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BMC Genomics

, 20:829 | Cite as

High-throughput sequencing reveals the core gut microbiota of the mud crab (Scylla paramamosain) in different coastal regions of southern China

  • Hongling Wei
  • Huan WangEmail author
  • Lei Tang
  • Changkao Mu
  • Chunyu Ye
  • Lizhi Chen
  • Chunlin WangEmail author
Open Access
Research article
Part of the following topical collections:
  1. Eukaryote microbial genomics

Abstract

Background

Scylla paramamosain is a commercially important mud crab. The microbiota is a community that inhabits the crab intestine, and is important for physiological functional and host health.

Results

Proteobacteria, Firmicutes, Bacteroidetes, Tenericutes, Spirochaetae and Fusobacteria were the dominant phyla of the 36 representative phyla. Eleven genera of the 820 representative genera were considered as core gut microbiota and were distributed in the five dominant phyla. The core genus of the Proteobacteria included Arcobacter, Photobacterium, Vibrio, Shewanella and Desulfovibrio. The other four phyla contained one or two genera. Male and female crab samples had two different core genera, (male samples: Psychrilyobacter & Lactococcus; female samples: Clostridium_sensu_stricto_11 and Candidatus_Bacilloplasma).

Conclusions

This is the first time core intestinal microbiota have been identified in crab from nine coastal regions of southern China. This study provides sequencing data related to the gut microbiota of S. paramamosain, and may contribute to probiotic development for S. paramamosain aquaculture industries.

Keywords

Scylla paramamosain Core gut microbiota Illumina MiSeq sequencing 16S rRNA 

Abbreviations

F

female

HL

Hele county, Wanning city, Hainan province

HP

Hepu county, Behai city, Guangxi zhuang autonomous region

M

male

RA

Ruian county, Wenzhou city, Zhejiang province

S. paramamosain

Scylla paramamosain

SM

Sanmen county, Taizhou city, Zhejiang province

ST

Shantou city, Guangdong province

TS

Taishan county, Zhuhai city, Guangdong province

XP

Xiapu county, Ningde city, Fujian province

YJ

Yangjiang city, Guangdong province

YX

Yunxiao county, Zhangzhou city, Fujian province

Background

Scylla paramamosain is a commercially important mud crab distributed along the coasts of southern China and other Indo-Pacific countries [1, 2, 3, 4]. Mud crab production reached 231,467 tons in 2017 in China [5]. Currently, thanks to its richness, rapid growth, and high market value, the species is important in both fisheries and aquaculture in southern China [6, 7, 8].

The microbiota inhabits the intestine which is an important physiological functional organ in S. paramamosain, and is closely related to host health [9, 10]. Much research in humans has shown that the gut microbiota plays basic roles in nutrient absorption and immune function, which is beneficial to host health [11, 12]. Some pathological conditions, such as, inflammatory bowel disease [13], liver cirrhosis [14], cancer [15], obesity [16], and Type 1 Diabetes [17] appear to be caused by disruption to its normal balance. Research has shown that the gut microbiota are widely involved in organ development, nutrition, immunity and crustacean diseases [18, 19, 20, 21]. Other gut microbiome research has shown that the health, eating habits and crustacean habitats are key to the formation of a symbiotic gut bacteria model [22, 23]. Although a close relationship between the crab and its gut microbiota is increasingly accepted, limited data are available on the gut microbiota of S. paramamosain from Southern Chinese coasts. As part of aquaculture development, it is crucial to develop better probiotics to facilitate S. paramamosain industries, by unraveling gut microbial composition.

In this study, Illumina MiSeq sequencing of 16S rRNA was used to identify gut microbial composition in S. paramamosain. Samples from southern Chinese coasts were compared to characterize core gut microbiota.

Results

Data summary

After filtering low-quality reads, trimming the longer homopolymer runs, adapters, barcodes and primers, and rarefying datasets, 21,993 (sample YJ-M) to 28,377 (sample XP-F) valid contigs were collected from each region, resulting in a total of 472,782 valid contigs from the nine regions. All valid contigs were delineated into OTUs using 97% sequence similarity thresholds, consistent with other studies performing deep sequencing methods [24]. A total of 2552 OTUs were obtained. Each region sample contained 87 (sample XP-F) to 755 (sample HL-M) OTUs, which differed no significantly in most of the male crabs samples than the female crabs samples (Table 1). Nevertheless, HL and ST crabs generated significant differences between female and male samples (HL: P = 0.0041, ST: P = 0.0037).
Table 1

Overview of sequencing data and alpha-diversity of samples from the nine coastal regions of southern China

