The  m assive  d ifferences in  d iet  i nduced an  a ltered  g ut  m icrobiota  s tructure in  m andarin  d uck  (  Aix    g   alericulata  )  at  b reeding  a rea

doi:10.21203/rs.3.rs-1413730/v1

PDF

Research Article

The m assive d ifferences in d iet i nduced an a ltered g ut m icrobiota s tructure in m andarin d uck ( Aix g alericulata ) at b reeding a rea

https://doi.org/10.21203/rs.3.rs-1413730/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Animal gut is a dynamic ecosystem, and there are many factors affecting the structure of intestinal flora and the diversity of intestinal microbes, among which food differences are the direct causes. To investigate the effect of food composition on the intestinal flora structure of mandarin duck during the reproductive period, 9 feces were collected from 2 foraging sites of Shiqian Mandarin Duck Lake National Wetland Park, Guizhou, China. We analyzed the chloroplast rbcL gene, mitochondrial COI gene and 16S rRNA gene from the total DNA of the feces in detail to know the plants and animals in food compositions and intestinal bacteria of mandarin duck by high-throughput sequencing technology. We found that the gut microbiota composition were significantly correlated with the number of feeding species, the mandarin duck with a wide variety of feeding food had the more complex gut flora with more probiotics and less pathogenic bacterium. This study provides further theoretical support for food difference to affect the changes of intestinal bacterial structure of host, and provides scientific basis for understanding the feeding preference of mandarin duck at breeding area.

food composition

gut microbiota structure

high throughput sequencing technology

mandarin duck

During the breeding period, the food of mandarin ducks mainly came from Poales plants and arthropods.

The gut microbiota structure of mandarin ducks were mainly affected by food rather than hosts themselves.

Mandarin ducks with a rich diet were physically healthier.

Animal gut is the natural host of microorganisms, which plays an important role in the physiological health, food digestion and nutrition acquisition of animal hosts (Grond et al. 2018; Waite and Taylor, 2015). ^{The host gut interacts with the microbial flora}, t^{he stable gastrointestinal flora includes probiotics, pathogenic bacteria and intermediate bacteria between them. The stable microbial flora is conducive to improving the body}’^{s resistance, and the disorder of intestinal flora will lead to a series of diseases} (Fu et al. 2020), such as cardiovascular system, immune system, nervous system and metabolic system and other aspects of the disease (Spor et al. 2011). As the results show that the main factors affecting the structure of intestinal flora include environmental factors, dietary differences, behavior habits and host factors (Kasper, 2014; Perofsky et al. 2019), among them, dietary differences and environmental factors are more important than other factors on the structure of intestinal flora (Muegge et al. 2011; Delsuc et al 2014; Sanders et al. 2015). In particular, the feeding habits of the host can not only affect but also determine the composition of intestinal microbiota to a certain extent (Semova et al. 2012). For example, studies on Grus monacha and Tetrao urogallus show that the composition of intestinal microbes changes due to the changes in food resources with the change of season (Zhao et al. 2017a). Studies on geese also showed that food differences affected the composition of intestinal microbiota (Yang et al. 2016). However, at present, animal diet are mostly studied by observation or fecal microscopic identification, therefore, it is very important to investigate the relationship between accurately food composition differences and intestinal microbiota structure of endangered animals.

The study of animal diet is one of the most important contents in ecology. Diet is a bridge to understand the relationship between animals and environment and between predators and prey. It is the source of energy and nutrition for animal survival and reproduction (Harwood et al. 2005; Liu et al. 2018). Most of the traditional research methods on animal diet are based on morphological identification (Shibazaki and Hoshi 2006; Lee et al. 2014) or depending on hair, blood and tissue damage sampling, its technology and application scope are limited (Xiong et al. 2016). In recent years, with the development of high-throughput technology, DNA macro barcodes have been widely used in ecological research. High throughput sequencing technology can classify food to species level taxon with high accuracy, which greatly promotes the study of animal diet and food web (Taberlet et al. 2012), Some studies show that the number of species in feces detected by high-throughput sequencing technology is more than that by microscopic analysis (Ando et al. 2013), so it is widely used in the analysis of animal diet, as the study on Suncus murinus and Leiolopisma telfairii (Brown et al. 2013), Propithecus tattersalli (Erwan Quéméré et al. 2013), Orcinus orca (Ford et al 2016), Ammodramus bairdii and Ammodramus savannarum (Titulaer, et al. 2017), bats (Galan et al. 2017) and so on. However, DNA barcoding also faces the challenges of searching candidate genes and constructing and managing reference libraries (Elliott and Jonathan, 2014). At present, in animal research, mitochondrial cytochrome c oxidase subunit I (COI) gene is recognized as a universal DNA barcode in the animal world (Deagle et al. 2009; Leray et al. 2013). In herbivores, chloroplast genes with fast evolutionary rate become alternative selection because of the slow evolutionary rate of mitochondrial genes in plants (Chase et al. 2005), and different marker genes such as chloroplast psbA-trnH and ribulose 1, 5-bisphosphate carboxylase (rbcL) are usually used (Pompanon et al. 2012; Garcıa-Robledo et al. 2013). TrnH gene has good amplification ability and discrimination ability, but the insertion deletion is serious and the sequence length is different (Shaw et al. 2007; Hollingsworth et al. 2009), while rbcL gene amplification efficiency is very high and has good versatility (Chase et al. 2007), so it is widely used in the study of herbivore feeding habits, for example, the chloroplast gene rbcL was used to study herbivores' feeding habits (Erickson et al. 2017).

The mandarin duck (Aix galericulata), belonging to the genus Aix of Anatidae, is a second-grade animal of protection in China's SEPA (State Environmental Protection Administration) and one of the important species in wetland ecological quality evaluation. In China, the breeding grounds of mandarin ducks are concentrated in the northern and central parts of Northeast China, North China and Northeast Inner Mongolia and the wintering grounds pass through North China to the south of the Yangtze River provinces (Zheng, 2017). The larger wintering populations of mandarin ducks were mainly distributed in Yuanyang Xi (Pingnan County, Fujian Province), Yuanyang Lake (Wuyuan County, Jiangxi Province) (He et al. 2014) and Shiqian Mandarin Duck Lake National Wetland Park (Shiqian County, Guizhou Province) (Zhu et al. 2010). It has been found that Shiqian Mandarin Duck Lake National Wetland Park is also a breeding ground for mandarin ducks, this is a magic phenomenon (Fang, 2019). At present, the research on Mandarin Duck mainly includes daily activity rhythm, nesting behavior (Zhi et al. 2019; Gong et al. 2018), virus infection (Kwon et al. 2017) and the diversity of intestinal flora of mandarin ducks in the wintering period (Fang et al. 2019). However, there is still a lack of research on the composition and diversity of intestinal microbiota and the relationship between intestinal microbiota structure and food composition in breeding period.

In this study, we used non-invasive sampling method, relying on high-throughput sequencing technology, based on bacterial common gene 16S rRNA In v3-v4 area, the intestinal microbiota structure of mandarin ducks in Shiqian Mandarin Duck Lake National Wetland Park. The intestinal bacteria identification and functional analysis were carried out to reveal the diversity of intestinal bacteria of mandarin ducks in breeding period; the composition of animal-based foods and plant-based foods in mandarin duck was determined by mitochondrial COI gene and chloroplast rbcL gene apartly. The relationship between food composition differences and intestinal microbiota structure of mandarin ducks was discussed, so as to know which is the the determining factor in host intestinal bacterial structure changes. At the same time, by understanding the feeding preference and feeding diversity of mandarin ducks, which will provide a new perspective for the food supplement of Mandarin ducks in the wild when food are scarce.

Ethics and sampling

This study conformed to the guidelines for the care and use of experimental animals established by the Ministry of Science and Technology of the People’s Republic of China (Approval number: 2006 − 398). The research protocol was reviewed and approved by the Ethical Committee of Guizhou University. We only collected feces for relevant studies, did not involve capture or any direct manipulation or disturbance of wild mandarin duck in the fieldwork.

From May 12 to 18, 2020, in Shiqian Mandarin Duck Lake National Wetland Park, two foraging places were chose where mandarin ducks population are stable and easy to collect feces, the feeding situation of mandarin ducks was observed from a distance about 500m with 10 × 60 binoculars. After they left, we quickly went to the places to collect fresh feces. In order to avoid sampling from the same individual and cross contamination, the upper layer of feces at a distance of more than 5m were quickly taken with disposable disinfection gloves and put into sterile centrifuge tube then stored at the portable ice box and returned to the laboratory within 24 hours and subsequently keep them in refrigerator at − 80 ℃. A total of 9 feces were collected and divided into group A and B according to the sample location. Point A is close to the reservoir footpath, close to the living area, basically surrounded by canyons, with great human interference. Point B is far away from the living area and located at the upstream of the reservoir. It is basically surrounded by shrub vegetation. The habitat is relatively sheltered and there is little human interference. The detailed information is shown in Table 1.

Table 1

Fecal sampling information of mandarin duck.
Latitude and longitude of foraging ground	Sample size	Sample number	Group information
108.26832073; 27.44583439	5	D1, D2, D4, D5, D6	A
108.26823953; 27.44313009	4	D3, D7, D8, D9	B

Total DNA extraction, PCR amplification and sequencing

The total genomic DNA was extracted from a 0.5g portion of each fecal sample using the Stool Genomic DNA Kit (Beijing ComWin Biotech Co., Ltd, China) following the manufacturer’s protocol. The concentration and purity were detected by fluorescence quantitative analysis(BioTek). The food composition of mandarin duck was detected by chloroplast gene rbcL and mitochondrial gene COI primers, and the intestinal flora was detected by bacterial 16S rRNA v3-v4 region universal primers. The detailed sequence of primers is shown in Table 2. The PCR Amplification system(25 µl): 5 µl of reaction buffer (5×), 5 µl of GC buffer (5×), 2 µl(2.5 mM)of dNTP, 1 µl (10 uM) of each Forward and Reverse primer, 2 µl of DNA Template, 0.25 µl of Q5® High-Fidelity DNA Polymerase and 8.75 µl of ddH₂O. The PCR procedure was initial denaturation at 98 ℃ for 2min, 35cycles of denaturation at 98 ℃ for 15s, annealing at 55 ℃ for 30s, extension at 72 ℃ for 30s, and a final extension at 72 ℃ for 5min. The amplified products were detected by agarose gel electrophoresis of 1.2% concentration. After recycling the target strip with the QIAquick Gel Extraction Kit (Qiagen), the products were sent to the Shanghai Personal Biotechnology Co., Ltd (Shanghai, China) for high-throughput sequencing using the Illumina MiSeq system (Illumina, CA, USA) according to the manufacturer’s instructions.

Table 2

Table of the primer information .
Primer Name	Primer Sequence	Reference documentation
rbcL	Z1aF: ATGTCACCACCAACAGAGACTAAAGC hp2R: CGTCCTTTGTAACGATCAAG	Hofreiter et al, 2010
COI	COIintF: GGWACWGGWTGAACWGTWTAYCCYCC COIjgHCO2198: TANACYTCNGGRTGNCCRAARAAYCA	Geller et al, 2013 Leray et al, 2013
16S rRNA v3-v4	338F:ACTCCTACGGGAGGCAGCA 806R:CGGACTACHVGGGTWTCTAAT	Lee et al, 2012

Data analysis

QIIME2 software (Bolyen et al. 2018) was used to identify and eliminate the interrogative sequences. DADA2 method (Callahan et al. 2016) was used to remove primers, quality filtering, denoise. After splicing and chimera detection, the remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity by Vsearch (Rognes et al. 2016). OTUs containing less than 0.001% of total sequences across all samples were discarded to obtain high-quality sequences. The sequences of rbcL and COI genes were compared by NCBI blast database. The 16S rRNA gene of bacteria was sequenced by using the Greenenes Database (DeSantis et al, 2006). The OTU representative sequences that failed to be classified into known taxa were classified as "unclassified". Using QIIME2 software, random sampling the sequence number of samples and its representative OTUs to draw the rarefaction curve. According to the results of sequence taxonomic annotation and the selected samples, the species composition at seven classification levels of domain, phylum, class, order, family, genus and species were counted. Based on 97% sequence similarity, the alpha diversity index was calculated, and the data were drawn into box chart to show the alpha diversity difference between different sample groups. At the same time, the species composition differences among samples were explored by species composition heatmap analysis. The 16S rRNA gene sequence in MetaCyc (https://metacyc.org/) was analyzed by PICRUSt2 software (Langille et al. 2013; Caspi et al. 2008) and KEGG (https://www.kegg.jp/) (Minoru et al. 2012) function prediction is carried out in the database.

Sequencing data analysis

Illumina Miseq sequencing of rbcL gene yielded a total of 828610 effective data, the COI gene yielded a total of 712987 effective data and the V3-V4 regions of the bacterial 16S rRNA yielded a total of 884807 effective data. According to the rarefaction curve, when the sequencing depth reaches 40000(Fig. 1a and Fig. 1b) and 30000(Fig. 1c), the sample curve basically tends to be flat and continues to maintain the level of sequencing curve, which indicates that the current sequencing depth is enough to reflect the diversity of samples and meet the analysis requirements of subsequent analysis.

Species composition analysis at different classification levels in fecal samples

The chloroplast rbcL gene was used to sequence the plant-based composition of mandarin ducks. At the order level, there were only three orders in group A had relative abundance of more than 1% in the top 15 orders, which mainly composed Poales (75.75%), Fagales (15.56%) and Fabales (3.69%). However, In group B, there were 9 orders had the relative abundance of more than 1% in the top 15 orders, which composed of Poales (64.68%), Caryophyllales (7.71%), Saxifragales (7.39%), Lamiales (3.75%), Fagales (3.18%), Rosales (2.58%), Asparagules (2.37%), Cucurbitales (2.35%) and Brassicales (1.73%) (Fig. 2a)(Table S1). The taxonomic composition for each group was then successfully outlined at the phylum, class, order, family, and genus level (Table S1). The mitochondria COI gene was used to sequence the animal-based composition of mandarin ducks. At the order level, there were 8 orders in group A that had relative abundance of more than 1% in the top 15 orders, which mainly composed of Coleoptera (42.0%), Anura(20.0%), Ploima(11.0%), Cypriniformes(9.0%), Sarcoptiformes(5.0%), Decapoda(1.0%), Rotaria(1.0%) and Diptera(1.0%). However, The group B had 4 more orders than group A, which composed Decapoda(19.0%), Diplostraca(19.0%), Anura(18.0%), Coleoptera(7.0%), Rotaria(4.0%)、Diptera (3.0%), Lepidoptera(3.0%), Mesostigmata(2.0%), Primates(2.0%), Philodinida(2.0%), Scleractinia(2.0%) and Zoantharia (2.0%) (Fig. 2b). The taxonomic composition for each group was then successfully outlined at the phylum, class, order, family, and genus level (Table S2).

Sequencing v3-v4 region of 16S rRNA, at the level of phylum, there were only 3 phylums in group A, that had relative abundance of more than 1% in the top 15 phylums, which mainly composed Firmicutes (58.04%), Proteobacteria (40.58%) and Actinobacteria (0.55%). However, In group B, there were 6 phylums had the relative abundance of more than 1% in the top 15 orders, which composed of Firmicutes (59.00%), Proteobacteria (23.15%) and Actinobacteria (10.55%), Cyanobacteria (2.54%), Chloroflexi (0.63%) and Verrucomicrobia (0.53%) (Fig. 2c). At the order level, there were 5 orders that were Bacillus (38.76%), Enterobacteriales (28.42%), Clostridiales (15.23%), Pseudomonasdales (10.64%) and Lactobacillus (2.93%), which had relative abundance of more than 1% in the top 15 orders. It had 4 orders less than the group B, which composed Bacillus (51.05%), Actinomyceteles (9.15%), Pseudomonadales (8.04%), Clostridiales (7.02%), Sphingomonadales (5.38%), Rhizobiales (3.05%), Streptophyta (1.88%), Enterobacteriales (1.77%) and Burkholderiales (1.43%) (Fig. 2d).

At the top 15 genera level of relative abundance of intestinal microbiota of mandarin ducks (Fig. 3), group A was mainly composed 10 genera that were Pseudomonadaceae Pseudomonas(10.4%), Cronobacter(9.2%), Bacillaceae Bacillus(9.1%), Exiguobacterium(3.4%), Planococcaceae Bacillus(2.8%), Clostridiaceae Clostridium (2.8%), Solibacillus (1.9%), Sporosarcina (1.2%), Ruminococcus (1.1%) and Lactococcus (1.1%). In group B, Exiguobacterium (24.6%), Sporosarcina (7.4%), Arthrobacter (6.1%), Pseudomonadaceae Pseudomonas (5.9%), Sphingomonas(5.0%), Bacillaceae Bacillus(4.0%), Paenisporosarcina(2.8%), Planococcaceae Bacillus(2.4%), Acinetobacter(1.6%), Clostridiaceae Clostridium (1.4%) and Solibacillus (1.1%) were the main components.

Alpha diversity and beta diversity analysis

The alpha diversity measures (Chao index, Shannon index and Simpson index) were calculated in each group of plant-based composition, animal-based composition and gut microbiotas to examine whether the mandarin ducks from the two groups had differences in alpha diversity (Table 3). The three index identified in group B were all higher than in group A .To evaluate the overall difference in the beta diversity, the heatmap analysis was used based on the relative abundance of the top 20 order. Figure 4a shows the heatmap of plant composition at the order level, group B had 14 orders, and group A had only 6 orders. At the heatmap of animal-based composition(Fig. 4b), the sequencing result was not correct, it also contained fungus and algae, group A had 8 orders, group B had 12 orders. There were 6 gut microbitas orders in group B more than in group A at the heapmap of gut microbitas (Fig. 4c).

Table 3

The comparison of Chao1 index, Shannon diversity index and Simpson diversity index of plant-based composition, animal-based composition and gut microbiotas across groups in fecal samples.
α-diversity index	Chao1index		Shannon index		Simpson index
Group	A	B	A	B	A	B
Plant-based composition	89.543	167.608	1.280	2.047	0.303	0.534
Anima-based composition	229.423	276.770	4.233	4.469	0.873	0.894
Gut microbiotas	1450.769	2369.060	5.706	6.635	0.867	0.906

Prediction of intestinal microbiota function in Mandarin Ducks

Using PICRUSt as a function prediction tool, the functions of bacteria in the whole intestine of mandarin ducks were predicted, including cellular processes, environmental information processing and genetic information processing. In this paper, we have studied the mechanism of metabolism, metabolism, biosynthesis, degradation, utilization and assembly, generation of precursor metabolite and energy, and so on. Among them, the relative abundance of classification function of biosynthesis and metabolism was the highest.

In the Biosynthesis functional groups, the secondary level classification function of intestinal bacteria in two groups of mandarin ducks mainly includes: Cofactor, Prosthetic Group, Electron Carrier, and Vitamin Biosynthesis (A: 36871.36, B: 36989.26), Amino Acid Biosynthesis (A: 34333.08, B: 33687.49), Nucleoside and Nucleotide Biosynthesis (A: 30747.56, B: 32945.66), Fatty Acid and Lipid Biosynthesis (A: 19776.19, B: 18799.73), Carbohydrate Biosynthesis (A: 8708.59, B: 9207.04), in which the abundance of Cofactor, Prosthetic Group, Electron Carrier, and Vitamin Biosynthesis, Nucleoside and Nucleotide Biosynthesis, Carbohydrate Biosynthesis in group B was higher than that in group A (Fig. 5a). In the Metabolism function groups, the secondary level classification function of intestinal bacteria in two groups of mandarin ducks mainly includes: Carbohydrate Metabolism (A: 4739.79, B: 5082.460, Amino Acid Metabolism (A: 4373.49, B: 4989.34), Metabolism of Cofactors and Vitamins (A: 4152.87, B: 4502.65), Lipid Metabolism (A: 2353.63, B: 2925.93), Metabolism of Terpenoids and Polyketides (A: 2405.62, B: 2741.96), Xenobiotics Biodegradation and Metabolism (A: 1855.68, B: 2462.6), and the abundances of the above six secondary level metabolic functions in group B were significantly higher than those in group A (Fig. 5b). Indicating that the intestinal flora function of mandarin duck in group B was mainly involved in the host intestinal metabolic activities.

In this study, high-throughput sequencing technology was used to study the intestinal microbiota structure and food composition of mandarin duck in Shiqian lake, The results showed that the main intestinal microbiota of mandarin duck were Firmicutes, Proteobacteria and Actinobacteria. This result was consistent with the intestinal microbiota of most water birds, such as Grus nigricollis (Wang et al. 2020), Grus japonensis (Xie et al. 2016), Wild goose (Wu et al. 2018; Wang et al. 2016a; Wang et al. 2016b) and goose (Gao et al. 2016) at the phylum level. However, there were significant differences in the structure of intestinal microbiota, and the function of intestinal microbiota between the two groups, which were closely related to the food composition of mandarin ducks. The analysis of food composition and diversity showed that at the order level of relative abundance ≥ 1, the type of plant-based foods and animal-based foods composition in group B was more than that in group A, and the number of gut microbiota and its diversity in group B was more than that in group A at phylum level and order level were also higher than that in group A. It showed that the richness of intestinal food composition of group B mandarin ducks was closely related to the easier and wider feeding range of point B mandarin ducks. At the same time, we found that the richer the intestinal food composition, the more complex the intestinal microbita in structure, the higher the intestinal microbial diversity.

Dietary differences were one of the most direct factors affecting the intestinal microbiota structure of mandarin ducks, and the type of food resources was an important driving factor for the diversity of intestinal microbiota of mandarin ducks. This conclusion agree with orther related study (Grond et al. 2017; Illiano et al. 2020; Michel et al 2018; Ley et al 2008) and also confirmed that the effect of food differences on intestinal microbiota were greater than that of host itself (Mikaelvan et al 2015). The diversity of host intestinal microbiota plays an important role in maintaining body balance and host health. The reduction of intestinal microbial diversity will reduce the ability of body to resist the invasion of pathogens (Fndriks, 2017). Among the intestinal microflora of mandarin duck in point A, Pseudomonadaceae_Pseudomonas and Cronobacter with the highest relative abundance were found to be potentially pathogenic microflora, with the total abundance of 19.6%, and the other intestinal symbiotic microflora with relative abundance ≥ 1 were 20.6%. In the intestinal flora of mandarin ducks at point B, the three genera of Exiguobacterium, Sporosarcina and Arthrobacter with the highest relative abundance were found to be intestinal probiotics, accounting for 38.1% of the total value, while the three potential pathogenic genera of Pseudomonadaceae_Pseudomonas, Sphingomonas and Acinetobacter accounted for 12.5%. So the more abundant the mandarin ducks eat, the greater the probability of increasing the probiotics in intestinal microbiota, and the more abundant the species and diversity of the microbiota, which can improve the capabilities of disease.

The study on the function of intestinal microbiota has found that intestinal microorganisms play an important role in nutrition absorption, immunity, toxin degradation and thermoregulation of host birds (Waite and Taylor, 2014). In group B, the abundance of amino acids, carbohydrates, cofactors, repair genes, electron carriers and vitamins, as well as fatty acids and fats in intestinal microbiota of mandarin ducks were higher than those in group A, this is because group B was rich of plant-based foods and animal-based foods, and had many bacteria with high abundance related to the decomposition of complex carbohydrates, polysaccharides, sugars and fatty acids have been found in the intestinal microbiota, such as Exiguobacterium, which can hydrolyze starch and ferment metabolism; Sporosarcina, which can decompose cellulose, starch and protein, and so on; Clostridiaceae_Clostridium, which can digest protein and carbohydrate, from degradation to metabolism of organic matter; Bacillaceae_Bacillus, which can decompose cellulose, protein and starch (Flint et al 2008; Tap et al 2009; Hooper et al 2001; Daquila et al 2019). There were much of Arthrobacter in group B, which can decompose xylose into short chain fatty acids. Therefore, it is suggested that mandarin duck in group B can provide sufficient energy for spawning and hatching at breeding period, and have more ability to maintain body balance and health.

Habitat can provide food resources for animals. The unique ecological environment makes animals evolve specific food selection behavior in the process of continuous feeding (Liu et al 2018). The abundance of food resources and the difficulty of feeding are important factors affecting the habitat selection and food composition of animals. The good ecological environment and food resources of Shiqian Mandarin Duck Lake National Wetland Park have added rich food sources to mandarin ducks. During the breeding period, Poales is the main plant-based foods of mandarin duck, accounting for 70.19% of the total relative abundance, which is consistent with the selection of plant-based foods for resident birds, including white cheeked babbler, sparrow, Bulbul, turtledove and blackbird (Zhao et al 2017b). In addition, the mandarin ducks also eat the main foods of Fagales, Caryophyllales and Saxifragales during the breeding period. Meanwhile, the main animal-based foods of mandarin duck is Arthropod. Therefore, In the breeding period of mandarin ducks, when the food resources are relatively scarce or the wild mandarin duck are injured, we can scientifically feed on Poales and Arthropod food that they obviously prefer and notice numerous in variety which will help them promote or restore their health.

The structure of intestinal microbiota of mandarin duck is mainly composed of Firmicutes, Proteobacteria and Actinobacteria. The plant-based foods of the species is mainly Poales, also include Fagales, Caryophyllales and Saxifragales and the animal-based foods is mainly Arthropod. Using high-throughput sequencing technology, the feeding species, intestinal microbiota composition and health status of mandarin ducks in two different habitats during the breeding period were analyzed in detail. It was found that the intestinal microbiota structure of mandarin ducks was greatly affected by the feeding species, while the influence of host itself could be ignored. The more complex the feeding, the more complex the intestinal microbiota structure, the higher the species diversity and the more stable the microbiota, the better the absorption and digestion of food, so which also have a healthier body. Therefore, we advocate a diverse diet when saving or raising wild animals.

ACKNOWLEDGMENTS

This work was supported by the Guizhou Provincial Science and Technology Foundation (Grant No. Qiankehe LH [2020] 1Y080), the Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou province (GrantNo. U1812401), the National Natural Science Foundation of China (Grant No. 31801990) and the College-county cooperation project (Grant No. 11904/0518168).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

Author Contributions

All authors participated the experimental design. Dan Zhu and Jinsong Wu collected the feces of Black-necked Crane. Binqiang Li and Haofeng Zhan performed the DNA extraction, PCR amplification, data collection, and analysis. Yeying Wang and Xiaoyan Lv wrote the original draft of the manuscript.Canshi Hu edited and reviewed the manusript. All authors have read and approved the fnal manuscript.

Ando H, Setsuko S, Horikoshi K, Suzuki H, Umehara S, Inoue-Murayama M, Isagi Y (2013) Diet analysis by next-generation sequencing indicates the frequent consumption of introduced plants by the critically endangered red-headed wood pigeon (columba janthina nitens) in oceanic island habitats. Ecology & Evolution. 3(12): 4057-4069. https://doi:10.1002/ece3.773
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Caporaso JG (2018) QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Prepr. 6: e27295v2. https://doi:10.7287/peerj.preprints.27295v2
Brown DS, Burger R, Cole N, Vencatasamy D, Clare EL, Montazam A, Symondson WOC(2013) Dietary competition between the alien asian musk shrew (suncus murinus) and a re-introduced population of telfair's skink (leiolopisma telfairii). Mol Ecol. https://doi:10.1111/mec.12445
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 13: 581-583. https://doi:10.1038/nmeth.3869
Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA (2012) The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res. 40(Database issue): D742. https://doi:10.1093/nar/gkr1014
Chase MW, Cowan RS, Hollingsworth PM, Berg CVD, Wilkinson MJ (2007) A proposal for a standardised protocol to barcode all land plants. Taxon.56: 295-299. https://doi:10.1002/tax.562004
Chase MW, Salamin N, Wilkinson M, Dunwell JM, Kesanakurthi RP, Haidar N, Savolainenl V (2005) Land plants and DNA barcodes: short-term and long-term goals. Philo Trans R Soc lond B Biol Sci. 360: 1889-1895. https://doi:10.1098/rstb.2005.1720
Daquila BV, Scudeler EL, Cleisto F, Dossi A, Dossi F (2019) Action of Bacillus thuringiensis (bacillales: bacillaceae) in the midgut of the sugarcane borer Diatraea saccharalis (fabricius, 1794) (lepidoptera: crambidae). Ecotoxicol Environ Saf. 184: 109642. https://doi:10.1016/j.ecoenv.2019.109642
Deagle BE, Kirkwood R, Jarman SN (2009) Analysis of Australian fur seal diet by pyrosequencing prey DNA in feces. Mol Ecol. 18: 2022-2038. https://doi:10.1111/j.1365-294X.2009.04158.x
Delsuc F, Metcalf JL, Wegener, Parfrey L, Song SJ, González A, Knight R (2014) Convergence of gut microbiomes in myrmecophagous mammals. Mol Ecol. 23: 1301-17. https://doi:10.1111/mec.12501
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 72: 5069-5072. https://doi:10.1128/AEM.03006-05
Elliott TL, Jonathan, Davies T (2014) Challenges to barcoding an entire flora. Mol Ecol Resour. 14: 883-891. https://doi:10.1111/1755-0998.12277
Erickson DL, Elizabeth R, Padmini R, Bourg NA, Mcshea WJ, Andrea O (2017) Reconstructing a herbivore's diet using a novel rbcl dna mini-barcode for plants. AoB PLANTS. (3): 3. https://doi:10.1093/aobpla/plx015
Erwan Quéméré, Hibert F, Miquel C, Lhuillier E, Rasolondraibe E, Champeau J, Rabarivola C, Nusbaumer L, Chatelain C, Gautier L (2013) A dna metabarcoding study of a primate dietary diversity and plasticity across its entire fragmented range. PLoS ONE. 8(3): e58971-. https://doi:10.1371/journal.pone.0058971
Fang ZY (2019) Population Dynamics and Overwintering Habitat Selection of Mandarin Duck(Aix galericulata) in Shiqian Mandarin Duck Lake, Guizhou Province. Guizhou: Guizhou University. https://doi:10.11934/j.issn.1673-4831.2016.03.021
Fang ZY, Wang YY, Ran JR, Xu GH, Wang C, Deng BL, Li H (2019) Intestinal Bacterial Composition and Characteristics of Wintering Mandarin Duck (Aix Galericulata) in Shiqian Mandarin Duck Lake National Wetland Park, Guizhou Province. Int J Zoo Animal Biol. 2(5): 000158. https://doi:10.23880/IZAB-16000158
Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA (2008) Polysaccharide utilization by gut bacteria: Potential for new insights from genomic analysis. Nature Reviews Microbiology. 6(2): 121-131. https://doi:10.2307/3432760
Fndriks L (2017) Roles of the gut in the metabolic syndrome: An overview. Journal of International Medicine. 281: 319-336 https://doi:10.1111/joim.12584
Ford MJ, Jennifer H, Bradley HM, Ayres KL, Baird RW, Emmons CK, Lundin JI, Schorr GS, Wasser SK, Park LK (2016) Estimation of a killer whale (orcinus orca) population's diet using sequencing analysis of dna from feces. Plos One. 11(1): e0144956. https://doi:10.1371/journal.pone.0144956
Fu R, Xiang X, Dong Y, Cheng L, Zhou L (2020) Comparing the intestinal bacterial communies of sympatric wintering hooded crane (grus monacha) and domestic goose (anser anser domesticus). Avian Research. 11(1). https://doi:10.1186/s40657-020-00195-9
Galan M, Pons JB, Tournayre O, Pierre É, Leuchtmann M, Pontier D, Charbonnel N (2017) Metabarcoding for the parallel identification of several hundred predators and their preys: application to bat species diet analysis. Mol Ecol Resour. https://doi:10.1111/1755-0998.12749
Gao GL, Zhao XZ, Li Q, He C, Zhao WJ, Liu SY, Ding JM, Ye WX, Wang J, Chen Y, Wang HW, Li J, Luo Y, Su J, Huang Y, Liu ZH, Dai RH, Shi YX, Meng H, Wang QG (2016) Genome and metagenome analyses reveal adaptive evolution of the host and interaction with the gut microbiota in the goose.Sci Rep. 6: 32961. https://doi:10.1038/srep32961
Garcıa-Robledo C, Erickson DL, Staines CL, Erwin TL, Kress WJ, Heil M (2013) Tropical plant-herbivore networks: reconstructing species interactions using DNA barcodes. PLoS One. 8: e52967. https://doi:10.1371/journal.pone.0052967
Geller J, Meyer C, Parker M, Hawk H (2013) Redesign of pcr primers for mitochondrial cytochrome c oxidase subunit i for marine invertebrates and application in all-taxa biotic surveys. Mol Ecol Resour. 13(5): 851-861. https://doi:10.1111/1755-0998.12138
Gong Y, Nehafta BB, Wang HH (2018) Nest Usurpation between Mandarin Duck Aix galericulata and Coexisting Bird Species in Nest Boxes in a Secondary Forest, Zuojia Nature Reserve, China. Pakistan J. Zool. 50: 1537-1540. https://doi:10.17582/journal.pjz/2018.50.4.sc2
Grond K, Lanctot RB, Jumpponen A, Sandercock BK (2017) Recruitment and establishment of the gut microbiome in arctic shorebirds. FEMS Microbiol Ecol. 93: 142. https://doi:10.1093/femsec/fix142
Grond K, Sandercock BK, Jumpponen A, Zeglin LH (2018) The avian gut microbiota: community, physiology and function in wild birds. J Avian Biol. 49: e01788. https://doi:10.1111/jav.01788
Harwood SKSD (2005) Advances in molecular ecology:tracking trophic links through predator-prey food-webs. Funct Ecol. 19 (5): 751-762. https://doi:10.1111/j.1365-2435.2005.01041.x
He FQ, Lin JS, Wang YY, Wang GF, Hong YH, Zheng PJ, Wen C, Lin Z, Shi QH (2014) Bird Records from Wuyuan, NE Jiangxi of SE China. Chinese Journal of Zoology. 49(02): 170-184.
Hofreiter M, Poinar HN, Spaulding WG, Bauer K, Pbo S (2010) A molecular analysis of ground sloth diet through the last glaciation. Mol Ecol. 9(12): 1975-1984. https://doi:10.1046/j.1365-294X.2000.01106.x
Hollingsworth PM, Forrest LL, Spouge JL, Hajibabaei M, Little DP (2009) A DNA barcode for land plants. Proc Natl Acad Sci U S A. 106: 12794-12797. https://doi:10.1073/pnas.0905845106
Hooper LV, Gordon J (2001) Molecular Analysis of Commensal Host-Microbial Relationships in the Intestine. Science. 291(5505): 881-884. https://doi:10.1126/science.291.5505.881
Illiano P, Brambilla R, Parolini C (2020) The mutual interplay of gut microbiota, diet and human disease. Febs Journal. 23: 1-23. https://doi:10.1111/febs.15217
Kasper LH (2014) The evolving role of the gut microbiome in human disease. FEBS Lett. 588: 4101. https://doi:10.1016/j.febslet.2014.09.015
Kwon JH, Noh YK, Lee DH, Yuk SS, Erdene-Ochir TO, Noh JY, Hong WT, Jeong JH, Jeong S, Gwon GB (2017) Experimental infection with highly pathogenic h5n8 avian influenza viruses in the mandarin duck (Aix galericulata) and domestic pigeon (columba livia domestica). Vet Microbiol. 203: 95-102. https://doi:10.1016/j.vetmic.2017.03.003
Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Dan K, Reyes JA, Clemente JC, Burkepile DE, Thurber RLV, Knight R (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 31: 814-+. https://doi:10.1038/nbt.2676
Lee CK, Barbier BA, Bottos EM, Mcdonald IR, Cary SC (2012) The Inter-Valley soil comparative survey: the ecology of Dry Valley edaphic microbial communities. ISME J. 6(5): 1046-1057. https://doi:10.1038/ismej.2011.170
Lee O, Lee S, Nam DH, & Lee HY (2014) Food habits of the leopard cat ( prionailurus bengalensis euptilurus ) in korea. Mammal Study. 39(1): 43-46. https://doi:10.3106/041.039.0107
Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, Boehm JT, Machida RJ(2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 10(1): 1-14. https://doi:10.1186/1742-9994-10-34
Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R (2008) Evolution of mammals and their gut microbes. Science. 320: 1647-1651. https://doi:10.1126/science.1155725
Liu G, Ning Y, Xia XF, Gong MH (2018) Application of high-throughput sequencing technology in the analysis of wild animal feeding habits. Acta Zoologica Sinica. 38(09): 3347-3356. https://doi:10.1360/052011-634
Michel AJ, Ward LM, Goffredi SK, Dawson KS, Baldassarre DT, Brenner A, Gotanda KM, McCormack JE, Mullin SW, Neill AO’, Tender GS, Uy JAC, Yu K, Orphan VJ, Chaves JA (2018) The gut of the finch: uniqueness of the gut microbiome of the galápagos vampire finch. Microbiome. 6(1): 167. https://doi:10.1186/s40168-018-0555-8
Mikaelvan A, Dietrich C, Kohler T, Poulsen M, Sillam-Dussès D, Brune A (2015) Diet is the primary determinant of bacterial community structure in the guts of higher termites. Mol Ecol. 24: 5284-5295. https://doi:10.1111/mec.13376
Minoru K, Susumu G, Yoko S, Miho F, Mao T (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40: D109-D114. https://doi:10.1093/nar/gkr988
Muegge B, Kuczynski J, Knights D (2011) Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 332: 970-4. https://doi:10.1126/science.1198719
Perofsky AC, Lewis RJ, Meyers LA (2019) Terrestriality and bacterial transfer: a comparative study of gut microbiomes in sympatric Malagasy mammals. ISME J, 13, 50-63. https://doi:10.1038/s41396-018-0251-5
Pompanon F, Deagle BE, Symondson WO, Brown DS, Jarman SN, Taberlet P (2012) Who is eating what: diet assessment using next generation sequencing. Mol Ecol. 21: 1931-1950. https://doi:10.1111/j.1365-294X.2011.05403.x
Rognes T, Flouri T, Nichols B, Quince C, Mahé F (2016) Vsearch: a versatile open source tool for metagenomics. Peerj. 4(10). https://doi:10.7717/peerj.2584
Sanders JG, Beichman AC, Roman J, Scott JJ, Emerson D, McCarthy JJ, Girguis PR (2015) Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat Commun. 6: 8285. https://doi:10.1038/ncomms9285
Semova I, Carten JD, Stombaugh J, Mackey LC, Knight R, Farber SA, Rawls JF (2012) Microbiota regulate intestinal absorption and metabolism of fatty acids in the Zebrafish. Cell Host & Microbe. 12(3): 277-288. https://doi:10.1016/j.chom.2012.08.003
