Seasonal Breeding Leads to Changes for Gut Microbiota Diversity in the Wild Ground Squirrel (Spermophilus Dauricus)

doi:10.21203/rs.3.rs-96089/v1

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Seasonal Breeding Leads to Changes for Gut Microbiota Diversity in the Wild Ground Squirrel (Spermophilus Dauricus)

https://doi.org/10.21203/rs.3.rs-96089/v1

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Background: Seasonal breeding is a normal phenomenon that animals adapt to natural selection and reproduce only in specific seasons. With the gradual popularization of Next-generation sequencing (NGS), large studies have shown that seasonal breeding has been affected by gut microbiota. Consequently, the purpose of this study is to explore the effect of seasonal breeding on the gut microbiota of wild ground squirrel (Spermophilus dauricus). We used 16S rRNA gene sequencing technology to sequence the gut microbiota of the wild ground squirrel in the breeding season and non-breeding season. We also predicted the function of gut microbiota by bioinformatic software.

Results: The results showed that the main components of gut microbiota in all samples consisted of Firmicutes (61.8%), Bacteroidetes (32.4%), and Proteobacteria (3.7%). Microbial community composition analyses revealed significant differences between these two groups. At the genus level, Alistipes, Mycoplasma, Anaerotruncus, and Odoribacter were up-regulated in the non-breeding season, while Blautia and Streptococcus spp. were up-regulated in the breeding season. The result of function prediction suggested that the relative abundance of functional categories related to lipid metabolism, carbohydrate metabolism, and nucleotide metabolism was higher in the breeding season. The expression of transcription, energy metabolism, and signal transduction was enriched in the non-breeding season.

Conclusions: Overall, the results of this study emphasized the significant effects of seasonal breeding on gut microbiota community composition of the wild ground squirrel and laid a foundation for further study of gut microbiota on seasonal breeding in the future.

Epigenetics & Genomics

seasonal breeding

gut microbiota

wild ground squirrel

lipid metabolism

energy metabolism

The gut microbiota of animals is composed of a large number of microbes. The micropopulation stays in the host and interacts with each other, forming a balanced, complex, and diverse gut microbiota system [1]. Gut microbiota is closely related to the physiological activities and growth of the body, and participate in many physiological processes, including metabolic, reproduction [2, 3].

With the gradual popularization of Next-generation sequencing (NGS), which has low cost and a large amount of data, research on gut microbiota has gradually increased. Many studies have shown that gut microbiota has essential effects on host growth [4], bone mineral density [5], energy metabolism [2] and immune regulation [6]. Furthermore, it has been found that gut microbiota is closely linked with a variety of diseases, including metabolic diseases such as obesity [7, 8] and diabetes [9], neurological diseases such as Alzheimer's disease [10, 11] and Parkinson's disease [12], cardiovascular diseases such as hypertension [13] and atherosclerosis [14], cancer [15] and liver cirrhosis [16], etc…The effect of gut microbiota on reproduction has also received considerable attention. For example, studies on zebrafish (Danio rerio) illustrated that probiotic Lactobacillus rhamnosus could activate leptin, which regulates the hypothalamus-pituitary-gonadal(HPG) axis to affect reproduction, thereby promoting the maturation of follicles and improving the reproductive ability of animals [3]. Besides, in the study of human beings, significant changes in gut microbiota diversity occur during pregnancy, thus adjusting their metabolism to adapt to the growing energy needs [17]. Gut microbiota also plays an important role in the synthesis, metabolism, and recycling of nitrogenous compounds such as amino acids, It has a significant influence on the host in terms of fecundity [18]. Therefore, the gut microbiota is crucial for the health and physiological activities of the host, including maintaining the health and ensuring the reproductive capacity of the host.

