. Mori, A.; Vincent, S.E. An integrative approach to specialization: Relationships among feeding morphology, mechanics, behaviour, performance and diet in two syntopic snakes. J. Zool. 2008, 275, 47–56. [Google Scholar] [CrossRef]
  16. Zhang, L.; Yang, F.; Li, N.; Dayananda, B. Environment-Dependent Variation in Gut Microbiota of an Oviparous Lizard (Calotes versicolor). Animals 2021, 11, 2461. [Google Scholar] [CrossRef]
  17. Zhang, L.; Yang, F.; Li, T.; Dayananda, B.; Lin, L.; Lin, C. Lessons from the diet: Captivity and sex shape the gut microbiota in an oviparous lizard (Calotes versicolor). Ecol. Evol. 2022, 12, e8586. [Google Scholar] [CrossRef]
  18. Montoya-Ciriaco, N.; Gomez-Acata, S.; Munoz-Arenas, L.C.; Dendooven, L.; Estrada-Torres, A.; Diaz de la Vega-Perez, A.H.; Navarro-Noya, Y.E. Dietary effects on gut microbiota of the mesquite lizard Sceloporus grammicus (Wiegmann, 1828) across different altitudes. Microbiome 2020, 8, 6. [Google Scholar] [CrossRef] [Green Version]
  19. Beaupre, S.J. Field Metabolic Rate, Water Flux, and Energy Budgets of Mottled Rock Rattlesnakes, Crotalus lepidus, from Two Populations. Copeia 1996, 1996, 319. [Google Scholar] [CrossRef]
  20. Starck, J.M.; Beese, K. Structural flexibility of the small intestine and liver of garter snakes in response to feeding and fasting. J. Exp. Biol. 2002, 205, 1377–1388. [Google Scholar] [CrossRef]
  21. Ley, R.E.; Hamady, M.; Lozupone, C.; Turnbaugh, P.J.; Ramey, R.R.; Bircher, J.S.; Schlegel, M.L.; Tucker, T.A.; Schrenzel, M.D.; Knight, R.; et al. Evolution of mammals and their gut microbes. Science 2008, 320, 1647–1651. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Xue, Z.; Zhang, W.; Wang, L.; Hou, R.; Zhang, M.; Fei, L.; Zhang, X.; Huang, H.; Bridgewater, L.C.; Jiang, Y.; et al. The bamboo-eating giant panda harbors a carnivore-like gut microbiota, with excessive seasonal variations. mBio 2015, 6, e00022-15. [Google Scholar] [CrossRef] [Green Version]
  23. Michalek, O.; Petrakova, L.; Pekar, S. Capture efficiency and trophic adaptations of a specialist and generalist predator: A comparison. Ecol. Evol. 2017, 7, 2756–2766. [Google Scholar] [CrossRef] [PubMed]
  24. Bron, P.A.; Kleerebezem, M.; Brummer, R.-J.; Cani, P.D.; Mercenier, A.; MacDonald, T.T.; Garcia-Ródenas, C.L.; Wells, J.M. Can probiotics modulate human disease by impacting intestinal barrier function? Br. J. Nutr. 2017, 117, 93–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Wu, X.; Zhang, H.; Chen, J.; Shang, S.; Wei, Q.; Yan, J.; Tu, X. Comparison of the fecal microbiota of dholes high-throughput Illumina sequencing of the V3-V4 region of the 16S rRNA gene. Appl. Microbiol. Biotechnol. 2016, 100, 3577–3586. [Google Scholar] [CrossRef] [PubMed]
  26. Kohl, K.D.; Skopec, M.M.; Dearing, M.D. Captivity results in disparate loss of gut microbial diversity in closely related hosts. Conserv. Physiol. 2014, 2, cou009. [Google Scholar] [CrossRef] [PubMed]
  27. Qian, L.; Zhang, C.; Huang, X.; Pan, T.; Wang, H.; Zhang, B. Mitochondrial genome of Dinodon rufozonatum (Squamata: Colubridae: Dinodon). Mitochondrial DNA Part A DNA Mapp. Seq. Anal. 2016, 27, 970–971. [Google Scholar] [CrossRef]
  28. IUCN. IUCN Red List of Threatened Species; Version 2013.2; IUCN Gland: Gran, Switzerland, 2013. [Google Scholar]
  29. Cai, B.; Li, J.; Chen, Y.; Wang, Y. Exploring the status and causes of China’s threatened reptiles through the red list assessment. Biodivers. Sci. 2016, 24, 578–587. [Google Scholar] [CrossRef]
  30. Suzuki, T.A.; Nachman, M.W. Spatial Heterogeneity of Gut Microbial Composition along the Gastrointestinal Tract in Natural Populations of House Mice. PLoS ONE 2016, 11, e0163720. [Google Scholar] [CrossRef] [Green Version]
  31. Magoc, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef] [Green Version]
  32. Bokulich, N.A.; Subramanian, S.; Faith, J.J.; Gevers, D.; Gordon, J.I.; Knight, R.; Mills, D.A.; Caporaso, J.G. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 2013, 10, 57–59. [Google Scholar] [CrossRef]
