Comparative analysis of the gut microbiota of the Chinese Kunming dog, German Shepherd, and Belgian Malinois

doi:10.21203/rs.3.rs-4288714/v1

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Comparative analysis of the gut microbiota of the Chinese Kunming dog, German Shepherd, and Belgian Malinois

https://doi.org/10.21203/rs.3.rs-4288714/v1

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The composition of the gut microbiota is essential for the host’s health and adaptation to the environment. Due to bacterial functional redundancy in microbial communities, different bacteria can produce the same important metabolites beneficial to health, and studying the gut flora composition of dogs of different breeds under the same domestication conditions can help improve microbial diversity and normalize metabolite production in diseased dogs. In this study, noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare and analyze the structure and function of the gut microbiota of three dog breeds, namely the Chinese Kunming dog (CKD), German Shepherd dog (GSD) and Belgian Malinois dog (BMD). The results showed that the gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla. The dominant bacterial phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%). Further analysis also revealed significant differences in the intestinal flora of the three breeds, at both the phylum and genus levels. The richness of intestinal flora of BMD was significantly higher than that of CKD and GSD. The results of this study provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status.

Chinese Kunming dog

German Shepherd dog

Belgian Malinois dog

Gut microbiota

High-throughput sequencing

16S rRNA

The intestine, as the largest and most complex micro-ecosystem in animals, is inhabited by a wide variety of microorganisms. There is a delicate balance between these microorganisms and the host, which is affected not only by the external environment, but also by the host's internal conditions [1]. This dynamic balance is crucial for understanding how the host adapts to the environment, especially in promoting the digestion and absorption of nutrients, maintaining the normal physiological functions of the intestinal tract, and regulating the body's immune system [2, 3]. Several studies have confirmed that intestinal flora can synthesize nutrients, such as vitamin B and essential amino acids, that the host cannot synthesize through metabolic processes [4]. Additionally, intestinal flora can hydrolyze carbohydrates to produce a large number of short-chain fatty acids, which bind to the G protein-coupled receptors to regulate the host immune homeostasis [5].

The mammalian intestinal tract consists of several microorganisms, including bacteria, fungi, viruses, and parasites [6], which exist in a symbiotic relationship with the host. Over the long course of evolution, the abundance and diversity of bacteria in these gut microorganisms change with host genetic signatures [7–9]. For example, the intestinal flora of a giant panda (Ailuropoda melanoleuca) is similar to that of a carnivora [10] and contains Clostridium, which enables it to metabolize cellulose and hemicellulose, allowing it to obtain energy and nutrition from bamboo [11]. Additionally, the intestinal flora of cats and dogs can improve the bioavailability of their diets by degrading starch and cellulose using glycoside hydrolase [12]. The ratio of Sclerenchyma to Bacteroides is higher in rock sheep (Pseudois nayaur) at a high altitude than in European argali (Ovis aries musimon) at a low altitude, allowing rock sheep to be better adapted to high altitudes [13]. In addition, each lemur, of the family Lemuridae, has a unique microbial metabolic pathway to adapt to different niches in the same environment [14].

The Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD) are breeds of dogs (Canis lupus familiaris), belonging to the family Canidae of the order Carnivora, which are widely distributed all over the world. The CKD, GSD, and BMD breeds are widely used as police dogs and play an important role in identifying physical evidence, searching and tracking, escorting and guarding, as well as patrolling and explosion-proofing [15]. The intestinal flora of police dogs can be affected by the composition of their diets. For instance, dietary fiber can increase the proportion of beneficial bacteria and improve the intestinal flora of police dogs [16]. Additionally, nutrients, such as probiotics and prebiotics, can improve the immunity of the intestinal mucosal system of police dogs, thus effectively inhibiting the invasion and colonization of various pathogens [16, 17]. However, there are limited reports on the intestinal flora of CKD, GSD, and BMD.

