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Article

Effects of Ambient Microbiota on the Gut Microbiota of Macrobrachium rosenbergii

by
Qianfu Liu
1,2,3,4,
Yuan Gao
1,2,3,4,*,
Chao Wang
1,2,3,4,
Yanyi Zeng
1,2,3,4,
Jiajia Ni
5,6 and
Zini Lai
1,2,3,4,*
1
Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, China
2
Guangzhou Scientific Observing and Experimental Station of National Fisheries Resources and Environment, Guangzhou 510380, China
3
Fishery Ecological Environment Monitoring Center of Pearl River Basin, Ministry of Agriculture and Rural Affairs, Guangzhou 510380, China
4
Key Laboratory of Aquatic Animal Immune Technology of Guangdong Province, Guangzhou 510380, China
5
Research and Development Center, Guangdong Meilikang Bio-Science Ltd., Dongguan 523808, China
6
Dongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Medical University, Dongguan 523808, China
*
Authors to whom correspondence should be addressed.
Water 2022, 14(4), 658; https://doi.org/10.3390/w14040658
Submission received: 29 January 2022 / Revised: 16 February 2022 / Accepted: 17 February 2022 / Published: 20 February 2022
(This article belongs to the Topic Microorganisms in Aquatic Environments)
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Abstract

:
The gut microbiota (GM) compositions of aquatic animals are influenced by microorganisms in ambient water and sediment. However, the extent to which environmental microorganisms can affect shrimp GM composition is unknown. We analyzed the impact of water and sediment microorganisms on the GM of Macrobrachium rosenbergii at different growth stages. We collected water, sediment, and M. rosenbergii gut samples at the early, middle, and late stages of an M. rosenbergii culture and analyzed the microbiota composition. The shrimps’ body weight differed significantly between sampling stages. The shrimp GM composition differed significantly from that of the ambient water and sediment, and these differences were remarkably stronger than those between the shrimp GM at different sampling times and in different ponds. The proportion of sediment bacteria in the shrimp GM was approximately three times higher than that of water bacteria, which changed among ponds and over sampling time. These results provide important reference information for a deeper understanding of the impact of environmental microorganisms on the composition of shrimp GM. Moreover, the results also provide reference information for increasing the production of shrimp culture as well as ensuring a good health status of the culture.

1. Introduction

Gut microorganisms are important for nutrient absorption, growth, metabolism, immunity, and disease resistance in aquatic animals. Their composition and diversity are important factors that affect the hosts’ growth and health [1,2,3,4]. For instance, Wu et al. [5] found that the relative abundances of Shewanella algae and Neptunomonas in the gut microbiota (GM) of fast-growing white shrimp (Litopenaeus vannamei) were significantly higher than those of slow-growing shrimp. In contrast, the relative abundances of Delftia, Hydrogenophaga, Pseudomonas, Synechococcus, Methylibium, Acidovorax, Limnohabitans, Burkholderia, Candidatus Koribacter, and Vogesella in the GM of fast-growing shrimp were significantly lower.
However, the GM composition in aquatic animals is not constant due to influences from the aquatic environment, diet, growth of the animal, and other factors [6]. Xiong et al. [7] found that the gut bacterial communities of white shrimp clustered according to their original habitat and health status, which constrained the variation in the bacterial communities by 14.6% and 7.7%, respectively. Furthermore, Dai et al. [8] reported several gut bacterial indicators that can characterize shrimp nutrient status, with more abundant opportunistic pathogens in starved shrimp. Moreover, they found that starved shrimp exhibited less connected and cooperative interspecies interactions than normal cohorts. Huang et al. [6] reported that the relative abundances of gut-dominant taxa in white shrimp were significantly different between the middle and late rearing stages. Compared with the gut and water, cluster analysis revealed that the shrimp gut and sediment had a similar profile of dominant bacterial genera. However, it is unclear to what extent the microbiota of the ambient water and sediment affects the composition of the shrimp GM.
Giant freshwater prawn, Macrobrachium rosenbergii, is an important aquaculture species in many Asia-Pacific countries [9]. In 2018, the global production of M. rosenbergii was 234.4 thousand tons, which accounted for 2.50% of the total products of crustaceans [10].
Our study examined the impact of environmental microbiota on the GM of M. rosenbergii at different growth stages. We collected water, sediment, and M. rosenbergii gut samples in the early, middle, and late stages of an M. rosenbergii culture and analyzed the microbiota composition. This study provides important reference information for us to understand the impact of environmental microorganisms on the composition of shrimp GM.