The seasonal breeding strategy is a survival strategy that animals can reproduction in the most favorable environment. Survival and reproduction are the primary tasks of each individual, which depend on their ability to adapt to seasonal variation, to meet their own needs under the conditions of changes in food distribution, supply, and abundance [19]. The reproductive function of mammals is mainly regulated by the HPG axis. The hypothalamus secretes gonadotropin releasing hormone (GnRH), which stimulates the anterior pituitary to secrete gonadotropins, such as luteinizing hormone (LH) and follicle stimulating hormone (FSH). Therefore, the HPG axis was activated only during the breeding season [20]. Studies have shown that the reproductive system of seasonal breeding animals is regulated by melatonin [21], thyroid hormone (TH) [22], kisspeptin [23], gonadotropin inhibitory hormone (GnIH) [24, 25],leptin [26], and other hormones. In recent years, the gut microbiota has been shown to interact with estrogens [27]. Estrobolome gene exists in gut microbiota, which is defined as the gene pool that can metabolize estrogen [28]. The gut microbiota can affect the estrogen level by the secretion of β-glucuronidase, which can bind to estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) and affect downstream physiological effects [27]. However, the mechanism of how gut microbiota affects seasonal breeding is still unclear.

The wild ground squirrel (Spermophilus dauricus) belongs to Mammalia, Rodentia, Sciuridae, and Spermophilus, which is a typical seasonal breeding small mammal. During the year, the wild ground squirrel doesn’t breed in any month except April to May. Therefore, the wild ground squirrel includes breeding season (April to May) and non-breeding season (June to March of the following year). The female wild ground squirrel is most likely to become pregnant in May [29–31]. At the same time, the wild ground squirrel is a very suitable animal model for the study of seasonal breeding. Previous studies have showed that android receptor (AR), estrogen receptors α and β (ER α, ER β), and aromatase cytochrome P450 (P450arom) are seasonally expressed in the hypothalamus, hippocampus, uterus, testis, and epididymis [32–36]. Luteinizing hormone receptor (LHR), Follicle stimulating hormone (FSHR) also showed seasonal expression in testis and epididymis [37, 38]. In addition, ghrelin, obestatin, insulin, and other gut-derived hormones have direct or indirect effects on the reproductive axis and play their roles in regulating energy balance and reproductive function [39]. In summary, the important role of gut microbiota in regulating various physiological activities of the host prompted us to research on seasonal breeding and gut microbiota. Thus, the study aimed to explore the effect of seasonal breeding on the gut microbiota of the wild ground squirrels, and to explore the differences of gut microbiota between breeding season and non-breeding season, further analyzed the main functional differences.

2.1 Sample collection

All wild ground squirrels were collected by mousetraps in the wild in Hebei Province, China. Six individuals were captured in the breeding season (B; April to May; n = 6) and non-breeding season (NB; June to September; n = 6) respectively. The gut contents were collected as soon as possible after the animals were captured (within 24hours). The animals were killed by decapitation after anesthesia. The surface of animals was disinfected with alcohol. The contents of the gut tract were collected quickly by opening the abdominal cavity. After collection, the intestinal contents were frozen in liquid nitrogen immediately and stored at − 80℃ before DNA extraction.

All animal experiments were approved by the Policy on the Care and Use of Animals by the Ethics Committee of Beijing Forestry University and Hebei Provincial Department of Agriculture (jnzf11 / 2007).

2.2 DNA extraction and sequencing

Total genomic DNA in each gut sample was extracted according to the instructions of the TIANamp DNA Kit (TIANGEN BIOTECH Inc., Beijing, China). The quality and integrity of DNA were evaluated by the A260/280 ratio and agarose gel electrophoresis. After extracted the DNA from gut samples,V3–V4 region of the bacteria 16S rRNA gene was amplified by two-step PCR, which was used forward primer 340F (5’-ACTCCTACGGGAGGCAGCAG-3’) and reverse primer 806R (5’- GGACTACHVGGGTWTCTAAT-3’). All PCR reactions were carried in 25 ul total volume consisting of 12.5 ul KAPA HiFi HotStartReadyMix (2×), 0.25umol L-1 of each primer, and 10 ng of DNA template. KAPA HiFi Hotstart PCR Kit (KAPA Biosystems, United States) was used for the first round of PCR amplification. First, pre denaturation at 95℃ for 3 min, then denaturation at 95℃, annealing at 55℃, and extension at 72℃ for 30 s respectively. These three steps are subjected to 25 cycles, and finally extended at 72℃ for 5 min. The PCR products were purified with 1 x AMPure XP Beads, and then perform the second round PCR amplification was performed. Except for 8 cycles, the other cycle steps were the same as the first round PCR amplification. The PCR products of each sample were mixed to prepare the PCR amplicon libraries. Amplicons were extracted from 2% agarose gels and recycled using the QIAquick Gel Extraction Kit (QIAGEN, CA, United States), and then quantified using the KAPA Library Quantification Kit (KAPA Biosystems, United States) as the standard of Libraries mixing. After the quality assessment and quantification, the same amount of amplified products were collected and sequenced by pair-end 2 × 300-bp in the Illumina Miseq platform at ORI-GENE Technology Co., Ltd. (Beijing, China).