In this study, we used noninvasive sampling and 16S rRNA high-throughput sequencing to compare and analyze the intestinal flora of CKD, GSD, and BMD that were inhabited under the same domestication conditions. The analysis of the fecal microbial composition of different police dog breeds can facilitate in determining the underlying causes for variations in the intestinal flora of species bred in the same habitat and understanding the interactions between host and intestinal flora. The results of our study may provide the scientific basis for intestinal flora research and provide new insights into improving the health status of police dogs. Intestinal health of police dogs can be effectively maintained through reasonable diet adjustment, personalized nutritional supplementation, and regular detection and intervention.

Animals and diets

The CKD, GSD, and BMD breeds used in this study were obtained from the Kunming Police Dog Base, Ministry of Public Security (Kunming, China). A total of 15 CKD (2 females and 3 males), GSD (1 female and 4 males), and BMD (3 females and 2 males) individuals, with similar birth dates, were selected for this analysis. The puppies were raised with bitches until weaning. Thereafter, the puppies were transferred to independent kennels for feeding. The dog food was purchased from the Mars Pet Food Co., Ltd. (China). It was composed of crude protein (≥ 30%), crude fat (≥ 14. 5%), crude fiber (≤ 5. 0%), water-soluble chloride (≥ 0. 4%, Cl⁻), calcium (≥ 1. 0%), phosphorus (≥ 0. 8%), zinc (≤ 200 mg/kg), crude ash (≤ 12. 0%), water (≤ 10.0%), lysine (≥ 0. 85%), and vitamin E (≥ 80 IU/kg). The puppies were not administered any antibiotics, probiotics, prebiotics, drugs, or nutrition products.

Sample collection

In this experiment, we used a noninvasive sampling technique to obtain fecal samples. Three-month-old healthy puppies of similar weights were selected for this analysis (N = 15; 5 CKD: CKD1–CKD5, 5 GSD: GSD1–GSD5, and 5 BMD: BMD1–BMD5). Sterile gloves and masks were worn during sample collection, and large pieces of fresh pollution-free feces were collected as quickly as possible. The collection of fecal samples that were in contact with the ground or other impurities was avoided, if possible. The fecal samples were cryopreserved at − 80°C [18].

DNA extraction

The fecal samples were sent to Shanghai Meiji Biomedical Technology Co., Ltd. (Shanghai, China) for total microbial DNA extraction. The total microbial DNA was extracted using the E.Z.N.A ® soil DNA kit (OMEGA, USA), according to the manufacturer’s instructions. The integrity of the DNA samples was detected by 1% agarose gel electrophoresis (AGE), and the concentration and purity of the samples were quantitatively detected using the NanoDrop2000.

16S rRNA gene sequencing

The V3-V4 variable region of the 16S rRNA gene was amplified by polymerase chain reaction (PCR) using the primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). The PCR conditions were as follows: 95°C for 3 min; 30 cycles of 95°C for 30 s, 45°C for 30 s, and 72°C for 45 s; and 72°C for 10 min. Experiments were conducted in three replicates, and the PCR products of the same sample were mixed and detected by 2% AGE. The PCR products were then cut and recovered by using the AxyPrepDNA Gel Recovery Kit (AXYGEN, USA), eluted with Tris-HCl, and detected by 2% AGE. Thereafter, the PCR products were quantified using the QuantiFluorTM-ST Blue Fluorescence Quantification System (Promega, USA). Subsequently, the PCR products were mixed according to their concentration and sequenced on the Miseq PE300 platform (Illumina, USA). The 16S rRNA database construction and sequencing was conducted by Shanghai Meiji Biomedical Technology Co., Ltd (China).