  33. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Pena, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Haas, B.J.; Gevers, D.; Earl, A.M.; Feldgarden, M.; Ward, D.V.; Giannoukos, G.; Ciulla, D.; Tabbaa, D.; Highlander, S.K.; Sodergren, E.; et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011, 21, 494–504. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glockner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef]
  37. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef] [Green Version]
  38. Li, W.; Cowley, A.; Uludag, M.; Gur, T.; McWilliam, H.; Squizzato, S.; Park, Y.M.; Buso, N.; Lopez, R. The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res. 2015, 43, W580–W584. [Google Scholar] [CrossRef] [Green Version]
  39. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [Green Version]
  40. Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef] [Green Version]
  41. Oren, A.; Garrity, G.M. Valid publication of the names of forty-two phyla of prokaryotes. Int. J. Syst. Evol. Microbiol. 2021, 71, 005056. [Google Scholar] [CrossRef]
  42. Wu, X.; Shang, Y.; Wei, Q.; Chen, J.; Zhang, H.; Chen, Y.; Gao, X.; Wang, Z.; Zhang, H. Gut Microbiota in Dholes during Estrus. Front. Microbiol. 2020, 11, 575731. [Google Scholar] [CrossRef] [PubMed]
  43. Tong, Q.; Liu, X.N.; Hu, Z.F.; Ding, J.F.; Bie, J.; Wang, H.B.; Zhang, J.T. Effects of Captivity and Season on the Gut Microbiota of the Brown Frog (Rana dybowskii). Front. Microbiol. 2019, 10, 1912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Ley, R.E.; Lozupone, C.A.; Hamady, M.; Knight, R.; Gordon, J.I. Worlds within worlds: Evolution of the vertebrate gut microbiota. Nat. Rev. Microbiol. 2008, 6, 776–788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Xu, L.; Xiang, P.; Zhang, B.; Yang, K.; Liu, F.; Wang, Z.; Jin, Y.; Deng, L.; Gan, W.; Song, Z. Host Species Influence the Gut Microbiota of Endemic Cold-Water Fish in Upper Yangtze River. Front. Microbiol. 2022, 13, 906299. [Google Scholar] [CrossRef] [PubMed]
  46. Kundu, P.; Blacher, E.; Elinav, E.; Pettersson, S. Our Gut Microbiome: The Evolving Inner Self. Cell 2017, 171, 1481–1493. [Google Scholar] [CrossRef] [Green Version]
  47. Rosshart, S.P.; Vassallo, B.G.; Angeletti, D.; Hutchinson, D.S.; Morgan, A.P.; Takeda, K.; Hickman, H.D.; McCulloch, J.A.; Badger, J.H.; Ajami, N.J.; et al. Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance. Cell 2017, 171, 1015–1028. [Google Scholar] [CrossRef] [Green Version]
  48. McLaughlin, R.W.; Cochran, P.A.; Dowd, S.E. Metagenomic analysis of the gut microbiota of the Timber Rattlesnake, Crotalus horridus. Mol. Biol. Rep. 2015, 42, 1187–1195. [Google Scholar] [CrossRef]
  49. Colston, T.J.; Noonan, B.P.; Jackson, C.R. Phylogenetic Analysis of Bacterial Communities in Different Regions of the Gastrointestinal Tract of Agkistrodon piscivorus, the Cottonmouth Snake. PLoS ONE 2015, 10, e0128793. [Google Scholar] [CrossRef]
  50. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 2012, 486, 207–214. [Google Scholar] [CrossRef] [Green Version]
  51. Hong, P.Y.; Wheeler, E.; Cann, I.K.; Mackie, R.I. Phylogenetic analysis of the fecal microbial community in herbivorous land and marine iguanas of the Galapagos Islands using 16S rRNA-based pyrosequencing. ISME J. 2011, 5, 1461–1470. [Google Scholar] [CrossRef]
  52. Xiao, K.; Fan, Y.; Zhang, Z.; Shen, X.; Li, X.; Liang, X.; Bi, R.; Wu, Y.; Zhai, J.; Dai, J.; et al. Covariation of the Fecal Microbiome with Diet in Nonpasserine Birds. mSphere 2021, 6, e00308-21. [Google Scholar] [CrossRef] [PubMed]
  53. Holmberg, A.; Kaim, J.; Persson, A.; Jensen, J.; Wang, T.; Holmgren, S. Effects of digestive status on the reptilian gut. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 2002, 133, 499–518. [Google Scholar] [CrossRef] [PubMed]
  54. He, J.; Yi, L.; Hai, L.; Ming, L.; Gao, W.; Ji, R. Characterizing the bacterial microbiota in different gastrointestinal tract segments of the Bactrian camel. Sci. Rep. 2018, 8, 654. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Costello, E.K.; Gordon, J.I.; Secor, S.M.; Knight, R. Postprandial remodeling of the gut microbiota in Burmese pythons. ISME J. 2010, 4, 1375–1385. [Google Scholar] [CrossRef] [Green Version]
  56. Kohl, K.D.; Brun, A.; Magallanes, M.; Brinkerhoff, J.; Laspiur, A.; Acosta, J.C.; Caviedes-Vidal, E.; Bordenstein, S.R. Gut microbial ecology of lizards: Insights into diversity in the wild, effects of captivity, variation across gut regions and transmission. Mol. Ecol. 2017, 26, 1175–1189. [Google Scholar] [CrossRef]