2.3 Bioinformatic analyses

High throughput sequencing results were received as the FASTQ file. Paired-end reads were merged using VSEARCH (v2.14.1) [40] to form consensus sequences and truncated at both primers, then reads with the error threshold above 0.01 were discarded. After removed low abundance noise with miniquesize 8, sequences were denoised to obtain single-base precision amplicon sequence variants (ASVs) using the UNOISE3 in USEARCH (v10.0.240) [41, 42], during which part of chimeric sequences was also removed. To improve accuracy, the Ribosomal Database Project (RDP) (v1.6) was used as a reference sequence to remove chimeras again. Further, a feature table was created using USEARCH (v10.0.240) [43]. Subsequently, the taxonomic origin of each ASV was determined in VSEARCH (v2.14.1) with the confidence value of 0.60, based on the RDP (v1.6), and plastid and non-Bacteria were removed.

The 16S rRNA gene sequences were analyzed mainly using USEARCH (v10.0.240) and R (v3.6.2). After normalizing by subsample, the community richness index and community diversity index were calculated using the Vegan package [44] to determine the alpha diversity within the groups of the wild ground squirrels. Then, the number of ASVs in 1% − 100% sequence without replacement was taken to calculate the richness change of the dilution process. Next, β-diversity was calculated using the Bray-Curtis distance and visualized with Principal Coordinate Analysis (PCoA) analyses to find the difference of microbiota structure among groups. Taxonomies can annotate species information from phylum to genus level, and Linear Discriminant Analysis Effect Size (LEFSe) was used to analyze the difference at each level [45]. Microbial functions were predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), based on high-quality sequences [46]. STAMP (v2.1.1.0) [47] was used to explore the functional pathways with significant differences. The R script is used to generate the corresponding figures.

3.1 Data summary

A total of 1812298 16S rRNA gene reads were obtained from the raw offline data, with an average of 151025 reads per sample. The raw data were merged at two ends, and the clean sequences were obtained after quality control. The number of ASV in each sample was obtained by denoising based on the clean sequences, eliminating the wrong sequence, and selecting the credible sequence with higher abundance as the representative sequence. A total of 1638 ASVs were obtained from 12 samples. The average number of ASV in the breeding season was 847 and that in the non-breeding season was 224 (Table S1).

To evaluate whether the sequencing depth was enough to make a stable estimation of the species richness we used the rarefaction curve, which tends to be gentle indicated that sequencing is saturated and there is no need to increase the sample size. The sequencing depth has basically covered all species in the sample, and the marginal contribution of more data to the discovery of new ASV is very small (Fig.S1).

3.2 Microbial Diversity

We analyzed chao1, ACE, Shannon, and Simpson as four common alpha diversity indexes of gut microbiota (Table 1). The alpha diversity index showed that there was no significant difference in both the diversity index and the abundance index of gut microbiota between breeding season and non-breeding season.

Table 1

Number of community richness index (ACE and Chao1), community diversity index (Shannon and Simpson) for each sample.
SampleID	Chao1	ACE	Shannon	Simpson
B1	1189.168	1161.4	5.188146	0.980939
B2	960.5446	952.2633	4.300073	0.944246
B3	1067.103	1022.224	5.406034	0.989723
B4	962.8922	948.7459	3.569155	0.841802
B5	883.2471	863.3297	4.720447	0.971907
B6	393.6512	397.6003	2.217882	0.750912
NB1	1025.778	1027.65	4.867893	0.967579
NB2	1022.585	989.4314	5.301287	0.98375
NB3	905.1235	870.5195	4.879087	0.970492
NB4	957.8264	957.1833	4.542708	0.956739
NB5	1029.827	1018.301	5.195961	0.986655
NB6	930.1932	895.0828	4.755879	0.976622
B, breeding season; NB, non-breeding season

In the statistics of β diversity, the PCoA analysis was used to determine the differences between these two groups. PCoA plots and sample correlation heatmap based on the Bray-Curtis distance matrices showed that the samples of the breeding season and non-breeding season could cluster basically. The first principal axis can explain 25.24% of the total sample difference, and the second principal axis can explain 12.46% of the sample difference, indicating a possible effect of seasonal breeding strategy (Fig. 1).

3.3 Taxonomic Composition of Microbiotas

At the phylum level, three major bacterial were found in the wild ground squirrel gut microbiota, most of which were classified as Firmicutes (61.8%), followed by Bacteroidetes (32.4%), and some Proteobacteria (3.7%) (Fig. 2 and Table S2). The other phyla were Verrucomicrobia, Elusimicrobia, Actinobacteria, and Tenericutes, but not abundant (< 1%). The rest of the sequences of the wild ground squirrel gut microbiota were not annotated, called unassigned group.

At the genus level, the unassigned group also means that the sequences cannot be compared to any known genus. The proportion of this unassigned group at the genus level is 46.2% in all samples. The top 13 genera were listed in Fig. 2d and the top four genera in all samples are Lactobacillus (7.9%), Barnesiella (7.4%), Streptococcus (6.3%), Ruminococcus (5.7%) (Table S3).

3.4 Analysis of the microbiota difference between groups

To identify specific microbial communities that exist in the breeding season and non-breeding season, we used the LEFSe analysis to explore the microbial community in these two different periods (Fig. 3). At the genus level, the wild ground squirrel in the non-breeding season had a significantly higher relative abundance of Alistipes, Mycoplasma, Anaerotruncus and Odoribacter. In contrast, the wild ground squirrel in the breeding season had significantly more Blautia and Streptococcus spp. At the order level, the number of Rhodospirillales in the non-breeding season was significantly more than that in the breeding season, and Lactobacillales are more in the breeding season. At the class level, Alphaproteobacteria were more in the non-breeding season, and Bacilli were more in the breeding season.

We also draw the Manhattan plot at the phylum level with the negative logarithm of the P-value, which showed that Proteobacteria, Tenericutes, Verrucomicrobiais were significantly down-regulated in the breeding season compared with the non-breeding season, and the abundance of Elusimicrobia was significantly higher in the breeding season (Fig. 4). These results suggest that there are abundant microbial communities in the gut of the wild ground squirrel, and their composition and relative abundance may affect the physiological activities of the host.

3.5 Analysis of the functional difference of gut microbiota between groups

We used PICRUSt to predict the function of gut microbiota. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, analysis of functional categories of the KEGG pathway indicated that the predicted functional categories changed significantly between the breeding season and the non-breeding season, including cell motility, carbohydrate metabolism, nucleotide metabolism, transcription, energy metabolism, signal transduction, lipid metabolism and cancers (Fig. 5 and Table S4). The relative abundance of functional categories related to carbohydrate metabolism, nucleotide metabolism, and lipid metabolism was higher in the breeding season. Besides, the expression of transcription, energy metabolism, and signal transduction was higher in the non-breeding season. These results illustrated that the metabolic function of gut microbiota in the seasonal breeding wild ground squirrel is altered by the changes in season.

The seasonal breeding of animals is a phenomenon that only occurs in the specific season. In this study, for the first time, we used the NGS to explore the composition of gut microbiota in the wild ground squirrel, and we demonstrated the effect of seasonal breeding on gut microbiota. Diet, phylogeny, age, genotype, and other factors can affect the composition of host gut microbiota [48–51]. Among them, diet is considered to be the most important factor [52]. Our study found that there was no significant difference in the diversity and richness of gut microbiota between the breeding season and the non-breeding season of the wild ground squirrel. It may be due to the foraging environment, which relatively free in the wild and their feeding preference is not easy to change. The PCoA based on the Bray-Curtis distance matrices showed that the community structure of the wild ground squirrel gut microbiota clustered basically by breeding seasons, with differences in community structure between samples from the breeding season and non-breeding season.