2. Materials and Methods

2.1. Sample Collection and Determination of Water Physicochemical Properties

Pond water, sediment, and M. rosenbergii samples were collected from three outdoor ponds (113°09′11″ E, 22°38′30″ N) located in Hetang Town (Pengjiang District, Jiangmen, China) on 15 July (early stage of the shrimp culture), 21 September (middle stage), and 22 October (late stage) 2020. Each pond had an approximate area of 4000 m2 and annual shrimp production of 2400 kg.
At each stage, we collected a water sample in each pond from 0.5 m below the water surface using a 5 L plexiglass water sampler. The water samples were stored at 4 °C until further analysis. Further, sediment samples (approximately 5 g each) were collected from three different locations in each pond, placed into 50 mL sterile centrifuge tubes, transported to the laboratory at 4 °C, and stored at −80 °C until DNA extraction. Additionally, for each pond and each stage, 50 shrimp samples were randomly collected and weighed. Of these samples, three randomly selected individuals were collected and transported to the laboratory at 4 °C for dissection under sterile conditions. Gut samples were collected in 2 mL sterile Eppendorf tubes and stored at −80 °C for DNA extraction.
Water temperature (WT, °C), pH, dissolved oxygen (DO, mg/L), Salinity (Sal, ‰); oxidation–reduction potential (ORP, mV), conductivity (Cond, μS/m), and total dissolved solids (TDS, g/L) were measured in the field using a portable multiparameter water quality analyzer (YSI, Yellow Springs, OH, USA). Turbidity (Turb, NTU) was measured by turbidimeter (Sheng’aohua, Shanghai, China). Chlorophyll a (Chl a) was measured using a spectrophotometer. Ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), nitrite nitrogen (NO2−-N), phosphate (PO4-P), Silicate (Si), total nitrogen (TN), and total phosphorus (TP) were determined using a San++ flow injection water quality analyzer (Skalar, Netherlands). The permanganate index (CODMn) was determined following the standard method ISO 8467:1993. The concentration of unionized ammonia (NH3) was calculated according to the method described by Zou and Cheng [11].

2.2. DNA Extraction and High-Throughput Sequencing of 16S rDNA

DNA extraction and high-throughput sequencing of 16S rDNA were performed as previously described by Liu et al. [12]. Briefly, 500 mL of each water sample were filtered to collect microorganisms using a GF/C membrane with 0.22 μm pore size (Whatman, Maidstone, UK) prior to DNA extraction [13]. The microbial DNA in the water, sediment (0.5 g of wet weight), and shrimp gut samples were extracted using the DNeasy PowerSoil kit (QIAGEN, Hilden, Germany). The V4-V5 hypervariable region of prokaryotic 16S rDNA was amplified using primers 515F and 909R [14]. The amplicons were sequenced using a HiSeq 3000 platform (Illumina, San Diego, CA, USA) at Guangdong Meilikang Bio-Science, Ltd. (Dongguan, China).
Raw reads were merged using Flash 1.2.8 [15] and processed using QIIME 1.9.0 [16]. Low-quality barcode, primer, and chimeric sequences were removed before performing the operational taxonomic unit (OTU) clustering at 97% identity using UPARSE [17]. Alpha diversity indices (i.e., OTU number, Shannon, Simpson, Chao1 indices, and Goods’ coverage) were calculated using QIIME 1.9.0. Taxonomic assignment of each OTU was determined using the Ribosomal Database Project Classifier [18].

2.3. Data Analysis

Data are presented as the mean ± standard error. Shapiro–Wilk normality test and Bartlett test were conducted through R 4.0.3 [19] to test data normal distribution and homogeneity of variances, respectively. Then, one-factor analysis of variance (one-way ANOVA) and a Kruskal–Wallis H-test were chosen to test statistical difference of data. To quantify the effect of pond, sampling time, and habitat (i.e., water, sediment, and shrimp gut) on the microbiota composition, we used the Kruskal–Wallis H-test with a Welch’s post-hoc test. This allowed us to analyze the number and proportion of OTUs and genera with significant differences in their relative abundance between the microbiota grouped according to habitat, pond, and sampling time. A non-parametric multivariate analysis of variance (PERMANOVA) [20] was used to determine significant differences in microbiota composition between the groups using the vegan package [21] in R 4.0.3 [19]. A principal coordinate analysis (PCoA) was conducted using QIIME 1.9.0 and a partial redundancy analysis (RDA) was performed using the R vegan package to analyze the affected proportion of ambient factors on the microbiota. Pearson correlation coefficients were calculated and statistically tested using R psych and reshape2 packages. Furthermore, a source tracking analysis was conducted using the R SourceTracker package [22]. p-values < 0.05 were considered statistically significant.