Group

Valid contigs

OTU

Shannon

Simpson

Chao 1

Good’s coverage

HL-F

26,157 ± 1969

215 ± 57

3.48 ± 0.216

0.84 ± 0.030

338 ± 125.87

0.995 ± 0.0016

HL-M

26,168 ± 2337

755 ± 148

5.44 ± 0.993

0.91 ± 0.021

858 ± 109.83

0.988 ± 0.0008

HP-F

27,904 ± 860

154 ± 53

3.42 ± 0.495

0.80 ± 0.040

189 ± 52.33

0.997 ± 0.0008

HP-M

26,118 ± 2418

453 ± 280

4.29 ± 1.123

0.88 ± 0.070

527 ± 317.45

0.992 ± 0.0047

SM-F

28,219 ± 821

286 ± 176

4.57 ± 2.047

0.89 ± 0.080

318 ± 150.82

0.997 ± 0.0014

SM-M

27,103 ± 1061

270 ± 113

3.41 ± 0.367

0.79 ± 0.066

353 ± 146.69

0.994 ± 0.0025

RA-F

27,495 ± 1216

318 ± 193

4.79 ± 1.872

0.90 ± 0.079

385 ± 133.53

0.996 ± 0.0015

RA-M

25,757 ± 2074

179 ± 43

3.85 ± 0.594

0.86 ± 0.075

213 ± 65.40

0.997 ± 0.0011

ST-F

26,952 ± 1164

174 ± 66

3.09 ± 1.118

0.75 ± 0.143

248 ± 107.24

0.996 ± 0.0015

ST-M

27,729 ± 2215

429 ± 30

4.84 ± 1.043

0.92 ± 0.045

596 ± 91.95

0.991 ± 0.0022

TS-F

22,555 ± 3723

530 ± 242

3.55 ± 0.896

0.76 ± 0.075

617 ± 269.36

0.990 ± 0.0044

TS-M

26,197 ± 1560

186 ± 97

3.55 ± 0.579

0.84 ± 0.071

266 ± 170.60

0.996 ± 0.0030

XP-F

28,377 ± 698

87 ± 14

2.47 ± 0.471

0.70 ± 0.068

129 ± 57.67

0.998 ± 0.0006

XP-M

26,811 ± 1415

92 ± 43

2.43 ± 0.984

0.66 ± 0.217

153 ± 103.13

0.998 ± 0.0015

YJ-F

26,447 ± 657

594 ± 368

5.12 ± 2.241

0.89 ± 0.086

695 ± 394.79

0.991 ± 0.0054

YJ-M

21,993 ± 5211

378 ± 80

5.52 ± 1.623

0.92 ± 0.077

394 ± 70.29

0.998 ± 0.0016

YX-F

25,662 ± 2052

219 ± 186

4.74 ± 1.923

0.91 ± 0.066

242 ± 167.04

0.998 ± 0.0004

YX-M

25,138 ± 6631

480 ± 8

6.98 ± 0.149

0.98 ± 0.003

487 ± 15.34

0.998 ± 0.0004

The characterization of bacterial community richness, diversity and sequencing depth was performed using the alpha diversity index (Table 1). The Chao1 indices, which ranged from 129 ± 57.67 to 858 ± 109.83, were used to determine bacterial community richness in S. paramamosain. There were no differences in Shannon and Simpson indices. The Good’s coverage estimator of the samples ranged from 0.990 to 0.998 (Table 1), indicating that sequencing depths covered all species in samples. Meanwhile, the sparse curve reaches the saturation platform (Additional file 1: Figure S1A), manifesting that the sequencing depth is large enough to obtain a stable and unbiased estimate of species richness. In addition, the specaccum accumulation curves tend to gradually, indicating that the sample size is sufficient to reflect the abundance of the community richness, the results reflect the rate of increase in new species observed as the sample size continues to increase during the overall sampling of the sample. The number of OTUs increased rapidly from 1 to 54 and began to level off at the end of our sampling, indicating that bacterial diversity was largely saturated (Additional file 1: Figure S1B).

Composition of microbial communities in S. paramamosain

At the taxonomic level, six different patterns of intestinal microbial composition were distinguished. As shown (Additional file 1: Table S1), the number of taxonomic units detected in each region were present. The pattern of gut microbial composition in male samples was greater than female samples (Additional file 1: Table S1). Additional file 1: Figure S2-S4 show microbial community composition at Class, Order, and Family levels.

We found 36 different phyla (Additional file 1: Table S1) in all samples. There were differences between male and female samples (30 phyla in female samples and 35 phyla in male samples). Figure 1a and Additional file 1: Table S2 show the top 15 highly abundant phyla. Tenericutes, Proteobacteria, Bacteroidetes, Firmicutes and Fusobacteria were identified in all samples, another ten phyla were only detected in one or several samples, including Spirochaetae, Actinobacteria, Acidobacteria, Gemmatimonadetes, CKC4, Deferribacteres, Cyanobacteria, Gracilibacteria, WCHB1_60 and Nitrospirae. Proteobacteria levels were significantly different between female and male samples (P = 0.0085). Tenericute levels were significantly different between female and male samples (P = 0.0346). No significant differences were found in the other thirteen phyla.
Fig. 1

The 15 most abundant phyla and genera. a Bar-plots showing the abundance and distribution of the 15 most abundant phyla. b Bar-plots showing the abundance and distribution of the 15 most abundant genera

At the genus level, sequences from samples represented 820 genera (Additional file 1: Table S1). Genera in male samples numbered 45 more than in female samples (711 genera in female samples and 756 genera in male samples). The top 15 genera are listed in Fig. 1b and Additional file 1: Table S3; Candidatus_Hepatoplasma, Arcobacter, Photobacterium, Vibrio, Carboxylicivirga, Bacteroides, Spirochaeta_2, Psychrilyobacter, Sunxiuqinia, Parabacteroides, Defluviitaleaceae_UCG_011, Lachnoclostridium, Shewanella, Enterococcus, and Clostridium_sensu_stricto_11. These 15 genera accounted for nearly half or more of the total sequences in the samples. Candidatus_Hepatoplasma and Shewanella abundance differed significantly in female samples when compared to male samples (P = 0.0286, P = 0.0291, respectively). No significant differences were found for the other thirteen genera.