  57. Xie, Y.; Xia, P.; Wang, H.; Yu, H.; Giesy, J.P.; Zhang, Y.; Mora, M.A.; Zhang, X. Effects of captivity and artificial breeding on microbiota in feces of the red-crowned crane (Grus japonensis). Sci. Rep. 2016, 6, 33350. [Google Scholar] [CrossRef] [Green Version]
  58. Colston, T.J.; Jackson, C.R. Microbiome evolution along divergent branches of the vertebrate tree of life: What is known and unknown. Mol. Ecol. 2016, 25, 3776–3800. [Google Scholar] [CrossRef] [Green Version]
  59. Shin, N.R.; Whon, T.W.; Bae, J.W. Proteobacteria: Microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 2015, 33, 496–503. [Google Scholar] [CrossRef]
  60. Esteve, C. Numerical Taxonomy of Aeromonadaceae and Vibrionaceae associated with Reared Fish and Surrounding Fresh and Brackish Water. Syst. Appl. Microbiol. 1995, 18, 391–402. [Google Scholar] [CrossRef]
  61. Zhao, E.; Huang, M.; Zong, Y.; Jiang, Y.; Huang, Q.; Zhao, H.; Ma, J.; Zheng, J.; Huang, Z.; Wei, G.; et al. Zoology of China: Reptilia; Science Press: Beijing, China, 1998; Volume Ⅲ. [Google Scholar]
  62. Mundy, R.; Macdonald, T.T.; Dougan, G.; Frankel, G.; Wiles, S. Citrobacter rodentium of mice and man. Cell. Microbiol. 2006, 7, 1697–1706. [Google Scholar] [CrossRef]
  63. Stiles, M.E.; Ng, L.K. Enterobacteriaceae associated with meats and meat handling. Appl. Environ. Microbiol. 1981, 41, 867–872. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Zhang, K.; Wang, X.; Gong, X.; Sui, J. Gut Microbiome Differences in Rescued Common Kestrels (Falco tinnunculus) before and after Captivity. Front. Microbiol. 2022, 13, 858592. [Google Scholar] [CrossRef]
  65. Keenan, S.W.; Engel, A.S.; Elsey, R.M. The alligator gut microbiome and implications for archosaur symbioses. Sci. Rep. 2013, 3, 2877. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Spence, C.; Wells, W.G.; Smith, C.J. Characterization of the primary starch utilization operon in the obligate anaerobe Bacteroides fragilis: Regulation by carbon source and oxygen. J. Bacteriol. 2006, 188, 4663–4672. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Sears, C.L. A dynamic partnership: Celebrating our gut flora. Anaerobe 2005, 11, 247–251. [Google Scholar] [CrossRef]
  68. Matsui, H.; Kato, Y.; Chikaraishi, T.; Moritani, M.; Ban-Tokuda, T.; Wakita, M. Microbial diversity in ostrich ceca as revealed by 16S ribosomal RNA gene clone library and detection of novel Fibrobacter species. Anaerobe 2010, 16, 83–93. [Google Scholar] [CrossRef]
  69. Hu, X.; Liu, G.; Shafer, A.B.A.; Wei, Y.; Zhou, J.; Lin, S.; Wu, H.; Zhou, M.; Hu, D.; Liu, S. Comparative Analysis of the Gut Microbial Communities in Forest and Alpine Musk Deer Using High-Throughput Sequencing. Front. Microbiol. 2017, 8, 572. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Bennett, K.W.; Eley, A. Fusobacteria: New taxonomy and related diseases. J. Med. Microbiol. 1993, 39, 246–254. [Google Scholar] [CrossRef]
  71. Mira, A.; Pushker, R.; Legault, B.A.; Moreira, D.; Rodríguez-Valera, F. Evolutionary relationships of Fusobacterium nucleatum based on phylogenetic analysis and comparative genomics. BMC Evol. Biol. 2004, 4, 50. [Google Scholar] [CrossRef] [Green Version]
  72. Roggenbuck, M.; Baerholm Schnell, I.; Blom, N.; Baelum, J.; Bertelsen, M.F.; Sicheritz-Ponten, T.; Sorensen, S.J.; Gilbert, M.T.; Graves, G.R.; Hansen, L.H. The microbiome of New World vultures. Nat. Commun. 2014, 5, 5498. [Google Scholar] [CrossRef] [Green Version]
  73. Larsen, A.M.; Mohammed, H.H.; Arias, C.R. Characterization of the gut microbiota of three commercially valuable warmwater fish species. J. Appl. Microbiol. 2014, 116, 1396–1404. [Google Scholar] [CrossRef] [PubMed]
  74. Nayak, S.K. Role of gastrointestinal microbiota in fish. Aquac. Res. 2010, 41, 1553–1573. [Google Scholar] [CrossRef]
  75. Yu, Y.Y.; Ding, L.G.; Huang, Z.Y.; Xu, H.Y.; Xu, Z. Commensal bacteria-immunity crosstalk shapes mucosal homeostasis in teleost fish. Rev. Aquac. 2021, 13, 2322–2343. [Google Scholar] [CrossRef]
