Characteristics of intestinal microbiota in the Pacific white shrimp Litopenaeus vannamei differing growth performances in the marine cultured environment

Accepted Manuscript Characteristics of intestinal microbiota in the Pacific white shrimp Litopenaeus vannamei differing growth performances in the marine cultured environment

Lanfen Fan, Qing X. Li PII: DOI: Reference:

S0044-8486(19)30092-4 https://doi.org/10.1016/j.aquaculture.2019.02.075 AQUA 633957

To appear in:

aquaculture

Received date: Revised date: Accepted date:

12 January 2019 25 February 2019 28 February 2019

Please cite this article as: L. Fan and Q.X. Li, Characteristics of intestinal microbiota in the Pacific white shrimp Litopenaeus vannamei differing growth performances in the marine cultured environment, aquaculture, https://doi.org/10.1016/j.aquaculture.2019.02.075

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ACCEPTED MANUSCRIPT Manuscript revised (R2) according to editor’s and reviewers’ comments for possible publication in Aquaculture

Characteristics of intestinal microbiota in the Pacific white shrimp

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Litopenaeus vannamei differing growth performances in the marine

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cultured environment

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Lanfen Fan1,2*, Qing X. Li2*

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1. Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation, College of Marine Sciences, South China Agricultural University, Guangzhou, 510642, China 2. Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA

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* Corresponding author: Lanfen Fan, College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China, E-mail: [email protected]

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* Corresponding author: Qing X. Li, Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA, E-mail: [email protected] AUTHOR INFORMATION

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Corresponding Authors

*E-mail: [email protected] (L.F.) *E-mail: [email protected] (Q.X.L.) ORCID Lanfen Fan: 0000-0002-8154-1516 Qing X. Li: 0000-0003-4589-2869

ACCEPTED MANUSCRIPT Abstract: The Pacific white shrimp Litopenaeus vannamei is the most widely cultivated shrimp in the world, particularly in Asia. The growth performance is a vital factor in shrimp farming. In the present study, comparisons were made among the bacterial communities in shrimp intestines and the related marine farm sediments that supported different growth

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performances, based on Miseq sequencing data of the V3-V4 region of 16S rRNA gene. Fifty

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three phyla were identified with the classifiable sequence. Sequencing data demonstrated a

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statistically significant diversity in microbiota compositions at the phylum and genus levels. The dominant phyla were Proteobacteria, Actinobacteria, Cyanobacteria, Bacteroidetes,

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Chloroflexi, Firmicutes, Verrucomicrobia, and Saccharibacteria. Of which, Actinobacteria

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and Saccharibacteria were more abundant in the shrimp intestines with a normal growth performance, these phyla may be related with shrimp immunity and digestion. Proteobacteria

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were more abundant in shrimp intestines with slow growth performance, an increased

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abundance of Proteobacteria was a potential risk of disease. At the genus level, norank_f__Propionibacteriaceae, Ruegeria, Robiginitalea, unclassified_c__Actinobacteria

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and norank_f__TM146 exhibited extremely statistically significant differences at 0.001 < P ≤

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0.01 while norank_c__Cyanobacteria exhibited statistically significant differences at 0.01 < P ≤ 0.05 among the four groups. The relative abundances of the intestinal bacterial communities in the normal growth shrimps differed significantly from those in the slow growth shrimps. The Firmicutes/Bacteroidetes ratio in the normal growth shrimp intestines was 3.08/3.31 in comparison with 0.34/6.04 in the slow growth shrimp intestines, which suggests that the normal growth shrimp can absorb the nutrient better than the slow growth shrimp. Overall, this study provides valuable findings on the shrimp intestinal microbiota and

ACCEPTED MANUSCRIPT helps guide the healthy aquaculture practices.

Keywords: Aquaculture; Litopenaeus vannamei; Microbiota; Intestine; Growth performance.

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1. Introduction

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The intestine is a complex ecosystem containing a vast number of microorganisms

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(Sekirov et al., 2010). The intestinal microbiota plays crucial roles to diverse physiological processes of the host, including metabolic and nutritional homeostasis, energy expenditure

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and immunity (Lankelma et al., 2015; Walter et al., 2011; Wu and Wu, 2012; Xia et al., 2018;

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Zhu et al., 2016; Zmora et al., 2017). The microbiome also plays a crucial role in immune development and maintenance of the host metabolic state (Belkaid and Hand, 2014; Lin and

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Zhang, 2017; Thursby and Juge, 2017; Wu and Wu, 2012). A balanced intestinal microbiota is

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crucial to host health. The disturbance of microbial diversity in the intestine may cause host stresses, diseases and the host slow growth (Belkaid and Hand, 2014; Clarke et al., 2014c). It

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is aquaculturally meaningful to study the assembly and coordination of intestinal microbiota