Core gut microbiota at the genus level in S. paramamosain

A major goal of the study was to determine whether a common core microbiota was shared among all samples. At the genus level, we assigned 11 genera candidates (Table 2), each of these candidates exhibited a frequency of occurrence higher than 90% over all samples and were treated as core gut microbiota [25, 26]. We analyzed 11 core genera and found that these organisms constituted a phylogenetic core of the genera, accounting for 48.81% of all sequences (Fig. 2a). These 11 core genera were distributed among five phyla, and 63.6% of these genera were in the Proteobacteria and Firmicutes, with the remaining in the Bacteroidetes, Tenericutes and Spirochaetae genera. However, from this study, the relative abundance of these core genera varied greatly across samples (Fig. 2b and Table 2).
Table 2

The core genera identified in samples

Phylum

Genus

Relative abundance (%)

Range (%)

Tenericutes

Candidatus_Hepatoplasma

16.89

0.000–74.325

Proteobacteria

Arcobacter

6.89

0.058–40.186

Proteobacteria

Photobacterium

6.81

0.000–42.027

Proteobacteria

Vibrio

4.48

0.074–22.604

Bacteroidetes

Carboxylicivirga

3.94

0.004–29.438

Bacteroidetes

Bacteroides

3.70

0.000–47.259

Spirochaetae

Spirochaeta_2

3.48

0.000–35.887

Proteobacteria

Shewanella

0.85

0.000–7.066

Firmicutes

Lactobacillus

0.73

0.000–11.112

Firmicutes

Romboutsia

0.68

0.000–17.830

Proteobacteria

Desulfovibrio

0.36

0.000–4.543

Fig. 2

Core gut microbiota composed of 11 bacterial genera in S. paramamosain in nine regions samples. a The proportion of each genus in all sequences combined. b The abundance and distribution of 11 core genera. c Correlation matrix showing the Spearman’s rank correlations among the collective core, which ranges from − 1 to 1, corresponding to a strongly positive to a strongly negative correlation, respectively

We also investigated these intergeneric the co-occurrence patterns of these genera based on Spearman’s rank correlations (Fig. 2c). We observed that the genus Candidatus_Hepatoplasma was inversely associated with almost every other genera (Spearman’s rank correlation coefficients (ρ) ranged from − 0.45 to 0.17) and Bacteroides showed relatively strong negative correlations with Photobacterium (ρ = − 0.53). Vibrio was positively associated with other genera besides Candidatus_Hepatoplasma, Lactobacillus and Bacteroides (Spearman’s rank correlation coefficients (ρ) ranged from − 0.27 to 0.68). Other genera were positively or inversely correlated with each other to different degrees.

We also found genus level differences, between male and female samples, in the core gut microbiota. Fifteen core genera were distributed between male and female samples, (Tables 3 and 4), and thirteen uniform core genera were distributed between male and female samples (Fig. 3). There were two additional core genera in female samples (Clostridium_sensu_stricto_11 & Candidatus_Bacilloplasma), and two special core genera in male samples (Psychrilyobacter & Lactococcus).
Table 3

The core genera identified in female samples

Phylum

Genus

Relative abundance (%)

Range (%)

Tenericutes

Candidatus_Hepatoplasma

22.71

0.000–54.672

Proteobacteria

Photobacterium

6.12

0.000–40.127

Proteobacteria

Arcobacter

5.88

0.083–28.949

Bacteroidetes

Bacteroides

5.55

0.000–47.259

Bacteroidetes

Carboxylicivirga

4.78

0.206–29.438

Spirochaetae

Spirochaeta_2

4.54

0.000–35.887

Proteobacteria

Vibrio

3.05

0.074–18.715

Firmicutes

Defluviitaleaceae_UCG_011

1.59

0.000–7.729

Firmicutes

Clostridium_sensu_stricto_1

0.70

0.000–4.331

Firmicutes

Clostridium_sensu_stricto_11

0.52

0.000–6.029

Firmicutes

Romboutsia

0.41

0.000–6.419

Proteobacteria

Shewanella

0.39

0.007–3.808

Firmicutes

Lactobacillus

0.37

0.000–3.316

Proteobacteria

Desulfovibrio

0.29

0.007–1.440

Tenericutes

Candidatus_Bacilloplasma

0.24

0.000–1.511

Table 4

The core genera identified in male samples

Phylum

Genus

Relative abundance (%)

Range (%)

Tenericutes

Candidatus_Hepatoplasma

11.07

0.007–74.325

Proteobacteria

Arcobacter

7.91

0.058–40.186

Proteobacteria

Photobacterium

7.51

0.000–42.027

Proteobacteria

Vibrio

5.90

0.098–22.604

Fusobacteria

Psychrilyobacter

3.19

0.000–21.912

Bacteroidetes

Carboxylicivirga

3.09

0.004–14.964

Spirochaetae

Spirochaeta_2

2.42

0.000–23.831

Bacteroidetes

Bacteroides

1.84

0.000–16.469

Firmicutes

Defluviitaleaceae_UCG_011

1.47

0.000–12.943

Proteobacteria

Shewanella

1.32

0.000–5.223

Firmicutes

Lactobacillus

1.10

0.004–11.112

Firmicutes

Romboutsia

0.94

0.000–17.830

Firmicutes

Lactococcus

0.70

0.000–3.718

Firmicutes

Clostridium_sensu_stricto_1

0.60

0.000–4.932

Proteobacteria

Desulfovibrio

0.43

0.000–4.543

Fig. 3

Core genera identified in male and female samples

Gut microbiota relationships across S. paramamosain, in nine regions

This study investigated relationships of gut microbial communities in 54 samples using weighted UniFrac PCoA and hierarchical dendrogram analyses (Fig. 4). There were obvious separated and overlapped samples for each of the regions. Composition of the community in the nine regions, had changed. Among these, samples derived from SM, RA, XP and YX were grouped closer than the other five flocks. However, there were differences between male and female samples in community compositions. Samples HP-F, ST-F, TS-F, XP-F, YJ-F and YX-F clustered closer than samples HL-F, SM-F and RA-F. Furthermore, samples HF-M, SM-M, TS-M and YX-M were closer than samples HP-M, RA-M, ST-M, XP-M and YJ-M in term of community composition (Fig. 5a and b). UPGMA clustering analyses, based on weighted UniFrac distances, also indicated a similarly discriminative structural separation between male and female samples (Fig. 5c, d).
Fig. 4