  76. Sanders, J.G.; Beichman, A.C.; Roman, J.; Scott, J.J.; Emerson, D.; McCarthy, J.J.; Girguis, P.R. Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat. Commun. 2015, 6, 8285. [Google Scholar] [CrossRef] [Green Version]
Animals 13 00731 g001 550
Figure 1. Alpha diversity analysis of the different segments of the gastrointestinal tract of L. rufozonatus. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach; **: p < 0.01.
Figure 1. Alpha diversity analysis of the different segments of the gastrointestinal tract of L. rufozonatus. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach; **: p < 0.01.
Animals 13 00731 g001
Animals 13 00731 g002 550
Figure 2. (a) Non−metric multidimensional analysis of the microbial community structures of the gut microbiota in the three segments of the L. rufozonatus digestive tract. (b) Venn diagram of gut microbiota in Lycodon rufozonatus at the operational taxonomic unit (OTU). Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 2. (a) Non−metric multidimensional analysis of the microbial community structures of the gut microbiota in the three segments of the L. rufozonatus digestive tract. (b) Venn diagram of gut microbiota in Lycodon rufozonatus at the operational taxonomic unit (OTU). Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g002
Animals 13 00731 g003 550
Figure 3. Microbial composition among the different samples (a) and segments (b) at the phylum level. The top 10 abundant taxa are shown. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 3. Microbial composition among the different samples (a) and segments (b) at the phylum level. The top 10 abundant taxa are shown. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g003
Animals 13 00731 g004 550
Figure 4. Microbial composition of different samples (a) and segments (b) at the genus level. The top 30 most abundant taxa are shown. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 4. Microbial composition of different samples (a) and segments (b) at the genus level. The top 30 most abundant taxa are shown. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g004
Animals 13 00731 g005 550
Figure 5. Statistical heat map of the significantly different species at the phylum and genus levels. (a) Top 10 species with significant differences at the phylum level. (b) Top 35 species with significant differences at the genus level. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 5. Statistical heat map of the significantly different species at the phylum and genus levels. (a) Top 10 species with significant differences at the phylum level. (b) Top 35 species with significant differences at the genus level. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g005
Animals 13 00731 g006 550
Figure 6. Biomarker analysis of microbial communities from the different segments of the gastrointestinal tract. (a) Differentially abundant taxa. The length of the histogram represents the impact of the different species (LDA > 4). (b) Cladogram showing the phylogenetic structures of the microbiota. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 6. Biomarker analysis of microbial communities from the different segments of the gastrointestinal tract. (a) Differentially abundant taxa. The length of the histogram represents the impact of the different species (LDA > 4). (b) Cladogram showing the phylogenetic structures of the microbiota. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g006
Animals 13 00731 g007 550
Figure 7. Heatmap of the hierarchy cluster results for the functional diversity of microbiota in the different segments of the gastrointestinal tract. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 7. Heatmap of the hierarchy cluster results for the functional diversity of microbiota in the different segments of the gastrointestinal tract. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g007
Animals 13 00731 g008 550
Figure 8. Comparison of the microbial functions (level two, top 20) that were significantly different among the three segments of the gastrointestinal tract. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Figure 8. Comparison of the microbial functions (level two, top 20) that were significantly different among the three segments of the gastrointestinal tract. Abbreviations: LI, large intestine; SI, small intestine; ST, stomach.