The analysis for the taxonomic of gut microbiota showed that the main bacteria of both breeding season and non-breeding season wild ground squirrel belonged to Firmicutes and Bacteroidetes, which are the same as the dominant microbiota species of mammalian gut [53]. In addition, these results are consistent with studies on the gut microbiome of other Spermophilus mammals, such as arctic ground squirrels (Urocitellus parryii) [54] and 13-lined ground squirrels (Ictidomys tridecemlineatus) [55]. Firmicutes can degrade polysaccharides through amyloplast and cellulosome multi-enzyme complexes [56], and it has a positive effect on nutrient and energy absorption from feed ingredients [57]. Bacteroides can not only degrade polysaccharides, carbohydrates, and proteins improve in the gut but also can assemble polysaccharides to assist the host to get nutrients from the diet [56, 58]. Besides, it can also improve the gut environment and make it more friendly to itself and other microorganisms [56]. At the same time, there is a mutual promoting symbiosis between Firmicum and Bacteroides. The high abundance of Firmicum and Bacteroides have the potential to promote the host to absorb or store energy and the high proportion of Firmicum / Bacteroides are also related to the prevalence of some diseases [59, 60].At the genus level, Lactobacillus has the largest proportion, which is similar to other rodent mammals like wild wood mice (Apodemus sylvaticus) [61] and Siberian hamsters (Phodopus sungorus) [62]. These results also indicate that in the evolution of vertebrate gut microbiota, the species of bacteria that can settle in the gut are limited [63].

The wild ground squirrel of the breeding season and non-breeding season had four representative different communities at the phyla level, including Proteobacteria, Tenericutes, Verrucomicrobiais, and Elusimicrobia. Proteobacteria have versatile physiology and greatly variable morphology, which are the most unstable phylum compared with the other three major phyla (Firmicutes, Bacteroidetes, and Actinobacteria) [64]. The phylum can be divided into six classes according to the rRNA sequence, including Alpha-, Beta-, Gamma-, Delta-, Epsilon-, and Zetaproteobacteria. It can adjust metabolism flexibly and tolerate low nutritional food [65], but at the same time, the growth of Proteobacteria can cause biological diseases, which can be used as a diagnostic marker of potential disease risk [66]. Our study showed that the phylum of Proteobacteria, the class of Alphaproteobacteria and the order of Rhodospirillales increased in the gut microbiota of the wild ground squirrel during the non-breeding season, we hypothesize that Proteobacteria which is high abundance in the non-breeding season helps to improve the digestion and absorption efficiency of the wild ground squirrel and adjust its metabolism to balance its consumption during the breeding period. Tenericutes are a special class of bacteria that famous for lack of cell walls, consisting of the sole class Mollicutes [67]. The most significant genus in the phylum Tenericutes is Mycoplasma (Mycoplasmataceae, Mycoplasmatales) [68]. In humans, Mycoplasma (Mycoplasmataceae, Mycoplasmatales, Tenericutes) infection can cause some reproductive disorder diseases [69, 70]. Our study shows that the relative abundance of Mycoplasma in the non-breeding season is significantly increased, which indicates that Mycoplasma has a certain negative impact on reproduction. During the breeding period, the abundance of order Lactobacillales belongs to the class Bacilli increased significantly. Though Lactobacillales, one of the diverse and phylogenetically heterogeneous orders of lactic acid producing bacteria, are not dominant microbiota in the gut, they play an important role in animal reproduction. They can use carbohydrates through fermentation to produce lactic acid and have a bearing on vitamin B9 (folate) biosynthesis in humans [71, 72]. Folate is mainly involved in the synthesis of genetic material, which is essential for cell growth and reproduction [73]. Besides, Lactobacillales can also indirectly regulate the HPG axis through the activation of leptin [3]. Therefore, the increase of Lactobacillales in the wild ground squirrel during the breeding season is conducive to the improvement of reproductive performance.