Principal coordinate analysis and circular tree plot of all samples using the weighted UniFrac distance matrices. a Principal coordinate analysis of the microbial communities in all samples; b Circular tree plot of all samples using the weighted UniFrac distance matrices

Fig. 5

Principal coordinate analysis and circular tree plot of female or male samples using the weighted UniFrac distance matrices. a Principal coordinate analysis of the microbial communities in female samples; b Principal coordinate analysis of the microbial communities in male samples; c Circular tree plot of female samples using the weighted UniFrac distance matrices; d Circular tree plot of male samples using the weighted UniFrac distance matrices

Discussion

S. paramamosain are usually cultured in brackish, seawater ponds along the coasts of southern China and other Indo-Pacific countries. It is a commercially important mud crab distributed [3, 27]. Breeding of S. paramamosain mainly occurs along the coasts of southern China (Additional file 1: Figure S1), including Zhejiang, Fujian, Guangdong, Guangxi, Hainan province. Although gut microbiota regulates many aspects of digestive function, nutrition, metabolism, fat storage and gut-associated mucosal immunity [28], little is known about gut bacterial community structures in S. paramamosain. Hence, this study sought to examine gut microbial diversity and core gut microbiota of S. paramamosain from nine coastal regions in southern China. To the best of our knowledge, this study is the first to characterize core gut microbiota from S. paramamosain from southern Chinese coasts using state of the art, Illumina MiSeq sequencing methodologies.

Analysis of gut microbiota composition demonstrated that the dominant bacteria of the fifty-four samples belonged to six phyla, Proteobacteria, Firmicutes, Bacteroidetes, Tenericutes, Spirochaetae and Fusobacteria, and the first four phyla were also found in the Eriocheir sinensis gastrointestinal tract [29]. These results were consistent with a previous study on gut bacterial assemblages of Eriocheir sinensis from Lake Tai (286 km from Lake Gucheng in China) [30]. These dominant genera may play major roles in gut function or adapt to the environment by the digestive tract.

The 11 core genera constituted a phylogenetic core of the genus, accounting for 48.81% of total sequences. Among them, Tenericutes from the genus Candidatus_Hepatoplasma, accounted for the greatest average relative abundance. Previous research had discovered that isopods with intestinal tract based Candidatus_Hepatoplasma, had higher survival rates when food was deficient [31]. However, this has not yet been reported in S. paramamosain. In China, artificially cultured crabs are located in ponds, with little phytoplankton or zooplankton. Similarly, breeding densities are higher. In addition, farmers feed crabs at fixed times, therefore, S. paramamosain may be in hungry environments for prolonged periods. So, we speculated that the reason the mud crab could be able to adapt to thehunger environment is the regulation of Candidatus_Hepatoplasma. However, this conjecture must be corroborated by further research.

The core genera; Arcobacter, Photobacterium, Vibrio, Shewanella and Desulfovibrio belong to Proteobacteria. The genus Arcobacter is common in many marine invertebrates, such as crabs [32], mussels [33], abalones [34], and oysters [35]. The genus Photobacterium, which is one of the nine genera in the family Vibrionaceae (order “Vibrionales”, class Gammaproteobacteria), is the largest genera after Vibrio [36, 37]. Some of its species exhibit bioluminescence and pathogenesis mechanisms [38], with one study reporting that Photobacterium is a potential freshwater fish pathogen [39]. Worldwide, Vibrio is widely distributed in aquatic environments. However, many Vibrio members are considered primary pathogens in causing disease and death in aquaculture animals [40], and they seriously jeopardize the development of aquaculture. Many studies have shown that Vibrio provides a benefit to the host, for example, Asfie et al., [41] isolated multiple strains of Protease-producing bacteria from the gingiva intestinal tract, and showed that some proteases secreted by Vibrio are beneficial to gums, growth and development. Similarly, Hamid et al., [42] observed that Vibrio secreted amylases, proteases, lecithinases and chitinases to help digest important nutrients such as fat, proteins and carbohydrates in the host body. Further research also showed that Vibrio was present in both healthy and diseased S. paramamosain [27]. We therefore speculated that Vibrio may digest important nutrients such as fat, protein and carbohydrates in the host body by secreting amylases and proteases to maintain normal activities in healthy crabs. Therefore, it was not surprising that Vibrio was found in samples in this study. Our study has also illustrated the diversity of Vibrio and its beneficial role as a dominant bacteria in the intestine. The separation and identification of beneficial Vibrio species may promote crab aquaculture production.

Firmicutes are often found in the gut of marine invertebrates, such as sea squirt (Ciona intestinalis) [43], black tiger shrimp (Penaeus monodon) [44] and the Atlantic blue crab (Callinectes sapidus) [32]. The Lactobacillus genus belongs to the Firmicutes, which are commonly found in the gastrointestinal tracts of humans and other animals. In this study, Lactobacillus was also found in these nine regions, therefore, we speculated it may have potential probiotic properties in S. paramamosain. Studies have shown that due to its relevance in industrial applications in certain species, such as L. lactis, the central metabolic pathway of this genus has been extensively studied. These bacteria can convert large hexose sugar substrates to pyruvate via glycolysis and then to lactate [45]. Lactococcus is the focus of intensive research in carbohydrate catabolism, the industrial fermentation process [46] and its role in promoting health, such as the prevention and protection of diarrhea and intestinal infections, are important for a well-balanced gut microbiota [47, 48]. So, on one hand, due to it can prevent and protect diarrhea and intestinal infections, it is not surprising that it can be found in all samples from nine regions. On the other hand, it is worthy of further study to isolate and characterize the functional bacteria of this genus from intestinal samples, and it may develop probiotics for the S. paramamosain breeding industries. Moreover, the genus Bacteroides from the Bacteroidetes phyla has been associated with animal protein metabolism, a variety of 354 Yang et al., amino acids and saturated fats [49]. And other core genera of Shewanella, Desulfovibrio, Romboutsia, Carboxylicivirga and Spirochaeta_2 in functional study have not yet been reported.