Animals 13 00731 g008
Table
Table 1. Statistics of the sequencing data preprocessing and quality control.
Table 1. Statistics of the sequencing data preprocessing and quality control.
Sample Raw
Tags 		Clean
Tags		Effective
Tags		Average
Length (nt)		Effective
%		OTU Number
ST177,93074,10765,02940663.2014041785.50 ± 313.97
ST270,07966,07956,91240264.981780
ST374,05973,02062,35837863.802173
ST465,96064,88360,53937662.481785
SI171,63369,12358,87341272.97505617.33 ± 225.56
SI273,75870,63059,18941369.12470
SI383,93778,54668,88840660.69877
LI182,56879,96363,32641968.42406501.71 ± 173.88
LI287,41284,51165,07542167.78687
LI379,28177,13762,15042074.34360
LI478,85576,42161,75941773.93588
LI592,00589,60361,18042063.34348
LI686,30883,89268,67542071.38761
LI776,94474,80459,45342273.03362
Abbreviations: LI, large intestine; SI, small intestine; ST, stomach; OTU, operational taxonomic unit; nt, nucleotide.
Table
Table 2. The top 10 bacterial genera in different segments of the digestive tract of Lycodon rufozonatus.
Table 2. The top 10 bacterial genera in different segments of the digestive tract of Lycodon rufozonatus.
Stomach (ST)Small Intestine (SI)Large Intestine (LI)
Other (34.74%)Cetobacterium (61.15%)Bacteroides (25.01%)
Bacteroides (12.50%)Bacteroides (12.65%)Cetobacterium (19.85%)
Delftia (9.85%)Other (9.38%)Other (17.71%)
Sphingomonas (7.03%)Citrobacter (2.32%)Citrobacter (13.94%)
Cetobacterium (4.81%)Fusobacterium (2.26%)Aeromonas (11.54%)
unidentified Clostridiales (4.14%)Aeromonas (1.72%)Providencia (3.89%)
Aeromonas (3.88%)Odoribacter (1.62%)unidentified Erysipelotrichaceae (2.71%)
Chryseobacterium (2.69%)unidentified Enterobacteriaceae (1.59%)Helicobacter (0.78%)
Lactobacillus (2.45%)unidentified Erysipelotrichaceae (1.58%)Shewanella (0.77%)
Methanosaeta (2.16%)Brevundimonas (0.85%)Fusobacterium (0.77%)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Shang, Y.; Zhong, H.; Liu, G.; Wang, X.; Wu, X.; Wei, Q.; Shi, L.; Zhang, H. Characteristics of Microbiota in Different Segments of the Digestive Tract of Lycodon rufozonatus. Animals 2023, 13, 731. https://doi.org/10.3390/ani13040731

AMA Style

Shang Y, Zhong H, Liu G, Wang X, Wu X, Wei Q, Shi L, Zhang H. Characteristics of Microbiota in Different Segments of the Digestive Tract of Lycodon rufozonatus. Animals. 2023; 13(4):731. https://doi.org/10.3390/ani13040731

Chicago/Turabian Style

Shang, Yongquan, Huaming Zhong, Gang Liu, Xibao Wang, Xiaoyang Wu, Qinguo Wei, Lupeng Shi, and Honghai Zhang. 2023. "Characteristics of Microbiota in Different Segments of the Digestive Tract of Lycodon rufozonatus" Animals 13, no. 4: 731. https://doi.org/10.3390/ani13040731

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop