Diversity of Intestinal Microbiota in Coilia ectenes from Lake Taihu, China

Jinrong Duan; Dongpo Xu; Kai Liu; Yanfeng Zhou; Pao Xu

doi:10.1515/biol-2017-0037

Article Open Access

Diversity of Intestinal Microbiota in Coilia ectenes from Lake Taihu, China

Jinrong Duan , Dongpo Xu , Kai Liu , Yanfeng Zhou and Pao Xu

Published/Copyright: October 23, 2017

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Open Life Sciences Volume 12 Issue 1

Abstract

To investigate the community structure and species composition of intestinal microbiota in Coilia ectenes, sixty-seven samples were collected from Lake Taihu in China. The intestinal microbiota of the C. ectenes were identified by the V4 of the 16S ribosomal RNA gene using high-throughput sequencing. Furthermore, the water quality of different sampling sites was also evaluated. A total of 53 phyla and 730 classified genera were found in all the samples. The eight dominant phyla Planctomycetes, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, Crenarchaeota, Actinobacteria, and Verrucomicrobia were included. The intestinal microbiota compositions of the C. ectenes obtained from the same location presented more similar profiles, and the intestinal microbiota compositions of the C. ectenes from different geographical locations showed considerable differences. The operational taxonomic units (OTUs) abundance of the intestinal microbiota was significantly correlated with both the concentrations of total nitrogen and chlorophyll-a of the aquatic environment (p<0.05). Geographical location was an important determinant for the fish’s intestinal microbiota composition. The intestinal microbiota of C. ectenes would be affected by the concentrations of total nitrogen and chlorophyll-a in the water. These findings achieve a basic understanding of fish’s gut microbiota, and are helpful for the protection of fish resources in Lake Taihu and provided the cornerstone to sustainable utlization of C. ectenes.

Keywords: Coilia ectenes; high-throughput sequencing; intestinal microbiota; water quality

1 Introduction

The intestinal microbiota plays a key role in the nutrition and health of host. The establishment of a normal intestinal microbiota may be considered complementary to the establishment of digestive enzymes, and under normal conditions, the microorganisms serve as barrier against invading pathogens [1].

The composition of intestinal microbiota of the fish is highly variable and depends mostly on the species and the developmental stage of the fish [2]. A more prevailing viewpoint proposes that the diet is the most crucial factor determining the composition of the intestinal microbiota [1, 3-6]. Except for the self-factor of fish, the external factor may have an impact for diversity of intestinal microbiota of fish. Previous studies have demonstrated that the geographic difference may have an effect on diversity of microbiome in intestine of fish [7, 8]. However, this geographic effect was confounded by environmental differences [9]. For fish, water quality is an important parameter for living in the environment[10, 11]. Therefore, water quality may be an important factor in determing the differences seen across geographic regions.

Coilia ectenes is a small- to medium-sized fish classified under Family Engraulidae and Order Clupeiformes [12]. C. ectenes is widely distributed in near-ocean waters, freshwater rivers, and lakes, including Lake Taihu, which is an important habitat for C. ectenes in China [13]. C. ectenes is also an important model species that can be used to study the protection of fish resources [14]. However, Mao et al. [15] reported an obvious trend of population shrinkage of C. ectenes due to overfishing and water pollution.

Sequence analysis of the 16S ribosomal RNA (rRNA) gene has been used extensively in the study of the community structure and species composition of the intestinal microbiota of humans [16, 17], terrestrial animals [18], and aquatic animals [18, 19]. However, such sequence analysis has been rarely used in exploring the intestinal microbiota of C. ectenes.

Up until now, the intestinal microbiota of C. ectenes remains poorly understood. Moreover, little is known about the difference of intestinal microbiota of C. ectenes, which may be affected by external factor including geographic difference and water quality. Therefore, this study is aimed to investigate the community structure and species composition of intestinal microbiota of C. ectenes, compare the differences among the intestinal microbiota of C. ectene from different geographical locations, and explore the factors that influence the intestinal microbiota of C. ectene.

2 Materials and Methods

2.1 Sampling

C. ectenes samples were collected from China’s Lake Taihu in 2015 (Figure 1). Sixty-seven fish were collected form Gongshan (GS), Jiaoshan (JS) and Xishan (XS) of Lake Taihu (Figure 2), and their total lengths and body heights were measured. The samples were aseptically collected in sterilized plastic bags and kept cold at 4°C with ice bags during transport to the laboratory.