According to the experience of many farmers, there were differences in development processes between male and female crabs. However, there were 13 identical genera in the mid-gut population of male and female crabs (Fig. 3). This meant that gender had no significant effects on gut composition in S. paramamosain. This was consistent with Jin et al., [50] and their study, the intestinal flora of Eriocheir sinensis in Yangcheng West Lake. An interesting discovery from this study was the difference between male and female samples in terms of the core gut microbiota, at the genus level. There were two different core genera in female samples (Clostridium_sensu_stricto_11 and Candidatus_Bacilloplasma), and two additional core genera in male samples (Psychrilyobacter and Lactococcus). At present, the functions of these four genera have not been reported. We speculated these genera may be the reason for differences in flavors between male and female crabs. It will be interesting to determine which gut microbiota cause flavor differences. In this study, the 11 core genera were distributed in the five dominant phyla. Among these, five genera (Arcobacter, Photobacterium, Vibrio, Shewanella, Desulfovibrio) belonged to Proteobacteria, while the other four phyla contained one or two genera, such as Bacteroidetes (genus Carboxylicivirga & Bacteroides), Firmicutes (genus Lactobacillus & Romboutsia), Tenericutes (genus Candidatus_Hepatoplasma), and Spirochaetae (genus Spirochaeta_2). We speculated that these phyla and genera present in the S. paramamosain’ gut might have many reasons. Firstly, in terms of internal factors, in China, although the seedlings of these S. paramamosain have broken through the key technologies of large-scale breeding [51], the artificial breeding technology is not quite ripe yet. So mud crab seedlings are mainly derived from natural sea areas, these crabs in nine regions may be not the same source, they are the same species, S. paramamosain, therefore they are hereditary. We suspect that they have these common core genera that may be genetic factors.

On the other hand, environmental factors have also been reported to affect gut microbial composition [52]. Our analyses found 11 core genera in nine different regions. This observation may indicate that environmental factors do little to affect the structure of the gut microbiota of S. paramamosain or the lack of an effect may have been due to similar natural conditions (Additional file 1: Table S4), such as temperature, salinity, pH, dissolved oxygen (DO) and so on. It may be that these nine regions are all at suitable temperatures and salinities and they live in ponds that are separated by mud with black plastic membranes to prevent the loss of crabs.

Conclusions

In this study, we used Illumina MiSeq sequencing to clarify the gut microbiota of composition from S. paramamosain. This was the first time we identified core intestinal microbiota from nine coastal regions of southern China. We analyzed 472,782 valid tags from nine regions samples, of which 2552 OTUs were identified. Our results showed that Proteobacteria, Firmicutes, Bacteroidetes, Tenericutes, Spirochaetae and Fusobacteria were the dominant phyla of the 36 representative phyla. 11 genera of the 820 representative genera were considered as core gut microbiota and were distributed in the five dominant phyla. The core genera distributed in Proteobacteria were the most genera, including Arcobacter, Photobacterium, Vibrio, Shewanella, Desulfovibrio. While other four phyla contain one or two genera. Moreover, there were differences between male and female samples in the core gut microbiota, at the genus level. There were two special genera in female samples (Clostridium_sensu_stricto_11 and Candidatus_Bacilloplasma), and two special genera in male samples (Psychrilyobacter and Lactococcus). This study has generated much sequencing data related to the gut microbiota of S. paramamosain and may contribute to probiotic development for mud crab aquaculture industries in the future.

Methods

Sample collection in different areas

Mud crab breeding is mainly distributed along the coast of southern China (Additional file 1: Figure S5), including Zhejiang, Fujian, Guangdong, Guangxi, Hainan provinces. Firstly, we purchased crabs from local farmers in nine areas between May and June 2017. These crab collection areas were at Sanmen county, Taizhou city, Zhejiang province (SM, 29°06′16.81″N, 121°23′44.45E), Ruian county, Wenzhou city, Zhejiang province (RA, 27°46′42.17″N, 120°39′18.65″E), Xiapu county, Ningde city, Fujian province (XP, 26°53′6.61″N, 120°0′20.02″E), Yunxiao county, Zhangzhou city, Fujian province (YX, 23°57′29.02″N, 117°20′22.74″E), Shantou city, Guangdong province (ST, 23°21′22.19″N, 116°40′39.89″E), Yangjiang city, Guangdong province (YJ, 21°51′39.78″N, 111°58′39.04″E), Taishan county, Zhuhai city, Guangdong province (TS, 22°12′24.13″N, 113°17′50.43″E), Hepu county, Behai city, Guangxi zhuang autonomous region (HP, 21°39′48.50″N, 109°12′10.98″E), Hele county, Wanning city, Hainan province (HL, 18°54′0.52″N, 110°28′32.39″E). In these locations, we caught mud crab. Each region chose good limbs and good vitality 12 female and male crabs (body weight: 250-450 g), immediately isolated their intestinal tracts and contents and froze samples in liquid nitrogen. Samples were transported to the lab on dry ice. When extracting DNA, every three samples were mixed into one, so each region had six samples, with a total of 54. All samples were stored in sterile containers.

DNA extraction, 16S rRNA gene amplification, and Illumina MiSeq sequencing

Genomic DNA was extracted from each sample using the DNeasy PowerLyzer PowerSoil Kit (QIAGEN, Hilden, Germany). DNA concentration and purity were assessed on 1% agarose gels. The V3-V4 region of the bacterial 16S rRNA gene was amplified using the barcode-fusion forward primer 343F (5′- TACGGRAGGCAGCAG-3′) and the reverse primer 798R (5′- AGGGTATCTAATCCT − 3′). The PCR was performed in a 30 μl volume with 15 μl of 2 × HiFi Hot Start Ready Mix, 1.0 μl of forward and reverse primers (10 μmol/L), and 50 ng of genomic DNA as template. Thermal cycling consisted of initial denaturation at 94 °C for 5 min; followed by 26 cycles of denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s, elongation at 72 °C for 30 s and a final extension at 72 °C for 7 min.

The PCR products were purified using Agencourt Ampure XP beads (Beckman, CA, USA) according to the manufacturer’s instructions. 5 μl purified product were detected by 1% agarose gel electrophoresis and 1 ul purified product for concentration detection using a NanoDrop Lite spectrophotometer (Thermo Scientific). The purified DNA was then used as a template to perform a second PCR amplification using the same primer sequences and the protocol described above, however, 8 cycles were employed. After a further purification with the AMPure XP beads, the amplicon was measured concentration using NanoDrop Lite spectrophotometer. Purifed amplicons were pooled in equimolar and pairedend sequenced (2 × 300) on an Illumina MiSeq platform.

Bioinformatic analyses

Raw sequencing data came in FASTQ format. Paired-end reads were preprocessed using Trimmomatic software [53], to detect and remove ambiguous bases (N). The software also removed low quality sequences, with average quality scores below 20, using the sliding window trimming approach. After trimming, paired-end reads were assembled using FLASH software [54]. The parameters for assembly were: 10 bp minimal overlap, 200 bp maximum overlap and a 20% maximum mismatch rate. Sequences were performed further denoising as follows: contigs with ambiguous, homologous sequences or below 200 bp were abandoned. Reads with 75% of bases above Q20 were retained. Contigs with chimeras were detected and removed. These steps were achieved using the QIIME software (version 1.8.0) [55]. Clean reads were subjected to primer sequence removal and clustering to generate operational taxonomic units (OTUs) using UPARSE software, with 97% similarity cutoff [56]. The representative read of each OTU was selected using QIIME software. All representative reads were annotated and blasted against the Silva database Version 123 (or Greengens) (16 s rDNA) using RDP classifier (confidence threshold was 70%) [57]. For alpha-diversity metrics, Observed Species, Chao1 estimator, Shannon Wiener Index, Simpson diversity index, Good’s Coverage with QIIME (Version 1.8.0) and displayed with R software (Version 2.15.3). For beta-diversity metrics, the weighted UniFrac distance matrix [58] was calculated and visualized using Principal Coordinate Analysis (PCoA) in QIIME. Biomarker discovery analysis of each taxonomic unit was performed using LeFse (version 1.0.7) [59]. All figures were generated with customized R scripts.

Statistics

All statistical analyses were performed using SPSS (SPSS 21.0). ANOVA was also used for statistical analyses. Correlations between the core genera of gut microbiota were appraised by calculating nonparametric Spearman’s rank correlation coefficients, which were displayed in a correlation matrix. Results were considered significant at P < 0.05.

Notes

Acknowledgements

We thank Oebiotech (Oebiotech, Shanghai, China) for sequencing consultation and support.

Authors’ contributions

CW and HW1 (Huan Wang) conceived and designed the study. HW2 (Hongling Wei), HW1and LT took samples of experimental animals. HW2, HW1, LT, CM, CY and LC performed and analyzed all the other experiments. HW2 wrote the manuscript with support from all authors. All authors read and approved the final manuscript.

Funding

This work was supported by the Major Science and Technology Special Project of Zhejiang Province (No. 2016C02055–8); the Ministry of Agriculture of China and China Agriculture Research System (No. CARS-48); the Marine and Fishery Bureau, Sanmen County, Zhejiang Province (HK2017000088); and the K. C. Wong Magna Fund in Ningbo University. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethics approval and consent to participate

The animals used in this study are crabs, which are invertebrates and are exempt from ethical requirements. S. paramamosain is not an endangered or protected species. All animal work was conducted according to the relevant national and international guidelines. No specific permissions are required to work with invertebrates in China. Similarly, no specific permissions are required for the collection of S. paramamosain from sample sites because they were not collected from protected areas.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12864_2019_6219_MOESM1_ESM.docx (1 mb)
Additional file 1: Table S1. Number of bacterial taxonomic units. Table S2. Mean relative abundance of the 15 most abundant phyla in samples from the nine coastal regions of Southern China. Table S3. Mean relative abundance of the 15 most abundant genera in samples from the nine coastal regions of Southern China. Table S4. Salinity, temperature, pH, dissolved oxygen (DO), ammonia-nitrogen and nitrite levels of the nine regions of Southern China. Figure S1. Impact of sequencing depth and sampling on bacterial phylotypes. Figure S2. Composition of microbial communities at the Class level. Figure S3. Composition of microbial communities at the Order level. Figure S4. Composition of microbial communities at the Family level. Figure S5. Sample collection in the nine coastal regions of Southern China.