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for the host. In the recent few years, the intestinal microbiota of some aquatic animals has been studied, such as Grass carp (Ctenopharyngodon idellus) (Hao et al., 2017; Wu et al., 2012; Yang et al., 2019), Chinese mitten crab (Eriocheir sinensis) (Ding et al., 2017), Nile tilapia (Oreochromis niloticus) (Ma et al., 2018), Zebrafish (Danio rerio) (Zhou et al., 2018), Pacific white shrimp (Litopenaeus vannamei) (Gao et al., 2019; Suo et al., 2017), Mangrove Red Snapper (Lutjanus argentimaculatus) (Reshma et al., 2018), Common carp (Cyprinus carpio L.) (Meng et al., 2018), and most of these studies focused on the factors that shape the

ACCEPTED MANUSCRIPT intestinal microbiota (e.g., diet, environmental and pathogenic stress, developmental stage). In the recent years, world aquaculture production is growing rapidly with the increased consumption. World human population is a pivotal driver of seafood demand and fishery development. The demand for good quality seafood is increasing. However, production from

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marine capture fisheries has been quite stable, most of the main fishing areas have reached

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the maximum ecosystem productivity (Garcia Serge and Rosenberg Andrew, 2010).

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Therefore, aquaculture is an opportunity to meet the gap between the supply and demand for aquatic food in most regions of the world. To achieve this aim, aquaculture faces significant

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challenges, of which the growth performance of aquatic animals is an important sector

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affecting output of aquatic products.

The Pacific white shrimp Litopenaeus vannamei (L. vannamei) is among the most

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widely cultivated shrimp in the world, especially in Asia, mainly due to its wide consumer

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demands, ease of cultivation, rapid growth rate, high economic value and export (Zhang et al., 2014). Bacterial populations in aquaculture pond ecosystems plays crucial roles in nutrient

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absorption, water quality control, pathogen defense, antibiotic resistance and host heath

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maintenance (Blancheton et al., 2013). Moreover, the intestinal microbiota serves as a virtual endocrine organ acting as a barrier to prevent pathogen invasion and influencing host metabolism and body composition (Clarke et al., 2014a; b). Although the intestinal microbiota of L. vannamei has been studied recent few years (Chen et al., 2017; Fan et al., 2019; Gainza et al., 2018; Hou et al., 2018a; Hou et al., 2018b; Huang et al., 2018; Wang et al., 2019), the relationship between the intestinal microbiota and the growth performance of the shrimp has seldom been mentioned.

ACCEPTED MANUSCRIPT The present study aims to investigate the shrimp intestinal microbiota of different growth performances using bacterial high-throughput sequencing. We observed that the shrimps in one pond were much smaller than the others in another pond at the same farming condition (the same diet, feeding time and batch of seedlings) on the shrimp farm, so we

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hypothesized that intestinal bacterial community structure and function in the shrimps can be

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attributed to shrimp intestinal microbiota. Moreover, the sediments related to shrimps of

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different growth performances were analyzed. The results provide a comparative reference for bacterial characteristics in the shrimp intestines, which could help the shrimp healthy

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farming in the future.

2. Materials and methods

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2.1 Sample collection

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Three parallel intestine samples (MIN1, MIN2, MIN3) of L. vannamei (normal growth) and 3 parallel pond sediment samples (MSN1, MSN2, MSN3) were collected at the same

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pond with the shrimp respectively. The other three parallel intestines samples (MIS1, MIS2,

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MIS3) of L. vannamei (slow growth) and 3 parallel pond sediment samples (MSS1, MSS2, MSS3) were also collected at the same pond with the shrimp. The studied shrimp pond water was directly transferred from the nearby sea. The water salinity was approximately 18‰, pH was 7.9 ± 0.3, the water temperature was 28.5 ± 0.3 °C, dissolved oxygen (DO) was 7.3 ± 0.5 mg/L when sampling. The water quality was monitored daily during the entire farming period. The concentrations of ammonia-nitrogen and nitrite were remained in a low level (ammonia-nitrogen was 0-0.27 mg/L, nitrite was 0-0.06 mg/L). The samples were all

ACCEPTED MANUSCRIPT collected in July 2018 from the same shrimp farm in Maoming, Guangdong, China in Figure 1 (N21.54, E111.44). The shrimp’s surface was sterilized with 75% ethanol. The intestine was aseptically dissected. Each intestines sample was mixed with 10 shrimp’s intestines. The weight of the normal growth shrimp was 5.32 ± 0.25 g, while the weight of the slow growth

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shrimp was 2.19 ± 0.34 g. The shrimps were cultured for 2 months. The intestines were put

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into a 1.5 mL sterile centrifuge tube, frozen in liquid nitrogen and immediately stored at