Figure 1

The photograph of C. ectenes

Figure 2

The sampling sites in Lake Taihu

Eleven gut samples collected from the GS of Lake Taihu were marked as group A. The group of thirteen gut samples collected from JS1 of Lake Taihu were marked as group B. Fifteen and thirteen samples from JS2, JS3 of Lake Taihu were marked as group C and group D. Meanwhile, fifteen samples collected from the XS of Lake Taihu were marked as group Tx01. The total length, body height, and location of sample point were summarized in Table 1.

Table 1

Summary of total length, body height and sampling point

Group	Total length (mm)	Body height (mm)	Sample point	Number
A	123.47±2.09	20.95±0.81	Gongshan, Taihu(GS)	11
B	125.18±3.35	22.05±1.76	Jiaoshan, Taihu (JS1)	13
C	112.84±1.70	20.92±0.49	Jiaoshan, Taihu (JS2)	15
D	115.29±2.97	19.53±1.41	Jiaoshan, Taihu (JS3)	13
Tx01	112.60±5.46	18.87±0.85	Xishan, Taihu (XS)	15

Ethical approval

The research related to animals use has been complied with all the relevant national regulations and institutional policies for the care and use of animals.

2.2 DNA extraction and sequencing

The entire guts were squeezed and rinsed by asepsis injector with sterile phosphate buffered saline to remove intestinal contents. The total genomic DNA of intestinal content was extracted using the PowerFecal® DNA Isolation Kit (50) (MoBio Laboratories, Inc., Carlsbad, CA) in accordance with the manufacturer’s manual. The quality of DNA extracted was assessed by agarose gel electrophoresis with 0.7% w/v agarose in Tris– borate–EDTA buffer. The high-throughput sequencing of V4 of 16S rRNA was completed by the Illumina Miseq platform.

2.3 Analysis of water quality

The water quality of the sampling sites for C. ectenes was also tested. These parameters included total nitrogen (TN), dissolved total nitrogen (DTN), ammonium nitrogen (NH4+−N), nitrite nitrogen (NO2-−N), total phosphorus (TP), dissolved total phosphorus (DTP), phosphate (PO43-), chemical oxygen demand (COD), chlorophyll-a (Chl-a), temperature (T), and pH, respectively.

2.4 Bioinformatics and statistical analysis

High-quality sequence alignments were performed using mothur open sourced software to remove error sequences [20] and Qiime to remove chimera sequences [21]. After the high-quality sequence was clustered at 97% similarity threshold using Qiime [21], operational taxonomic units (OTUs) were obtained and noted using the same software [22]. A Venn diagram was constructed using mothur open sourced software. The microbial composition of each group was obtained at different taxonomic taxa in terms of OTUs using Qiime. A cladogram of microbial communities was created using MetaPhlAn [23] on the basis of microbiota abundance. Heatmap of 50 classified dominant genera was performed using R. The statistical significance and correlation analysis were analyzed using R. The function prediction of microbiota was performed by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) [24] based on the abundance of OTUs, and aligned with Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

3 Results

3.1 Microbial richness and biodiversity

A total of 654,595 high-quality sequences were obtained from 671,659 usable raw sequences after NAST alignment. Eventually, 2,803 OTUs were obtained at 97% similarity level from all samples. Group A had the highest number of OTUs at 1,807, followed by group C (n=1470), whereas group Tx01 achieved a lower number of OTUs at 1,144 (Table 2). The mutual and unique OTUs are shown in Venn diagrams for each group (Figure 3). Notably, 313 OTUs were shared by all groups. Group A contained the highest number of unique OTUs at 673, followed by group D (n=356), group C (n=277), group B (n=265) and group Tx01 (n=223).

Table 2

The number of OTUs at different annotated taxonomic level

Sample	Phylum	Class	Order	Family	Genus	Species	Unclassified
A	1,807	1,797	1,701	1,336	670	117	3
B	1,363	1,354	1,253	1,011	470	78	5
C	1,256	1,248	1,182	944	466	76	4
D	1,470	1,457	1,350	1,080	490	85	7
Tx01a	1,144	1,135	1,080	890	433	80	0

Note: The numbers indicated the OTUs that were classified to specific taxonomy level.

Figure 3

Venn diagram of the five groups showing the quantity variance of OTUs

3.2 Community structure and species composition

3.2.1 Community structures of species

A total of 53 phyla and 730 classified genera were identified from all samples. At phyla level, 43 phyla were included in group A. 39 and 37 phyla were observed in group D and C, and only 35 phyla in group B and C. At genus level, 242 genera were found in group A, followed by group D, B, Tx01 and C, respectively (Table 3).

Table 3

The number of classified species at different taxonomic taxa

Group	Phylum	Class	Order	Family	Genus
A	43	96	151	191	242
B	35	96	142	181	207
C	35	76	137	172	192
D	37	98	149	199	214
Tx01	39	81	126	176	196
Total	53	151	290	466	730

The dominant abundant microorganism (>1% relative abundance) were members of Planctomycetes, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, Crenarchaeota, Actinobacteria, and Verrucomicrobia (Figure 4).

Figure 4

Cladogram of the microbial communities of the intestinal microbiota in C. ectenes . The nodes indicate the abundance of the microorganism. The segments with different colors showed the most abundant phyla and the corresponding dominant branches. Different colors denote different taxonomic levels.

3.2.2 Microbial community compositions

The eight phyla that dominated more than 1% of the abundance in at least one group were Planctomycetes, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, Crenarchaeota, Actinobacteria, and Verrucomicrobia, respectively (Figure 5). The intestinal microbiota compositions showed significant differences between sampling sites. Most notably, Planctomycetes was the most abundant phylum in all samples accounting for 39.5%-76.5%%, which has less abundance in group A (39.5%) than other group. Firmicutes (4.2%-14.7%) and Proteobacteria (4.3%-13.6%) were also dominant in all groups. Firmicutes has the most abundance in group A with 14.7% and a least abundance in group Tx01 with 4.2%. Proteobacteria has a most abundance in group A with 13.6% and a least abundance in group B with 4.3%. Bacteroidetes is far more abundant in group A (18.5%) than in those of the other groups (2.9%–4.8%). Similarly, Cyanobacteria are dominant in group Tx01 accounting for 17.4% and Crenarchaeota accounted for 5.9% in group B, respectively, but are less abundant in the other groups (1.1%–3.7% and 0.2%–2.7%, respectively). Acidobacteria accounted for 0.2% in group C and Tx01, and 1.0% in group D. Actinobacteria accounted for 0.8% in group C and 3.5% in group A. Verrucomicrobia accounted for 1.4% in group A and B, 2.0% in Tx01.

Figure 5

Microbial community compositions at phylum level

Forty-two dominant genera above 0.5% of the abundance is shown in Figure 6. Obviously, an unclassified genus of Pirellulaceae is the most dominant in all the groups, accounting for 28.5%–49.0%. Except for group Tx01, an unclassified genus of Bacteroidales is the second most abundant in groups A, B, C, and D, accounting for 10.1%, 19.9%, 28.8%, and 17.1%, respectively. An unclassified genus of Gemmataceae is the second most abundant genus, accounting for 16.9% in group Tx01 but accounting for 0.90%–2.6% in other groups. Five classified genera, including Bacteroides (0.40%-1.50%), Faecalibacterium (0.50%-4.90%), Halomonas (0.60%- 3.50%), Mycobacterium (0.30%-5.10%) and Oscillospira (0.60%-3.50%), are abundant in C. ectenes. Meanwhile, the intestinal microbiota compositions showed significant differences between groups. Halomonas (0.60%-3.50%), Oscillospira (0.60%-3.50%), Faecalibacterium (0.50%-4.90%), Rhodococcus (0.20%-1.20%), Desulfuromonas (0.00%-1.40%), and three unclassified genera from Helicobacteraceae, Bacteroidales, and Actinomycetales are far more abundant in group A than in the other groups. Mycobacterium is more abundant in groups B, C, and D than in the other groups.

Figure 6

Microbial community compositions at genus level. The C indicates an unclassified genus of a specific class, O indicates an unclassified genus of a specific order, F indicates an unclassified genus of a specific family, and G indicated a classified genus.

3.3 Clustering analysis of abundant classified genera

Heatmap analysis of the 50 relatively most abundant classified genera within all groups is determined (Figure 7), which revealed the five most abundant classified genera including Bacillus, Lactococcus, Sphingomonas, Halomonas, and Ochrobactrum, with higher average relative abundance above 0.5% that are dominant in at least three samples. Interestingly, 35 classified genera of all with higher average relative abundances exceeded 0.5% in group A. Cluster analysis clearly exhibited that the compositions of groups B and C and those of groups D and Tx01are similar. Meanwhile, these groups are significantly different from group A.

Figure 7

Heatmap analysis of the 50 relatively most abundant classified genera

3.4 The prediction of function of the microbiome

PICRUSt analysis showed that the environmental information processing, genetic information processing and metabolism are the crucial KEGG pathways in the microbiome community (Figure 8). The results also show that amino acid metabolism and carbohydrate metabolism of metabolism pathway, the membrane transport of environmental information processing pathway, the replication and repair of environmental information processing pathway and energy metabolism of metabolism pathway possessed the main functions of genes.

Figure 8

The function prediction of microbiom based on KEGG

3.5 Analysis of water quality

The parameter of water quality at sampling sites is shown in Table 4. The difference between sampling sites was analyzed. A significant difference was found in the concentration of DTP between GS and JS (p<0.05, p=0.035). Significant differences in the concentration of both NH4+−N (p=0.042) and NO2-−N (p=0.048) between GS and JS were also found (p<0.05). In addition, a significant difference in concentration of both NH4+−N (p=0.026) and NO2-−N (p=0.013) was found between JS and XS (p<0.05). The relationship between OTU abundance and water properties index was analyzed. The OTU abundance also showed significant positive correlation with both the concentration of TN (p =0.028) and Chl-a (p =0.013) (p <0.05).

Table 4

Water quality indices

Sampling Site	TN	DTN	NH4+−N	NO2-−N	TP	DTP	PO43-	COD	Chl-α	T	pH

	g/m³	g/m³	g/m³	g/m³	g/m³	g/m³	g/m³	g/m³	mg/m³	°C
GS	1.73	0.93	0.70	0.35	0.10	0.04	0.01	2.19	16.59	20.93	8.61
JS1	2.58	1.58	0.45	0.44	0.08	0.03	0.00	2.75	17.73	20.80	8.84
JS2	1.23	0.43	0.43	0.44	0.11	0.05	0.03	2.90	21.53	20.70	8.52
JS3	1.84	1.04	0.23	0.39	0.08	0.04	0.00	2.20	12.99	21.20	8.15
XS	2.25	1.62	0.80	0.28	0.12	0.07	0.04	2.38	19.56	20.87	8.33

4 Discussion

Although a slight difference was present among all the groups, Planctomycetes, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, and Crenarchaeota remained as the key components of the intestinal microbiota in C. ectenes. In our study, Planctomycetes was the most abundant phylum in all of the groups investigated, which is a typical aquatic bacteria isolated from diverse aquatic habitats such as freshwater lakes, brackish water and sea water, as well as from a hot spring [25]. Previous studies have shown a compositional diversity of the intestinal microbiota of different fish species. Proteobacteria was the most predominant, followed by Firmicutes, in the intestinal microbiota of gibel carp [4]. Proteobacteria was the predominant phylum in rainbow trout [26]. Meanwhile, Proteobacteria, Firmicutes, and Actinobacteria were predominant in grass carp [27]. Cyanobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes were detected as the predominant phyla in invasive Asian silver carp and gizzard shad [8]. Furthermore, the guts of channel catfish, largemouth bass, and bluegill were dominated by Fusobacteria, followed by Proteobacteria [28]. Obviously, although these studies distinctly showed small differences in intestinal microbiota among fish species, Proteobacteria, Firmicutes, Actinobacteria, Cyanobacteria, and Bacteroidetes remained as the dominant phyla in most of the fish guts. More importantly, Planctomycetes had a higher abundance in intestine of C. ectenes than other fishes. Consequently, Planctomycetes may be a special phylum in intestine of C. ectenes.