References

  1. 1.
    Lin Z, Hao M, Zhu D, et al. Molecular cloning, mRNA expression and nutritional regulation of aΔ6 fatty acyl desaturase-like gene of mud crab, Scylla paramamosain. Comp Biochem Physiol B Biochem Mol Biol. 2017;208-209:29–37.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Walton ME, Vay LL, Lebata JH, et al. Seasonal abundance, distribution and recruitment of mud crabs (Scylla, spp.) in replanted mangroves. Estuar Coast Shelf Sci. 2006;66(3–4):493–500.CrossRefGoogle Scholar
  3. 3.
    Wang H, Wei H, Tang L, et al. A proteomics of gills approach to understanding salinity adaptation of Scylla paramamosain. Gene. 2018a;677:119–31.PubMedCrossRefPubMedCentralGoogle Scholar
  4. 4.
    Wang Z, Sun B, Zhu F. Epigallocatechin-3-gallate inhibit replication of white spot syndrome virus in Scylla paramamosain. Fish Shellfish Immunol. 2017;67:612–9.PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    China fishery statistical yearbook, China's Ministry of Agriculture, 2018.Google Scholar
  6. 6.
    Ma LB, Zhang FY, Ma CY, et al. Scylla paramamosain (Estampador) the most common mud crab (Genus Scylla) in China: evidence from mtDNA. Aquac Res. 2006;37:1694–8.CrossRefGoogle Scholar
  7. 7.
    Wang H, Wei H, Tang L, et al. Identification and characterization of miRNAs in the gills of the mud crab (Scylla paramamosain) in response to a sudden drop in salinity. BMC Genomics. 2018b;19:609.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Ye H, Tao Y, Wang GZ, et al. Experimental nursery culture of the mud crab Scylla paramamosain (Estampador) in China. Aquacult Int. 2011;19:313–21.CrossRefGoogle Scholar
  9. 9.
    Greathouse KL, Faucher MA, Hastings-Tolsma M. The gut microbiome, obesity, and weight control in Women's reproductive health. West J Nurs Res. 2017;39(8):1094–119.PubMedCrossRefGoogle Scholar
  10. 10.
    Kau AL, Ahern PP, Griffin NW, et al. Human nutrition, the gut microbiome and the immune system. Nature. 2011;474(7351):327–36.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Huang CH, Yu X, Liao WB. The Expensive-Tissue Hypothesis in Vertebrates: Gut Microbiota Effect, a Review. Int J Mol Sci. 2018;19(6):1792.PubMedCentralCrossRefGoogle Scholar
  12. 12.
    Rizzetto L, Fava F, Tuohy KM, et al. Connecting the immune system, systemic chronic inflammation and the gut microbiome: the role of sex. J Autoimmun. 2018;92:12–34.PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Ok SJ. Gut microbiota in inflammatory bowel disease. Pediatr Gastroenterol Hepatol Nutr. 2013;16(1):17–21.CrossRefGoogle Scholar
  14. 14.
    Qin N, Yang F, Li A, et al. Alterations of the human gut microbiome in liver cirrhosis. Nature. 2014;513(7516):59–64.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Garrett WS. Cancer and the microbiota. Science. 2015;348:80–6.PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Meijnikman AS, Gerdes VE, Nieuwdorp M, et al. Evaluating causality of gut microbiota in obesity and diabetes in humans. Endocr Rev. 2018;39(2):133–53.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Han H, Li Y, Fang J, et al. Gut Microbiota and Type 1 Diabetes. Int J Mol Sci. 2018;19(4):995.PubMedCentralCrossRefGoogle Scholar
  18. 18.
    Cheung MK, Yip HY, Nong W, et al. Rapid change of microbiota diversity in the gut but not the Hepatopancreas during gonadal development of the new shrimp model Neocaridina denticulata. Mar Biotechnol (NY). 2015;17(6):811–9.CrossRefGoogle Scholar
  19. 19.
    Olmos J, Ochoa L, Paniagua-Michel J, et al. Functional feed assessment on Litopenaeus vannamei using 100% fish meal replacement by soybean meal, high levels of complex carbohydrates and Bacillus probiotic strains. Mar Drugs. 2011;9(6):1119–32.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Wang Y, Zhang C, Wang H, et al. Involvement of a newly identified atypical type II crustin (SpCrus5) in the antibacterial immunity of mud crab Scylla paramamosain. Fish Shellfish Immunol. 2018;75:346–56.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Xiong J. Progress in the gut microbiota in exploring shrimp disease pathogenesis and incidence. Appl Microbiol Biotechnol. 2018;102(17):7343–50.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Wu T, Zhang Z, Cai C, et al. The effect of pectin and Xylan on intestinal microflora structure of Chinese mitten crab. Genomics & Applied Biology. 2015;34(4):745–53 (In Chinese with English abstract).Google Scholar
  23. 23.
    Zhang ML, Sun YH, Chen LQ, et al. Symbiotic bacteria in gills and guts of Chinese mitten crab (Eriocheir sinensis) differ from the free-living bacteria in water. PLoS One. 2016;11(1):e0148135.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Waite DW, Eason DK, Taylor MW. Influence of hand rearing and bird age on the fecal microbiota of the critically endangered kakapo. Appl Environ Microbiol. 2014;80(15):4650–8.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Roeselers G, Mittge EK, Stephens WZ, et al. Evidence for a core gut microbiota in the zebrafish. ISME J. 2011;5(10):1595–608.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Li S, Sun L, Wu H, et al. The intestinal microbial diversity in mud crab (Scylla paramamosain) as determined by PCR-DGGE and clone library analysis. J Appl Microbiol. 2012;113(6):1341–51.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Pajarillo EA, Ju KO, Chae JP, et al. Administration of probiotic Enterococcus faecium NCIMB 11181 changes the fecal microbiota and diversity in weaned pigs. Asian Conference on Lactic Acid Bacteria. 2015.Google Scholar
  29. 29.
    Ding ZF, Cao MJ, Zhu XS, et al. Changes in the gut microbiome of the Chinese mitten crab (Eriocheir sinensis) in response to white spot syndrome virus (WSSV) infection. J Fish Dis. 2017;40(11):1561–71.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Chen X, Di P, Wang H, et al. Bacterial community associated with the intestinal tract of Chinese mitten crab (Eriocheir sinensis) farmed in Lake tai. China PLoS One. 2015;10(4):e0123990.PubMedCrossRefGoogle Scholar