Statistical analysis

Paired-end reads obtained from Illumina sequencing were spliced based on the overlap using FLASH (https://ccb.jhu.edu/software/FLASH/index.shtml, v1.2.11) and then quality controlled and filtered using fastp (https://github.com/OpenGene/fastp, v0.19.6). Thereafter, the sequences were classified into operational taxonomic units (OTUs) according to a 97% similarity level. Subsequently, species diversity indexes and sequencing depth were analyzed. The OTUs were annotated using the Ribosomal Database Project Bayesian Classifier (https://sourceforge.net/projects/rdp-classifier, v2.13) against the Silva 16S rRNA database (https://www.arb-silva.de, v138). The bacterial community composition at the species level was generated using QIIME (http://qiime.org/install/index.html, v1.9.1) [19]. Statistical analysis of the sequencing data was conducted using the Kruskal–Wallis and Wilcoxon rank-sum tests, at a P-value of < 0.05. The principal component analysis (PCA) of different groups was conducted on the Megasonson Cloud Analysis Platform (https://cloud.majorbio.com), and functional prediction analysis was conducted using the picrust software (http://picrust.github.io/picrust, v1.1.0).

DNA extraction and 16S rRNA gene sequencing

The DNA samples extracted from the CKD, GSD, and BMD fecal samples were amplified by PCR and subjected to AGE (Fig. 1). A total of 994,123 optimized sequences, with an average sequence length of 415 bp, were obtained by Illumina MiSeq sequencing and denoising. The rarefaction curves, generated using the Sob index, revealed that the curves flattened with an increase in the sequencing depth of randomly selected data (Fig. 2A). The species cumulative curve was constructed using R mapping, with the x-axis representing the OTU ranking level and the y-axis representing the relative percentage content of the OTU. The species cumulative curve demonstrated that the species flattened gradually (Fig. 2B), indicating an even distribution of the species.

Composition of intestinal flora of different dog breeds

The nonrepetitive sequences (excluding single sequences) were clustered into OTUs according to 97% sequence similarity. A total of 257 species, representing 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla, were identified in the intestinal flora of the CKD, BMD, and GSD breeds. The intestinal flora of CKD contained 189 species, representing 113 genera, 52 families, 32 orders, 14 classes, and 9 phyla. The intestinal flora of BMD contained 213 species, representing 125 genera, 50 families, 27 orders, 12 classes, and 8 phyla. Lastly, the intestinal flora of GSD contained 209 species, representing 123 genera, 94 families, 29 orders, 13 classes, and 8 phyla. At the phylum level, the dominant phyla across the three breeds were Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%) (Fig. 3A). Meanwhile, at the genus level, Lactobacillus (16.31%) was the most abundant genus in the CKD fecal samples, followed by Norank_f_Muribaculaceae (11.12%), Pasteurella (10.63%), Fusobacterium (10.27%), Cetobacterium (9.73%), Peptostreptococcus (8.54%), Norank_f_Erysipelotrichaceae (7.15%), Romboutsia (3.68%), Peptoclostridium (1.94%), Turicibacter (1.87%), Allobaculum (1.73%), Bacteroides (1.61%), and Bifidobacterium (1.23%) (Fig. 3B). In contrast, Fusobacterium (22.64%) was the most abundant genus in the BMD fecal samples, followed by Peptostreptococcus (14.55%), Norank_f_Erysipelotrichaceae (10.88%), Lactobacillus (10.52%), Cetobacterium (7.11%), Norank_f_Muribaculaceae (6.48%), Allobaculum (3.62%), Peptoclostridium (2.81%), Coriobacteriaceae_UCG-002 (2.11%), Prevotella (1.98%), and Romboutsia (1.18%) (Fig. 3B). Lastly, Fusobacterium (18.69%) was the most abundant genus in the GSD fecal samples, followed by Peptoclostridium (11.22%), Prevostella (9.74%), Norank_f_Muribaculaceae (6.64%), Faecalibacterium (5.47%), Alloprevotella (4.76%), Cetobacterium (4.39%), Romboutsia (3.67%), Blautia (3.37%), Bacteroides (2.46%), Norank_f_Erysipelotrichaceae (1.97%), Peptostreptococcus (1.95%), and Ruminococcus_torques_group (1.34%) (Fig. 3B). A total of 221 OTUs were identified in the fecal samples of CKD, BMD, and GSD by Venn analysis (Fig. 4).

Among the top 10 abundant phyla, Firmicutes, Bacteroidota, Fusobacteriota, Proteobacteria, unclassified_k_norank_d_Bacteria, Actinobacteria, Campylobacterota, Spirochaetota, and Deferribacterota were common between CKD and GSD. Among these, the richness of Proteobacteria in CKD was significantly higher than that of GSD, and the richness of Bacteroidota in GSD was significantly higher than that of CKD (Fig. 5A). Additionally, CKD and BMD shared 10 common phyla, namely Firmicutes, Fusobacteriota, Bacteroidota, Proteobacteria, Actinobacteria, unclassified_k__norank_d__Bacteria, Campylobacteriota, Spirochaetota, Deferribacterota, and Patescibacteria. Among these, the proportion of Firmicutes, Fusobacteriota, and Actinobacteria was higher in BMD than in CKD, while the proportion of Bacteroidota and Proteobacteria was higher in CKD than in BMD. The bacterial phyla Deferiribacterota and Patescibacteria were specific to CKD and BMD, respectively (Fig. 5B). Lastly, BMD and GSD shared nine common phyla, namely Firmicutes, Fusobacteriota, Bacteroidota, Proteobacteria, Actinobacteria, unclassified_k__norank_d__Bacteria, Campylobacterota, Deferribacterota, and Patescibacteria. Among these, the richness of Firmicutes, Fusobacteriota, and Proteobacteria was significantly higher in BMD than in GSD. The bacterial phyla Patescibacteria and Deferiribacterota were unique to CKD and GSD, respectively (Fig. 5C).

At the genus level, Fusobacterium was abundant in BMD and GSD, Peptoclostridium was abundant in BMD, and Lactobacillus was abundant in only CKD. In BMD, the abundance of Fusobacterium, Peptostreptococcus, Prevostella, and Cetobacterium was higher than that in GSD, while the abundance of Lactobacillus, Cetobacterium, and Romboutsia was lower than that in CKD (Fig. 6). Moreover, the intestinal flora of the three breeds showed certain similarities, which were consistent with the results presented in Fig. 3B.

Further analysis revealed that the abundance of Fusobacterium (P = 0.0215), Norank_f_Prevotellaceae (P = 0.0116), Norank_f_Norank_o_Clostridia_UCG-014 (P = 0.0215), Norank_f_Lachnospiraceae (P = 0.0367), Eubacterium_eligens_group (P = 0.0311), and Lachnospira (P = 0.0109) was significantly lower in CKD compared to GSD, while the abundance of Lactobacillus (P = 0.0121), Parasutterella (P = 0.0121), Norank_f_Erysipelotrichaceae (P = 0.0367), and Fusobacterium (P = 0.0215) was significantly higher in GSD compared to CKD. Among the bacteria with significant differences among species, only Fusobacterium was the highest. The bacterial genus Bifidobacterium was specific to CKD, while the bacterial genera norank_f_Prevotellaceae, norank_f_norank_o_Clostridia_UCG-014, Eubacterium_eligens_group, and Lachnospira were specific to GSD (Fig. 7A). The abundance of Fusobacterium (P = 0.0121), Allobaculum (P = 0.0367), Coriobacteriaceae_UCG-002 (P = 0.0121), Eubacterium_brachy_group (P = 0.0121), Dubosiella (P = 0.0121), Norank_f_Ruminococcaceae (P = 0.0119), and unclassified_f_Eggerthellaceae (P = 0.0121) was significantly lower in CKD than in BMD, while the abundance of Parasutterella (P = 0.0121) and Turicibacter (P = 0.0121) was significantly higher in CKD than in BMD. The abundance of Fusobacterium was the highest. The bacterial genera Eubacterium_brachy_group, norank_f_Ruminococcaceae, and unclassfied_f_Eggerthellaceae were specific to BMD, while CKD had no endemic bacterial genera (Fig. 7B). Lastly, the abundance of Norank_f_Prevotellaceae (P = 0.0074) and UCG-005 (P = 0.0367) was significantly lower in BMD than in GSD, while the abundance of Peptostreptococcus (P = 0.0215), Norank_f_Erysipelotrichaceae (P = 0.0215), Lactobacillus (P = 0.0121), Allobaculum (P = 0.0215), Coriobacteriaceae_UCG-002 (P = 0.0200), and Bifidobacterium (P = 0.0344) was significantly higher in BMD than in GSD. The richness of Peptostreptococcus was the highest. This richness is Bifidobacterium was specific to BMD, while Norank_f_Prevotellaceae and UCG-005 were specific to GSD (Fig. 7C). The abundance of Fusobacterium (P = 0.0113), Peptostreptococcus (P = 0.0208), norank_f_Erysipelotrichaceae (P = 0.0185), and Allobaculum (P = 0.0139) was significantly higher in BMD than in CKD and GSD. Among the three breeds, the abundance of Fusobacterium was the highest.. The bacterial genus norank_f_Prevotellaceae was specific to CKD; however, BMD and GSD had no endemic bacterial genera (Fig. 7D).