In this study, we also used PICRUSt to predict the potential function of the gut microbiota, and the functional pathways with significant differences in expression between the breeding season and non-breeding season were identified. The results showed that three KEGG pathways (carbohydrate metabolism, nucleotide metabolism, and lipid metabolism) were significantly expressed in the breeding season, and five KEGG pathways (cell motility, transcription, energy metabolism, signal transduction, and cancers) were increased in the non-breeding season. It has been reported that gut microbiota plays an important role in a variety of metabolic processes in the host, including glucose metabolism, lipid metabolism, and energy homeostasis [74]. In terms of lipid metabolism, gut microbiota may affect host lipid metabolism through metabolites and lipopolysaccharides produced by gut microbiota [75]. Lipid metabolism affects fetal growth and late pregnancy outcome. Studies in women and rats have found that fat pools accelerate breakdown during the last third of pregnancy [76]. Our results indicated that the increase of lipid metabolism pathway expression in the wild ground squirrel during the breeding season may be due to the increased expression of gut microbiota related to lipid metabolism. It avoids the negative effect of fat accumulation on the body in the breeding season. The interaction between carbohydrate and lipid metabolism is mutual [77], so it is understandable that the carbohydrate metabolism pathway is up-regulated during the breeding season as a lipid metabolism pathway. We speculate that the reason for the up-regulation of the energy metabolism pathway in the non-breeding period may be the host needs to metabolize the energy accumulated during the breeding season to maintain its health. These pathways may contribute to the reproduction and homeostasis of the wild ground squirrel.

In summary, we sequenced the gut microbiota of the wild ground squirrel in the breeding season and non-breeding season by 16S rRNA gene sequencing technology to learn the effects of seasonal breeding on gut microbiota. The difference analysis of LEFSe showed that there were different expression groups at phylum to genus level in the breeding season and non-breeding season. The functional prediction results of PICRUSt showed that the gut microbiota with significant differences mainly existed in metabolic pathways under different reproductive strategies. These results suggest that the seasonal breeding of animals has a certain impact on gut microbiota. The results of this study expand our knowledge of the symbiosis and co-evolution of seasonal breeding animals and their gut microbiota, and provide a prerequisite for future studies on the special effects of gut microbiota on seasonal breeding through metagenomic analysis.

NGS

Next-generation sequencing; HPG axis:hypothalamus-pituitary-gonadal axis; GnRH:gonadotropin releasing hormone; LH:luteinizing hormone; FSH:follicle stimulating hormone; TH:hyroid hormone; GnIH:gonadotropin inhibitory hormone; ERα:estrogen receptor alpha; ERβ:estrogen receptor beta; AR:android receptor; P450arom:aromatase cytochrome P450; LHR:Luteinizing hormone receptor; FSHR:Follicle stimulating hormone; ASVs:amplicon sequence variants; RDP:Ribosomal Database Project; LEFSe:Linear Discriminant Analysis Effect Size; PICRUSt:Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; KEGG:Kyoto Encyclopedia of Genes and Genomes

Acknowledgements

The authors are grateful to those who helped with this project.

Authors’ contributions

XY Y: Conceptualization, Resources, Writing – original draft, Writing - review & editing. YC Y: Resources, Investigation, Writing - review & editing. XY Z, JH Z: Resources. FL G, HL Z, YY H, Q W: Conceptualization, Writing - review & editing. ZR Y: Conceptualization, Writing - review & editing, Supervision. All authors read and approved the final version of this manuscript.

Funding

No funding.

Availability of data and materials

All data generated or analyzed during this study can be made available by the corresponding author upon reasonable request.

Ethics approval and consent to participate

Consent for publication

Not applicable.

Competing Interests

The authors declare no conflicts of interest.

Author details

¹College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, PR China.

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Additionalfile1.docx
Table S1. Raw data before and after standard quality control (QC) filters.
Additionalfile2.pdf
Figure S1. Alpha diversity rarefaction plots of each group. B, breeding season; NB, non-breeding season.
Additionalfile3.docx
Table S2. Abundance of the major phylum in each group.
Additionalfile4.docx
Table S3. Abundance of the major genus in each group.
Additionalfile5.docx
Table S4. Statistical analysis of significant functional differences between the two groups.

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Seasonal Breeding Leads to Changes for Gut Microbiota Diversity in the Wild Ground Squirrel (Spermophilus Dauricus)

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Abstract

Figures

1. Background

2. Methods

2.1 Sample collection

2.2 DNA extraction and sequencing

2.3 Bioinformatic analyses

3. Results

3.1 Data summary

3.2 Microbial Diversity

3.3 Taxonomic Composition of Microbiotas

3.4 Analysis of the microbiota difference between groups

3.5 Analysis of the functional difference of gut microbiota between groups

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Supplementary Files

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