Shaw J, Lickey EB, Schilling EE, Edward E, Schilling, Randall L, Small (2007) Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: the tortoise and the hare III. Am J Bot. 94: 275-288. https://doi:10.3732/ajb.94.3.275
Shibazaki F (2006) Japanese Wood Pigeons Columba janthina nitens as a seed disperser in the Ogasawara Islands, southern Japan. Strix. 24: 171-176.
Spor A, Koren O, Ley R (2011) Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol. 9(4): 279-290. https://doi:10.1038/nrmicro2540
Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E (2012) Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol, 21(8): 2045-2050. https://doi:10.1038/nrmicro2540
Tap J, Mondot S, Levenez F, Pelletier E, Caron C, Furet JP, Leclerc M (2009) Towards the human intestinal microbiota phylogenetic core. Environ Microbiol. 11(10): 2574-2584. https://doi:10.1111/j.1462-2920.2009.01982.x
Titulaer M, Melgoza-Castillo A, Panjabi AO, Sanchez-Flores A, Fernández JA (2017) Molecular analysis of stomach contents reveals important grass seeds in the winter diet of baird's and grasshopper sparrows, two declining grassland bird species. Plos One. 12(12): e0189695. https://doi:10.1371/journal.pone.0189695
Waite DW, Taylor MW (2014) Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front Microbiol. 5: 223. https://doi:10.3389/fmicb.2014.00223
Waite DW, Taylor MW (2015) Exploring the avian gut microbiota: current trends and future directions. Front Microbiol. 6: 673. https://doi:10.3389/fmicb.2015.00673
Wang W, Cao J, Yang F, Wang XL, Zheng SS, Sharshov K, Li LX (2016a) High- throughput sequencing reveals the core gut microbiome of Bar-headed goose (Anser indicus) in different wintering areas in Tibet. MicrobiologyOpen.5(2): 287-295. https://doi:10.1002/mbo3.327
Wang W, Wang F, Li L, Wang A, Shi Y (2020) Characterization of the gut microbiome of black-necked cranes (Grus nigricollis) in six wintering areas in China. Arch Microbiol. 202(5). 983-993. https://doi:10.1007/s00203-019-01802-0
Wang W, Zheng S, Sharshov K, Cao J, Sun H, Yang F, Li L (2016b) Distinctive gut microbial community structure in both the wild and farmed Swan goose (Anser cygnoides). J Basic Microbiol. 56(11): 1299-1307. https://doi:10.1002/jobm.201600155
Wu Y, Yang Y, Cao L, Yin H, Xu M, Wang Z, Liu Y, Wang X, Deng Y (2018) Habitat environments impacted the gut microbiome of long-distance migratory swan geese but central species conserved. Sci Rep. 8(1): 13314 https://doi:10.1038/s41598-018-31731-9
Xie Y, Xia P, Wang H, Yu H, Giesy JP, Zhang Y, Mora MA, Zhang X (2016) Efects of captivity and artifcial breeding on microbiota in feces of the red-crowned crane (Grus japonensis). Sci Rep. 6: 33350. https://doi:10.1038/srep33350
Xiong M, Shao X, Long Y, Bu H, Zhang D, Wang D, Li S, Wang R, Yao M (2016) Molecular analysis of vertebrates and plants in scats of leopard cats (prionailurus bengalensis) in southwest china. J Mammal. (4): gyw061. https://doi:10.1093/jmammal/gyw061
Yang Y, Deng Y, Cao L (2016) Characterising the interspecific variations and convergence of gut microbiota in Anseriformes herbivores at wintering areas. Rep. 6: 32655. https://doi:10.1038/srep32655
Zhao G, Zhou L, Dong Y, Cheng Y, & Song Y (2017a) The gut microbiome of hooded cranes (Grus monacha) wintering at Shengjin Lake, China. Microbiologyopen. https://doi:10.1002/mbo3.447
Zhao YL, Chen MY, Miao N,Timothy (2017b) A Study on Seasonal Variation Diet of Five Most Common Resident Bird Species in Wangjiang Campus of Sichuan University, Chengdu. Sichuan Journal of Zoology. 36(05): 576-581. https://doi:10.11984/j.issn.1000-7083.20160218
Zheng GM (2017) A Checklist on the Classification and Distribution of the Birds of China(Third Edition). Beijing: Science Press. 26.
Zhi YJ, Shao MQ, Cui P, Chen B (2019) Time Budget and Activity Rhythm of the Mandarin Duck Aix galericulata in the Poyang Lake Watershed. Pakistan J. Zool. 51: 725-730. https://doi:10.17582/journal.pjz/2019.51.2.725.730
Zhu X, Tian XG, Lin JD, Liu YG, Wang LC, Chen JH, Liu RY, He Y, Xu ZJ, Zhou SW, Tong ZM, Teng JK, Li L (2010) Resources and Habitat of Mandarin Duck in Shiqian County of Guizhou Province. Guizhou Agricultural Sciences. 38(11): 189-190+193. https://doi:10.3969/j.issn.1001-3601.2010.11.059

TableS1.docx
Table S1: Statistical table of detailed information of plant-based composition at each taxonomic level under order level.
TableS2.docx
Table S2: Statistical table of detailed information of animal-based composition at each taxonomic level under order level.

PDF

Reviews received at journal
10 Mar, 2022
Reviewers invited by journal
07 Mar, 2022
Editor assigned by journal
04 Mar, 2022
First submitted to journal
02 Mar, 2022

You are reading this latest preprint version

The m assive d ifferences in d iet i nduced an a ltered g ut m icrobiota s tructure in m andarin d uck ( Aix g alericulata ) at b reeding a rea

Status:

Version 1

Abstract

Figures

Highlights

Introduction

Materials And Methods

Ethics and sampling

Total DNA extraction, PCR amplification and sequencing

Data analysis

Result

Sequencing data analysis

Species composition analysis at different classification levels in fecal samples

Alpha diversity and beta diversity analysis

Prediction of intestinal microbiota function in Mandarin Ducks

Discussion

Conclusion

Declarations

ACKNOWLEDGMENTS

CONFLICT OF INTEREST

Author Contributions

References

Supplementary Files

Status:

Version 1