3. Results

3.1. Changes in Shrimp Body Weight and Water Physiochemical Properties

The shrimp body weight differed significantly between the three sampling stages (one-way ANOVA, p < 0.05; Figure 1A). Based on the pond water physicochemical properties (pH, WT, Sal, TDS, ORP, DO, Cond, Turb, PO4-P, TP, TN, NO3−-N, NO2−-N, NH4+-N, NH3, Si, CODMn, Chl a, and ratio of TN and TP), the Pearson correlation coefficients between different ponds at the same sampling time were significantly higher than those of the same pond between July and October, and between September and October (Figure 1B). This indicated that the difference in physicochemical properties between different sampling times, especially from September to October, was significantly higher than that between ponds at the same sampling time (Figure 1B). These results showed that as the aquaculture progressed, the differences in the water’s physicochemical properties between the ponds increased gradually. A cluster heatmap based on the physicochemical properties showed that three samples from the same pond mostly clustered together (Figure 1C). Further, samples from ponds 1 and 2 clustered according to the sampling time, while samples from pond 3 clustered according to the pond in July and September, but clustered with the samples from the other ponds in October (Figure 1C). This shows that the water’s physicochemical properties differed more between the sampling stages than between ponds at the same stage.

3.2. Differences in Microbiota Composition between Pond Water, Sediment, and Shrimp Gut

To analyze the habitat and gut microbiota composition of M. rosenbergii, 6,660,868 high-quality sequences were obtained from a total of 63 samples (i.e., 27 sediment, 27 gut, and 9 water samples). Finally, 8242 high-quality sequences were randomly selected from each sample for further analysis. In total, 12,845 OTUs were identified. The PCoA revealed significant differences between the water, sediment, and shrimp gut microbiota (PERMANOVA, F = 2.168, p = 0.005; Figure 2A). However, shrimp GM did not differ significantly between sampling stages (PERMANOVA, F = 1.088, p = 0.279). The number of OTUs in the sediment microbiota was significantly higher than that in the shrimp GM and the water microbiota (Kruskal–Wallis H-test, p < 0.01; Figure 2B). When considering OTU abundance changes, the Shannon and Simpson indices of sediment microbiota were still significantly higher than those of shrimp gut and water microbiota (Kruskal–Wallis H-test, p < 0.05; Figure 2C,D). The alpha diversity indices between shrimp gut and water microbiota did not differ significantly (Kruskal–Wallis H-test, p > 0.05; Figure 2C,D). The differences in the number and abundance of OTUs between sediment, water, and shrimp gut microbiota also led to significant differences in Good’s coverage (Kruskal–Wallis H-test, p < 0.05; Figure 2E).
The composition of the dominant phyla also showed differences between shrimp gut, water, and sediment microbiota. The relative abundances of Cyanobacteria and Actinobacteria in the water microbiota were significantly higher than those in shrimp gut and sediment microbiota. In contrast, relative abundances of Acidobacteria and Chloroflexi in the sediment microbiota were significantly higher than those in shrimp gut and water microbiota. The relative abundance of Tenericutes in shrimp GM was significantly higher than that in the water and sediment microbiota (Kruskal–Wallis H-test, p < 0.05; Figure 2F).

3.3. Effects of Pond, Sampling Time, and Habitat on Microbiota Structure

The numbers of OTUs and genera with significant differences were the largest, according to habitat, accounting for 30.44% and 56.01% of the total number of OTUs and genera in the microbiota, respectively (Table 1). The numbers of OTUs and genera with significant differences were the smallest according to pond, accounting for 1.69% and 1.88%, respectively (Table 1). The partial RDA showed that at the OTU level, habitat explained 17.09% of the variation in microbiota composition, followed by sampling time (2.41%) and pond (0.77%; Figure 3A). A similar trend was observed at the genus level, although there were some differences in the degree of interpretation. At the genus level, habitat, sampling time, and pond explained 16.61%, 6.39%, and 0.46% of the microbiota variety, respectively (Figure 3B). These results implied that water, sediment, and shrimp gut were the main factors causing the variety of microbiota, which was consistent with the results of the PCoA and Kruskal–Wallis H-test (Table 1).

3.4. Effects of Environmental Microbiota on Shrimp Gut Microbiota Structure

To analyze the effects of ambient microbiota on the GM composition of M. rosenbergii, the proportion of microbiota from water and sediment in the GM was analyzed using the source tracking method. The number of bacteria entering the GM of M. rosenbergii from the sediment was approximately three times higher than that from water. The proportion of bacteria entering the GM from sediment or water differed significantly between ponds (Figure 3C–E). Moreover, the number of bacteria entering the GM from sediment was significantly higher than that from water across all sampling stages, with only a few differences between the stages (Figure 3F–H).