The Linear discriminant analysis Effect Size (LEfSe) multi-level discriminant analysis showed that the abundance of Proteobacteria, Bacilli, Gammaproteobacteria, Actinobacteria, Lactobacillales, Burkholderiales, Bifidobacilliales, Lactobacillaceae, Sutterellaceae, Bifidobacilliaceae, Lactobacillus, Parasutterella, Turicibacter, and Bifidobacillus was significantly higher in CKD than in BMD and GSD (Fig. 8A). In contrast, the abundance of Fusobacteria, Coriobacteriia, Fusobacteriia, Coriobacteriales, Erysipelotrichales, Fusobacteriales, Fusobacteriaceae, Erysipelotrichaceae, Atopobiaceae, unclassified_o_Lactobacillales, Fusobacterium, Peptostreptococcus, norank_f_Erysipelotrichaceae, Allobaculum, Coriobacteriaceae_UCG-002, and unclassified_o__Lactobacillales was significantly higher in BMD than in CKD and GSD (Fig. 8A). Lastly, the abundance of Clostridia_UCG-014, Anaerovoracaceae, norank_o__Clostridia_UCG-014, Eubacterium_brachy_group, norank_f__Prevotellaceae, norank_f__norank_o__Clostridia_UCG-014, Lachnospiraceae_UCG-004, and Sutterella was significantly higher in GSD than in CKD and BMD (Fig. 8B).

Alpha and beta diversity analysis of the intestinal flora of CKD, BMD, and GSD

Alpha diversity analysis showed that the Ace (322.48), Chao (311.10), and Simpson indexes (0.09585) were higher in BMD than in CKD (Ace index: 277.03, Chao index: 269.60, and Simpson index: 0.07911) and GSD (Ace index: 270.04, Chao index: 267.96, and Simpson index: 0.08588). The Ace index of BMD was significantly higher than that of GSD. The Ace and Chao indexes of CKD were higher than those of GSD. The average Shannon index was higher in GSD (3.2255) than in CKD (3.1669) and BMD (3.1637) (Fig. 9). The richness and diversity of the intestinal flora in BMD were higher than those of both CKD and GSD, while the richness and diversity of the intestinal flora in CKD were higher than those in GSD. Beta diversity analysis revealed that the intestinal flora samples of CKD and GSD, GSD and BMD, and CKD, GSD, and BMD exhibited differential spatial distributions, indicating that the intestinal flora of the two or three breeds, when compared with each other, formed completely different taxonomic clusters, and there were significant differences between them. Although the intestinal flora of CKD and BMD were distributed in different spaces, they were relatively clustered within the species, and there were differences among the species that reached a significant level (Fig. 10).