  31. 31.
    Fraune S, Zimmer M. Host-specificity of environmentally transmitted mycoplasma-like isopod symbionts. Environ Microbiol. 2008;10(10):2497–504.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Givens CE, Burnett KG, Burnett LE, et al. Microbial communities of the carapace, gut, and hemolymph of the Atlantic blue crab, Callinectes sapidus Mar Biol, 2013; 160 (11):2841–2851.CrossRefGoogle Scholar
  33. 33.
    Collado L, Cleenwerck I, Van TS, et al. Arcobacter mytili sp. nov., an indoxyl acetate-hydrolysis-negative bacterium isolated from mussels. Int J Syst Evol Microbiol. 2009;59(Pt 6):1391–6.PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Tanaka R, Ootsubo M, Sawabe T, et al. Biodiversity and in situ abundance of gut microflora of abalone (Haliotis discus hannai) determined by culture-independent techniques. Aquaculture. 2004;241(1):453–63.CrossRefGoogle Scholar
  35. 35.
    Romero J, Garcíavarela M, Laclette JP, et al. Bacterial 16s rRNA gene analysis revealed that bacteria related to Arcobacter spp. constitute an abundant and common component of the oyster microbiota (Tiostrea chilensis). Microb Ecol. 2002;44(4):365–71.PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Pérez-Cataluña A, Lucena T, Tarazona E, et al. An MLSA approach for the taxonomic update of the Splendidus clade, a lineage containing several fish and shellfish pathogenic Vibrio spp. Syst Appl Microbiol. 2016;39(6):361–9.PubMedCrossRefGoogle Scholar
  37. 37.
    Sawabe T, Ogura Y, Matsumura Y, et al. Updating the Vibrio clades defned by multilocus sequence phylogeny: Proposal of eight new clades, and the description of Vibrio tritonius sp. nov. Front Microbiol. 2013;4:414.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Labella AM, Arahal DR, Castro D, et al. Revisiting the genus Photobacterium: taxonomy, ecology and pathogenesis. Int Microbiol. 2017;20(1):1–10.PubMedGoogle Scholar
  39. 39.
    Cantas L, Sørby JR, Aleström P, et al. Culturable gut microbiota diversity in Zebrafish. Zebrafish. 2012;9(1):26–37.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Wang FI, Chen JC. The immune response of tiger shrimp Penaeus monodon, and its susceptibility to Photobacterium damselae, subsp. damselae, under temperature stress. Aquaculture. 2006;258(1):34–41.CrossRefGoogle Scholar
  41. 41.
    Hansen GH, Olafsen JA. Bacterial interactions in early life stages of marine cold water fish. Microb Ecol. 1999;38(1):1–26.PubMedCrossRefGoogle Scholar
  42. 42.
    Hamid A, Sakata T, Kakimoto D. Microflora in the alimentary tract of grey mullet. IV. Estimation of enzyme activities of the intestinal bacteria. Nihon-suisan-gakkai-shi. 1979;45(1):99–106.CrossRefGoogle Scholar
  43. 43.
    Dishaw LJ, Florestorres J, Lax S, et al. The gut of geographically disparate Ciona intestinalis harbors a core microbiota. PLoS One. 2014;9(4):e93386.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Rungrassamee W, Klanchui A, Maibunkaew S, et al. Characterization of intestinal bacteria in wild and domesticated adult black tiger shrimp (Penaeus monodon). PLoS One. 2014;9(3):e91853.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Price CE, Zeyniyev A, Kuipers OP, et al. From meadows to milk to mucosa- adaptation of Streptococcus, and Lactococcus, species to their nutritional environments. FEMS Microbiol Rev. 2012;36(5):949–71.PubMedCrossRefGoogle Scholar
  46. 46.
    Mayo B, Aleksandrzak-Piekarczyk T, Fernández M, et al. Updates in the metabolism of lactic acid bacteria. In: Mozzi F, Raya RR, Vignolo GM, editors. Biotechnology of lactic acid bacteria: novel applications. Oxford, U.K.: Wiley-Blackwell; 2010. p. 3–33.  https://doi.org/10.1002/9780813820866.ch1.CrossRefGoogle Scholar
  47. 47.
    Konstantinov SR. Lactobacilli in the porcine intestine: From composition to functionality. Wageningen, the Netherlands: PhD Thesis Wageningen University; 2005.Google Scholar
  48. 48.
    Hu Y, Dun Y, Li S, et al. Dietary Enterococcus faecalis LAB31 improves growth performance, reduces diarrhea, and increases fecal Lactobacillus number of weaned piglets. PLoS One. 2015;10(1):e0116635.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Zhang J, Guo Z, Lim AQ, et al. Mongolians core gut microbiota and its correlation with seasonal dietary changes. Sci Rep. 2014;4:5001.PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Jin B. Study on the intestinal flora of Eriocheir sinensis in Yangcheng West Lake. Nanjing Agricultural University, 2016.Google Scholar
  51. 51.
    Qiao ZG, Ma LB, Yu ZL, et al. Current status and development goals of marine crab culture in China. Fisheries Modernization. 2009;36(3):45–8 (In Chinese).Google Scholar
  52. 52.
    Bian GR, Ma SQ, Zhu ZG, et al. Age, introduction of solid feed and weaning are more important determinants of gut bacterial succession in piglets than breed and nursing mother as revealed by a reciprocal cross-fostering model. Environ Microbiol. 2016;18(5):1566–77.CrossRefGoogle Scholar
  53. 53.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Reyon D, Tsai SQ, Khayter C, et al. FLASH assembly of TALENs for high-throughput genome editing. Nat Biotechnol. 2012;30(5):460–5.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8.CrossRefGoogle Scholar
  57. 57.
    Wang Q, Garrity GM, Tiedje JM, et al. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.School of Marine ScienceNingbo UniversityNingboChina
  2. 2.Key Laboratory of Applied Marine Biotechnology, Ministry of EducationNingbo UniversityNingboChina
  3. 3.Agricultural and Rural BureauSanmen CountyChina
  4. 4.Fishery Technology StationSanmen CountyChina

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