4. Discussion

As GM plays a vital role in the physiological processes in aquatic animals, the factors affecting its composition have been widely investigated. Habitat, feeding habits, developmental stage, and health status are the main factors affecting the composition of GM in aquatic animals [3,7,23,24,25]. Our results showed that the composition of shrimp GM did not differ significantly between the three sampling stages (Figure 2A), although the samples were clustered according to sampling times, regarding basic water physicochemical properties (Figure 1C). These results suggest that the composition of the shrimp GM was more stable than that of the water physicochemical properties. This was probably because of the formation of a more stable microbial habitat in the gut than in the ambient water environment.
Considering the vital roles of microbes in aquaculture systems, a detailed understanding of the microbiota in such ecosystems can be helpful for establishing ecological strategies to sustain the successful production process of shrimp farming [6]. In previous studies [6,12], bacterial community structures were significantly different among water, sediment, and shrimp guts. However, these bacterial communities have not been completely isolated. Our results showed that the proportion of bacteria entering the GM of M. rosenbergii from the sediment in each pond was significantly higher than that from the water, and the number of bacteria entering the GM from sediment was approximately three times higher than that from the water. These results are consistent with those of the previous study [6]. However, these results were contrary to the results of a study on largemouth bass (Micropterus salmoides), in which the proportion of bacteria entering the GM from sediment was significantly lower than that in the water [12]. This may be due to the different living and feeding habits of the two aquatic animals. Shrimp mainly live on the bottom and eat the humus on the sediment’s surface [26,27]. Our results suggest that farms producing M. rosenbergii should pay attention not only to water quality, but also to the quality of sediment to avoid disturbance of the M. rosenbergii GM due to changes in the sediment microbiota.
The ecological mechanisms of maintaining the diversity of microbial communities, such as environmental filtering, dispersal limitation, and stochastic and deterministic processes, have attracted extensive attention [28,29,30,31] and have been used to analyze the diversity maintenance mechanism of aquatic animal GM [32]. Xiong et al. [33] speculated that the distinct gut community assembly could be attributed to random colonization in larval shrimp, and that an altered microbiota could cause overgrowth or retardation in shrimp. However, comparing the microbiota structures of pond water, sediment, and shrimp gut, our results imply that environmental filtering is probably the most important factor affecting the diversity of the shrimp GM.
Host physicochemical factors are closely related to its GM composition [33,34,35]. However, because of the small body size of shrimp, most studies focus only on the interaction between digestive enzymes and the GM as proxies to investigate this relationship [8,33]. In this study, we did not determine the physicochemical properties of shrimp. Therefore, we could not explain how they influence the shrimp GM. Consequently, further research is required to reveal more insights on how habitat physicochemical factors and microbiota affect the GM, physicochemical factors, growth, and health of shrimp.

5. Conclusions

The composition of the shrimp GM differed significantly from that of water and sediment. Differences between the composition of shrimp gut, pond water, and sediment microbiota were markedly higher than those between shrimp GM at different sampling times and ponds, and the proportion of bacteria from sediment microbiota in shrimp GM was approximately three times that of the water microbiota, which showed large differences among ponds and over sampling time. These results provide important reference information for a deeper understanding of the impact of environmental microorganisms on the composition of shrimp GM. Moreover, the results also provide reference information for increasing the production of shrimp culture as well as ensuring the good health status of the culture.

Author Contributions

Conceptualization, Q.L., Z.L. and Y.G.; methodology, Q.L. and Y.G.; software, Q.L., Y.Z. and J.N.; validation, Q.L., C.W. and J.N.; formal analysis, Q.L., Y.Z., J.N. and Y.G.; investigation, Q.L., Z.L., C.W. and Y.G.; resources, Q.L., Z.L., Y.Z. and Y.G.; data curation, Q.L. and Y.G.; writing—original draft preparation, Q.L.; writing—review and editing, Z.L. and Y.G.; visualization, Q.L., C.W., Y.Z. and J.N.; supervision, C.W. and Y.G.; project administration, Z.L. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams, grant number 2021KJ151, Develop Effluent Standards for Aquaculture in Guangdong Province, grant number GPCGD211115FD122F, and the Central Public-interest Scientific Institution Basal Research Fund, CAFS, grant number 2022SJ-TD01.

Data Availability Statement

Merged DNA sequences were deposited in the NCBI Sequence Read Archive database with accession number PRJNA798512 (https://www.ncbi.nlm.nih.gov/sra/PRJNA798512 accessed on 29 January 2022).