Functional prediction of the intestinal flora

Functional prediction analysis and KEGG analysis of the sequences revealed that the primary functions of the intestinal flora of the CKD, BMD, and GSD were similar. The intestinal flora of these three breeds was enriched in metabolic pathways, secondary metabolite biosynthesis, microbial metabolism in different environments, amino acid biosynthesis, carbon metabolism, ABC transporters, purine metabolism, quorum sensing, and two-component system. They were significantly enriched in starch and sucrose metabolism; pyrimidine metabolism; amino acid sugar and nucleotide sugar metabolism; glycolysis; aminoacly-tRNA biosynthesis; pyruvate metabolism; homologous recombination; cysteine and methionine metabolism; alanine, aspartate, and glutamate metabolism; prokaryotic carbon fixation pathway; and other metabolic pathways (Fig. 11A). The clusters of orthologous groups functional classification showed that the intestinal flora of CKD, BMD, and GSD was associated with genetic information processing and metabolic pathways related to various activities, including transcription, translation, replication, material transport, and metabolism (Fig. 11B).

In this study, we explored the intestinal flora of CKD, GSD, and BMD that were inhabited under the same domestication conditions. Our results revealed that Firmicutes (57.44%), Fusobacteriota (28.86%), and Bacteroidota (7.63%) were the dominant phyla across the three breeds, while Peptostreptococcus (21.08%), Fusobacterium (18.50%), Lactobacillus (12.37%), and Cetobacter (10.29%) were the dominant genera across the three breeds. These results are consistent with the reports on the intestinal flora of wolf (Canis lupus) and Labrador Retriever Dogs [20, 21], as well as the reports on human and mammalian intestinal flora [22]. The abundance of Firmicutes in mammals may be attributed to the high consumption of protein-rich food. Studies have reported that Firmicutes can digest proteins and carbohydrates and participate in metabolic processes.

In this study, we observed no significant difference in the intestinal flora of CKD, GSD, and BMD at the species level, which could plausibly arise from a shared domestication environment and uniform dietary intake. Feeding habits can affect the intestinal flora of hosts [23, 24]. Under the same feeding habits, the composition of the intestinal flora of different breeds can show convergence. [25]. For instance, a study on primates found that the intestinal flora of Leontopithecus chrysomelas, Saguinus midas, and Saguinus oedipus were similar and that these mammals consumed highly similar diets [6, 26]. Additionally, a study on Cervus dybowski and Cervus canadensis alashanicus found that the two deer breeds primarily consumed corn and alfalfa and that the dominant phyla in their intestinal flora were Firmicutes, Bacteroidota, and Proteobacteria [27]. In contrast, studies found that the composition of intestinal flora of the same breed can alter due to different feeding habits. For example, a study found that the abundance of Firmicutes and Proteobacteria is higher in the intestinal flora of Cebus capucinus in captivity, whose main food source is invertebrates, than in those who feed on fruits [28]. In this study, the intestinal flora of CKD, GSD, and BMD, which inhabited the same environment and consumed the same diet, were found to share similar characteristics, suggesting that the diet and environment affect the composition of the intestinal flora of the host.

A study found that the intestinal flora of canine species is affected by host species specificity [29, 30]. For example, the abundance of Firmicutes, Actinobacteria, Proteobacteria, Fusobacteria, Bacteroidota, and Cyanobacteria is significantly lower in the intestinal flora of a wolf than in that of other canine species. Additionally, the abundance of Fusobacteria and Proteobacteria is significantly higher in Labrador Retriever Dog compared to other dog breeds, while the abundance of Firmicutes and Actinobacteria is significantly lower than that of other dogs [31]. Another study found that the intestinal flora of wild red foxes (Vulpes vulpes), sand foxes (Vulpes corsac), and blue foxes (Vulpes lagopus) have significantly lower intraspecific differences than interspecific differences and that the interspecific differences are unaffected by habitat degradation [32]. In this study, we found that the variations in the composition of intestinal flora between CKD–BMD and CKD–GSD were greater than those between BMD–GSD, at both the phylum and genus levels, indicating that species specificity affects the intestinal flora of the host to a certain extent.