On the other hand, there were obvious differences in different fishes at genera level. The results showed that Bacteroides, Faecalibacterium, Halomonas, Mycobacterium and Oscillospira were abundant genera in intestine of C. ectenes. The diet containing high levels of protein and fat strongly influenced the abundance of Bacteroides in gut [17, 29]. Faecalibacterium was created as a new genus, while previous Fusobacterium prausnitzii was renamed Faecalibacterium prausnitzii [30]. Previous studies showed that the reduction of F. prausnitzii was associated with a higher risk of Crohn disease [31, 32] and F. prausnitzii may be a potential probiotic in Crohn disease treatment [32]. Halomonas genus was halotolerant or halophilic, Gram negative, aerobic and rod-shaped bacteria, which was usually found in gut of animals [33]. Mycobacterium may be a potential pathogen and has been detected in zebrafish [34, 35]. Oscillospira is an enigmatic bacterial genus that has never been cultured and was detected in human gut [36]. Oscillospira is butyrate producers, and at least some of them have the ability to utilize glucuronate, a common animal-derived sugar that is both produced by the human host and consumed by that host in diets rich in animal products [37]. Consequently, the members of these genera may play a key role in intestine of C. ectenes in terms of nutrient metabolism and disease prevention. The results also demonstrated that the intestine of C. ectenes is a complex ecosystem, which constructed a close relationship between the host and different resident microorganisms. Although the phyla in intestine of fish were similar among different fishes, but genera showed a great deal of the difference. In grass carp, Anoxybacillus, Leuconostoc, Clostridium, Actinomyces, and Citrobacter were the main genera [27]. In gibel carp, Veilonella, Streptococcus, Lactobacillus and Rothia were important genera [4]. In rainbow trout, four genera including Acinetobacter, Cetobacterium, Pseudomonas, and Psychrobacter were predominant [26]. The results further confirmed that the composition of intestinal microbiota of the fish depended mostly on the species of the fish [2].

Cyanobacteria play a vital role in aquatic food webs [38], the members of which are important foods for certain fish [39, 40]. In this study, Cyanobacteria was dominant in the gut of C. ectenes. The high abundance of Cyanobacteria suggested its potential role as a major food source for C. ectenes, although the fish mainly feeds on cladocerans, copepods, naupliis, and caridinas [41]. Similarly, a previous study also detected that Cyanobacteria in invasive Asian silver carp and gizzard shad serves as food source [8].

In our study, the microbiota composition of group B, C and D that were obtained from JS showed more similar profiles at phylum level. Similarly, analysis of microbiota composition at genus level also showed that the microbiota composition of group B, C and D that were obtained from JS showed more similarity. Moreover, the microbiota composition of Group A showed more significant differences that other groups at phylum level. Planctomycetes was most abundant phylum in all sampling sites, but abundance of Planctomycetes from Gongshan was significantly lower than that of other sampling sites. Bacteroidetes and Firmicutes were significantly higher in group A from Gongshan. Cyanobacteria was significantly abundant in group Tx01 from XS. A previous study has shown that although living in the same environment, fish intestines contained distinct bacterial populations [3]. Analysis of microbiota composition at genus level showed that microbiota composition of group A was more abundant than that of other groups and the microbiota composition of group Tx01 was lower than that of other groups. The results showed the intestinal microbiota compositions of the C. ectenes obtained from the same location presented more similar profiles, and the intestinal microbial compositions of C. ectenes from different geographical locations showed considerable differences. In our study, the gut of C. ectenes from the same location achieved more similar profiles than those of C. ectenes from different locations, and a more significant difference of microbial community composition was found among the samples from different geographical locations. The result suggested that geographical location may critically influence the composition of the fish intestinal microbiota. In particular, the intestinal microbiota of C. ectenes was sensitive to the host habitat. Similarly, a previous study observed that the intestinal microbiota of the fish was affected by sampling locations [8]. The cause of location variation remains unknown but could include some differences in water chemistry, diet, and the types of environmental microorganisms in different locations, even host intrinsic factors, such as immunology, nutrition, and gastrointestinal development and physiology [7].

Analysis of differences in water qualify showed that the water quality of sampling sites exhibited significant difference in the concentration of DTP, NH4⁺–N and NO2-−N. A significant positive correlation was detected between OTU abundance and TN concentration. Correlation analysis suggests that the OTU abundance of the intestinal microbiota was affected by the TN concentration of the aquatic environment. Therefore, the TN concentration potentially participates in shaping the intestinal microbiota of C. ectenes. Nitrogen, as an essential element that maintains the physiological metabolism of most bacteria, was possibly transferred from the water to the gut of C. ectenes. More importantly, the TN concentration of the aquatic environment could contribute to the geographic variation in gut microbiota composition. We also found a significant positive correlation between OTU abundance and Chl-a. The Chl-a probably come from Cyanobacteria because of presence of large amounts of Chl-a in Cyanobacteria. This result also proved that the Cyanobacteria may be an important component in C. ectenes.

The water quality may be an important parameter for living environment of fish [10, 11], so the water quality monitoring could be extremely effective in helping protect the survival and reproduction of C. ectene. According to our result, there is a positive correlation between the concentration of total nitrogen and chlorophyll-a in the water and diversity of intestinal microbiota. Therefore, the concentration of total nitrogen and chlorophyll-a could make an initial understanding of intestinal microbiota diversity of C. ectenes. An appropriate concentration of total nitrogen or chlorophyll-a may increase survival rate under artificial feeding condition of C. ectenes.

In the microbiome community, the crucial KEGG pathways were environmental information processing, genetic information processing and metabolism. It was obvious that these pathways are essential physiological process for bacteria cell, revealing that the function of microbiome may play an important role in the nutrition and health of host.

5 Conclusion

In conclusion, we investigated the composition and diversity of the intestinal microbiota of C. ectenes and achieved a basic understanding of the fish’s gut microbiota. Our results demonstrated the presence of a complex community structure and abundant species composition in the intestinal microbiota of C. ectenes. We also found that geographical location was an important determinant for the fish’s intestinal microbiota. The intestinal microbiota of C. ectenes would be influenced by the concentrations of total nitrogen and chlorophyll-a in the water. An appropriate concentration of total nitrogen may help increase variety of microbiota to increase survival rate under artificial feeding condition of C. ectenes. The water quality may be an important parameter for living environment of fish, and the water quality monitoring could be extremely effective in helping protect the survival and reproduction of C. ectene. These findings eventually contribute to benefit the protection of fish resources in Lake Taihu, China and provid the cornerstone to sustainable utlization of C. ectenes.

Acknowledgments

The study was supported by the Basic Research Funds from Freshwater Research Center (No. 2015JBFM15).

Author Contributions: P.X. conceived and designed the experiments; J.D. performed the experiments; J.D., D.X. and K.L. analyzed the data; Y.Z. contributed reagents and analysis tools.
Conflict of Interest: Authors state no conflict of interest.

References

[1] Ringø E, Gatesoupe FJ, Lactic acid bacteria in fish: a review, 1998, 160(3–4), 177-203.10.1016/S0044-8486(97)00299-8Search in Google Scholar

[2] Ringo E, Birkbeck TH, Intestinal microflora of fish larvae and fry, 1999, 30(2), 73-93.10.1046/j.1365-2109.1999.00302.xSearch in Google Scholar

[3] Li X, Yu Y, Feng W, Yan Q, Gong Y, Host species as a strong determinant of the intestinal microbiota of fish larvae, 2012, 50(1), 29-37.10.1007/s12275-012-1340-1Search in Google Scholar

[4] Wu SG, Tian JY, Gatesoupe FJ, Li WX, Zou H, Yang BJ, et al., Intestinal microbiota of gibel carp (Carassius auratus gibelio) and its origin as revealed by 454 pyrosequencing, 2013, 29(9), 1585-1595.10.1007/s11274-013-1322-4Search in Google Scholar

[5] Bolnick DI, Snowberg LK, Hirsch PE, Lauber CL, Knight R, Caporaso JG, et al., Individuals’ diet diversity influences gut microbial diversity in two freshwater fish (threespine stickleback and Eurasian perch), 2014, 17(8), 979-987.10.1111/ele.12301Search in Google Scholar

[6] Ringø E, Sperstad S, Myklebust R, Refstie S, Krogdahl Å, Characterisation of the microbiota associated with intestine of Atlantic cod (Gadus morhua L.) : The effect of fish meal, standard soybean meal and a bioprocessed soybean meal, 2006, 261(3), 829-841.10.1016/j.aquaculture.2006.06.030Search in Google Scholar

[7] Roeselers G, Mittge EK, Stephens WZ, Parichy DM, Cavanaugh CM, Guillemin K, et al., Evidence for a core gut microbiota in the zebrafish, 2011, 5(10), 1595-1608.10.1038/ismej.2011.38Search in Google Scholar

[8] Ye L, Amberg J, Chapman D, Gaikowski M, Liu WT, Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish, 2014, 8(3), 541-551.10.1038/ismej.2013.181Search in Google Scholar

[9] Smith CC, Snowberg LK, Gregory Caporaso J, Knight R, Bolnick DI, Dietary input of microbes and host genetic variation shape among-population differences in stickleback gut microbiota, 2015, 9(11), 2515-2526.10.1038/ismej.2015.64Search in Google Scholar

[10] Lloyd DS, Turbidity as a water quality standard for salmonid habitats in Alaska, 1987, 7(1), 34-45.10.1577/1548-8659(1987)7<34:TAAWQS>2.0.CO;2Search in Google Scholar

[11] Karr JR, Dudley DR, Ecological perspective on water quality goals, 1981, 5(1), 55-68.10.1007/BF01866609Search in Google Scholar

[12] Jordan DS, Seale A, List of fishes collected in 1882-83 by Pierre Louis Jouy at Shanghai and Hongkong, China, 1905, 29(1433), 13.10.5479/si.00963801.29-1433.517Search in Google Scholar

[13] Ma C, Cheng Q, Zhang Q, Zhuang P, Zhao Y, Genetic variation of Coilia ectenes (Clupeiformes: Engraulidae) revealed by the complete cytochrome b sequences of mitochondrial DNA, 2010, 385(1–2), 14-19.10.1016/j.jembe.2010.01.015Search in Google Scholar

[14] Shen H, Gu R, Xu G, Xu P, Nie Z, Hu Y, In-depth transcriptome analysis of Coilia ectenes, an important fish resource in the Yangtze River: de novo assembly, gene annotation, 2015, 23, 15-17.10.1016/j.margen.2015.03.002Search in Google Scholar

[15] Mao ZG, Xiao-Hong GU, Zeng QF, Zhou LH, Wang XL, Lin-Kun WU, et al., Community structure and diversity of fish in Lake Taihu, 2011, 30(12), 2836-2842.Search in Google Scholar

[16] Ley RE, Peterson DA, Gordon JI, Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine, 2006, 124(4), 837-848.10.1016/j.cell.2006.02.017Search in Google Scholar

[17] De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, et al., Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa, 2010, 107(33), 14691-14696.10.1073/pnas.1005963107Search in Google Scholar

[18] Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, et al., Evolution of Mammals and Their Gut Microbes, 2008, 320(5883), 1647-1651.Search in Google Scholar

[19] Navarrete P, Espejo RT, Romero J, Molecular analysis of microbiota along the digestive tract of juvenile Atlantic salmon (Salmo salar L.), 2009, 57(3), 550-561.10.1007/s00248-008-9448-xSearch in Google Scholar

[20] Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al., QIIME allows analysis of high-throughput community sequencing data, 2010, 7(5), 335-336.10.1038/nmeth.f.303Search in Google Scholar

[21] Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R, UCHIME improves sensitivity and speed of chimera detection, 2011, 27(16), 2194-2200.10.1093/bioinformatics/btr381Search in Google Scholar

[22] Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ, Basic local alignment search tool, 1990, 215(3), 403-410.10.1016/S0022-2836(05)80360-2Search in Google Scholar

[23] Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C, Metagenomic microbial community profiling using unique clade-specific marker genes, 2012, 9(8), 811-814.10.1038/nmeth.2066Search in Google Scholar PubMed PubMed Central

[24] Langille MGI, Zaneveld J, Caporaso JG, Mcdonald D, Dan K, Reyes JA, et al., Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences, 2013, 31(9), 814.10.1038/nbt.2676Search in Google Scholar PubMed PubMed Central

[25] Neef A, Amann R, Schlesner H, Schleifer KH, Monitoring a widespread bacterial group: in situ detection of planctomycetes with 16S rRNA-targeted probes, 1998, 144 ( Pt 12), 3257-3266.10.1099/00221287-144-12-3257Search in Google Scholar PubMed

[26] Etyemez M, Balcazar JL, Bacterial community structure in the intestinal ecosystem of rainbow trout (Oncorhynchus mykiss) as revealed by pyrosequencing-based analysis of 16S rRNA genes, 2015, 100, 8-11.10.1016/j.rvsc.2015.03.026Search in Google Scholar PubMed

[27] Wu S, Wang G, Angert ER, Wang W, Li W, Zou H, Composition, diversity, and origin of the bacterial community in grass carp intestine, 2012, 7(2), e30440.10.1371/journal.pone.0030440Search in Google Scholar PubMed PubMed Central

[28] Larsen AM, Mohammed HH, Arias CR, Characterization of the gut microbiota of three commercially valuable warmwater fish species, 2014, 116(6), 1396-1404.10.1111/jam.12475Search in Google Scholar PubMed

[29] Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al., Linking long-term dietary patterns with gut microbial enterotypes, 2011, 334(6052), 105-108.Search in Google Scholar

[30] Duncan SH, Hold GL, Harmsen HJ, Stewart CS, Flint HJ, Growth requirements and fermentation products of Fusobacterium prausnitzii, and a proposal to reclassify it as Faecalibacterium prausnitzii gen. nov., comb. nov, 2002, 52(Pt 6), 2141-2146.10.1099/00207713-52-6-2141Search in Google Scholar PubMed

[31] Machiels K, Joossens M, Sabino J, De Preter V, Arijs I, Eeckhaut V, et al., A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis, 2014, 63(8), 1275-1283.10.1136/gutjnl-2013-304833Search in Google Scholar PubMed

[32] Sokol H, Pigneur B, Watterlot L, Lakhdari O, Bermudez-Humaran LG, Gratadoux JJ, et al., Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients, 2008, 105(43), 16731-16736.10.1073/pnas.0804812105Search in Google Scholar PubMed PubMed Central

[33] Miao C, Jia F, Wan Y, Zhang W, Lin M, Jin W, Halomonas huangheensis sp. nov., a moderately halophilic bacterium isolated from a saline-alkali soil, 2014, 64(Pt 3), 915-920.10.1099/ijs.0.056556-0Search in Google Scholar PubMed

[34] Falcinelli S, Picchietti S, Rodiles A, Cossignani L, Merrifield DL, Taddei AR, et al., Lactobacillus rhamnosus lowers zebrafish lipid content by changing gut microbiota and host transcription of genes involved in lipid metabolism, 2015, 5, 9336.Search in Google Scholar

[35] Falcinelli S, Rodiles A, Unniappan S, Picchietti S, Gioacchini G, Merrifield DL, et al., Probiotic treatment reduces appetite and glucose level in the zebrafish model, 2016, 6, 18061.10.1038/srep18061Search in Google Scholar PubMed PubMed Central

[36] Konikoff T, Gophna U, Oscillospira: a Central, Enigmatic Component of the Human Gut Microbiota, 2016, 24(7), 523-524.10.1016/j.tim.2016.02.015Search in Google Scholar PubMed

[37] Gophna U, Konikoff T, Nielsen HB, Oscillospira and related bacteria - From metagenomic species to metabolic features, 2017, 19(3), 835-841.10.1111/1462-2920.13658Search in Google Scholar PubMed

[38] Zurawell RW, Chen H, Burke JM, Prepas EE, Hepatotoxic cyanobacteria: a review of the biological importance of microcystins in freshwater environments, 2005, 8(1), 1-37.10.1080/10937400590889412Search in Google Scholar PubMed

[39] Kamjunke N, Mendonca R, Hardewig I, Mehner T, Assimilation of different cyanobacteria as food and the consequences for internal energy stores of juvenile roach, 2002, 60(3), 731-738.10.1111/j.1095-8649.2002.tb01697.xSearch in Google Scholar

[40] Kamjunke N, Schmidt K, Pflugmacher S, Mehner T, Consumption of cyanobacteria by roach (Rutilus rutilus): useful or harmful to the fish?, 2002, 47(2), 243–250.10.1046/j.1365-2427.2002.00800.xSearch in Google Scholar

[41] Chen Y, Zhu S, Diet Overlap of Lake Anchovy (Coilia ectenes) and Ice Fish (Neosalanx tangkahkeii taihuensis) and the Relationship between Their Harvests in Lake Taih, 2008, 23(3), 463-465.10.1080/02705060.2008.9664225Search in Google Scholar

Received: 2017-6-23

Accepted: 2017-8-30

Published Online: 2017-10-23

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.