Conflicts of Interest

The authors declare no conflict of interest.

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Water 14 00658 g001 550
Figure 1. Comparison of the shrimp body weight between three sampling stages and three ponds (A), Pearson correlation coefficients between different ponds at the same sampling time and of the same pond between different sampling times calculated based on water physicochemical properties (B), and heatmap profile based on the water physicochemical properties (C). Different letters above the boxes in figure panel B indicate significant differences between groups. Cond, conductivity; Sal, salinity; TDS, total dissolved solids; WT, water temperature; N/P, ratio of total nitrogen and total phosphorus; Si, Silicate; NH3, unionized ammonia; CODMn, the permanganate index; Chla, chlorophyll a; pH, pH value; DO, dissolved oxygen; NH4-N, ammonium nitrogen; PO4-P, phosphate; TP, total phosphorus; ORP, oxidation-reduction potential; Turb, turbidity; NO2-N, nitrite nitrogen; TN, total nitrogen; NO3-N, nitrogen nitrate. ***, p < 0.001.
Figure 1. Comparison of the shrimp body weight between three sampling stages and three ponds (A), Pearson correlation coefficients between different ponds at the same sampling time and of the same pond between different sampling times calculated based on water physicochemical properties (B), and heatmap profile based on the water physicochemical properties (C). Different letters above the boxes in figure panel B indicate significant differences between groups. Cond, conductivity; Sal, salinity; TDS, total dissolved solids; WT, water temperature; N/P, ratio of total nitrogen and total phosphorus; Si, Silicate; NH3, unionized ammonia; CODMn, the permanganate index; Chla, chlorophyll a; pH, pH value; DO, dissolved oxygen; NH4-N, ammonium nitrogen; PO4-P, phosphate; TP, total phosphorus; ORP, oxidation-reduction potential; Turb, turbidity; NO2-N, nitrite nitrogen; TN, total nitrogen; NO3-N, nitrogen nitrate. ***, p < 0.001.
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Figure 2. Differences in microbiota composition of shrimp gut, pond water, and sediment. (A) PCoA profile; (B) OTU number; (C) Shannon index; (D) Simpson index; (E) Good’s coverage; (F) bar chart shows compositions of dominant phyla in shrimp gut, pond water, and sediment. **, p < 0.01; ***, p < 0.001.
Figure 2. Differences in microbiota composition of shrimp gut, pond water, and sediment. (A) PCoA profile; (B) OTU number; (C) Shannon index; (D) Simpson index; (E) Good’s coverage; (F) bar chart shows compositions of dominant phyla in shrimp gut, pond water, and sediment. **, p < 0.01; ***, p < 0.001.
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Figure 3. Partial redundancy analysis and source tracking results showed the contribution of various factors to microbiota. (A) Partial RDA results based on OTU composition; (B) Partial RDA results based on genus composition; (C–H), Source tracking results in pond 1, pond 2, pond 3, July, September, and October, respectively.
Figure 3. Partial redundancy analysis and source tracking results showed the contribution of various factors to microbiota. (A) Partial RDA results based on OTU composition; (B) Partial RDA results based on genus composition; (C–H), Source tracking results in pond 1, pond 2, pond 3, July, September, and October, respectively.
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Table
Table 1. Number and proportion of taxa with significant difference obtained according to different grouping pattern.
Table 1. Number and proportion of taxa with significant difference obtained according to different grouping pattern.
HabitatPondSampling Time
Number of significantly different OTUs3910217381
Number of total OTUs12,84512,84512,845
Proportion of significantly different OTUs30.44%1.69%2.97%
Number of significantly different genera6852354
Number of total genera122312231223
Proportion of significantly different genera56.01%1.88%4.42%
p-value<0.05<0.05<0.05
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Liu, Q.; Gao, Y.; Wang, C.; Zeng, Y.; Ni, J.; Lai, Z. Effects of Ambient Microbiota on the Gut Microbiota of Macrobrachium rosenbergii. Water 2022, 14, 658. https://doi.org/10.3390/w14040658

AMA Style

Liu Q, Gao Y, Wang C, Zeng Y, Ni J, Lai Z. Effects of Ambient Microbiota on the Gut Microbiota of Macrobrachium rosenbergii. Water. 2022; 14(4):658. https://doi.org/10.3390/w14040658

Chicago/Turabian Style

Liu, Qianfu, Yuan Gao, Chao Wang, Yanyi Zeng, Jiajia Ni, and Zini Lai. 2022. "Effects of Ambient Microbiota on the Gut Microbiota of Macrobrachium rosenbergii" Water 14, no. 4: 658. https://doi.org/10.3390/w14040658

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