Previous studies have shown that age, sex, and physiological state are important factors affecting the intestinal flora of dogs [33]. For instance, among captive Beagle and Cocker Spaniel breeds, the abundance of Pseudomonas adaceae and Escherichia is significantly higher, while the abundance of Actinobacteria is significantly lower in the aged group (> 7 years) than in the adult group (2–7 years) [34]. The study on the intestinal flora of Maltese found that the abundance of Bacteroidota and Prehostella is significantly higher in females than in males, while the abundance of Atopobiaceae and Megamonas is significantly higher in males than in females and that there are significant differences in the beta diversities of the male and female individuals [35]. Previous studies on captive Border Collies found that the abundance of Prevostella, Peptostreptococcus, and Allisonella is significantly lower in the intestinal flora of normal-weight individuals than in that of overweight individuals [31]. In this study, the composition of the intestinal flora of three police dogs showed some interspecific and intraspecific differences, despite inhabiting the same domestication conditions and consuming the same food. Based on the aforementioned reports, we hypothesized that the interspecific and intraspecific differences in the composition of the intestinal flora of CKD, GSD and BMD may be attributed to the age, sex, and physiological state of the individuals.

In this study, we compared the intestinal flora of CKD, GSD, and BMD under the same domestication conditions and found that the composition of their intestinal flora was similar, owing to their consumption of the same feed. Similarity analysis of the intestinal flora of the three breeds showed no significant differences, indicating that the similar captive conditions of the three breeds led to the high similarity of their intestinal flora. However, different breeds showed varying degrees of interspecific differences in their intestinal flora, which may be attributed to similar captive conditions as well as variations in the age and sex of the individuals. The intestinal flora may be closely associated with the intestinal health of police dogs and may serve as an intervention target for regulating the intestinal flora of the police dogs. In this study, CKD, GSD, and BMD showed certain similarities as well as differences in the composition of their intestinal flora, at both species and phylum levels. While the similarities are attributed to their shared domestication conditions and food source, the differences may be due to differences in the age, sex, and physiological state of the individuals. Therefore, in the future, attention should be paid to the composition of the intestinal flora of more dog breeds under different environments and the effects of age and physiological state should be comprehensively analyzed. The results of this study provide theoretical basis and technical support for the development of probiotic strains that can regulate the intestinal function of pets and maintain the intestinal microecological balance of police dogs.

Author contributions

ZCL and LGC conceived the study. QMH, LGC, XTC, YJM and SYZ contributed to the sample collection and laboratory work. QMH and FS analyzed the data. ZCL and QMH wrote and revised the manuscript. LLM and JYL provided valuable suggestions and revisions. All the authors have read and approved the final version of this manuscript.

Funding

This research was funded by the Science and Technology Planning Project of the Ministry of Public Security (2021JC25), Yunnan Provincial Ten Thousand People Plan (YNWR-QNBJ-2018-161), and Frontier Research Team of Kunming University 2023.

Data availability

All the sample raw reads obtained in this study have been deposited at the National Center for Biotechnology Information (accession number: PRJNA1088290).

Ethics approval and consent to participate

Animal care and handling were conducted in accordance with the guidelines of the Experimental Animal Welfare Ethics Committee of Kunming University (Kunming, China).

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests.

Acknowledgments

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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Comparative analysis of the gut microbiota of the Chinese Kunming dog, German Shepherd, and Belgian Malinois

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Abstract

Figures

Introduction

Materials and Methods

Animals and diets

Sample collection

DNA extraction

16S rRNA gene sequencing

Statistical analysis

Results

DNA extraction and 16S rRNA gene sequencing

Composition of intestinal flora of different dog breeds

Alpha and beta diversity analysis of the intestinal flora of CKD, BMD, and GSD

Functional prediction of the intestinal flora

Discussion

Conclusion

Declarations

References

Additional Declarations

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