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-80 ℃. To reduce the spatial variability within the pond, the sediment samples (5 cm of the surface mud) were randomly collected from five sites in each pond. The sediment samples

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were stored at a 100 mL sterile centrifuge tube, frozen in liquid nitrogen and then stored at

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-80 ℃. 2.2 DNA extraction

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Genomic DNA from intestines and sediment was extracted by the E.Z.N.A.® soil DNA

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Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocol (Gao et al., 2017). The integrality of total DNA was confirmed for the subsequent analysis of

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microbial community by 1% agarose gel electrophoresis. The purity and concentrations of

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total DNA were determined with a NanoDropTM ND 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). 2.3 Bacterial 16S rRNA gene amplification To profile the diversity and structure of the microbial communities, V3-V4 region of the bacterial 16S rRNA gene was sequenced by Illumina MiSeq platform. The V3-V4 hypervariable region of 16S rRNA gene was amplified with the primer pair 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’)

ACCEPTED MANUSCRIPT (Hong et al., 2016). The barcode is a sequence with eight-nucleotide unique to each sample. The following PCR reactions were performed in triplicate: 4 μL of 5 × FastPfu Buffer, 2 μl dNTPs of 2.5mM dNTPs, 0.8 μl of each primer (5 μM), 0.4 μl FastPfu Polymerase, 0.2 μl BSA, 10 ng template DNA and ddH2O to a total volume of 20 μl.

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The PCR reactions were performed on the ABI GeneAmp®PCR System 9700 (Applied

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Biosystems, Foster City, CA, USA) included 95 ℃ for 3 min, followed by 29 cycles of 95 ℃

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for 30 s, 55 ℃ for 30 s, 72 ℃ for 45 s, and finally 72 °C for 10 min. All PCR products (3 μl) were detected by 2% agarose gels containing ethidium bromide, purified with the AxyPrep

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DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified with

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QuantiFluorTM-ST (Promega, USA). Purified PCR amplicons were pooled and paired-end sequenced using a 600-cycle kit on the Illumina MiSeq platform (Illumina, San Diego, CA,

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USA) according to standard protocols with the manufacturer’s instruction (Caporaso et al.,

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2012).

2.4 Bioinformatic and statistical analysis

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Before analysis, the raw pyrosequencing reads obtained from the sequencer were

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demultiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria. The reads were truncated at any site receiving an average quality score less than 20 over a 50 bp sliding window. The primers were exactly matched allowing 2 nucleotide mismatching. The reads containing ambiguous bases were removed. The sequences whose overlap longer than 10 bp were merged according to their overlap sequence. The operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/) and chimeric sequences were

ACCEPTED MANUSCRIPT identified and removed using UCHIME (Edgar et al., 2011). The taxonomy of each 16S rRNA gene sequence was analyzed by RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the Silva (SSU123) 16S rRNA database using confidence threshold of 70%, based on the Silva (SSU115) 16S rRNA database (Amato et al., 2013; Quast et al., 2013).

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Rarefaction curves were plotted for each sample to determine the abundance of

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communities and sequencing data of each sample (Amato et al., 2013). Alpha-diversity was

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measured using the level of OTUs, community richness parameters (Chao index and ACE index), community diversity parameters (Shannon index and Simpson index) and a

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sequencing depth index (Good’s coverage) which were belong to analyses were calculated

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using the mothur software (Schloss et al., 2011). Beta-diversity measurements, including microbiota trees, were calculated as described (Jiang et al., 2013). Principal coordinate

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the R package software.

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analyses (PCoA) based on OUT level were determined. Other analyses were visualized with

All statistical analyses were conducted with R package software. Differences between

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populations were analyzed using a one-way ANOVA. All reported values were the average of

3. Results

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triplicate results (mean ± SD), and a significance level of 5% was used in all analyses.

3.1 Overview of 16S rRNA gene sequencing and microbiota structures The quality and chimera filtration of the raw data produced totally 635773 high quality sequencing reads from 12 samples belonging to four groups, with an average of 52,981 reads. The average read numbers, coverage, and statistical estimates of richness and diversity

ACCEPTED MANUSCRIPT indexes from each group at a genetic distance of 3% were presented in Table 1. Finally, at a 97% sequence identity these high-quality sequences were clustered into 2384 OTUs in total. 3.2 Relationship of the microbial communities In order to study the relationship of microbial communities in shrimp intestines and

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related pond sediments, a Veen diagram was constructed to identify dominant OTUs

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presented in the four groups (Fig. 2). A total of 266 OTUs was shared among MIN, MIS,

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MSN and MSS (N=3), representing 11.16% of the total reads. 578 and 487 OTUs were shared by MIN & MSN and MIS & MSS, representing 24.24% and 20.43% of the total reads

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separately. 345 and 1024 OTUs were shared by MIN vs MIS and MSN vs MSS, respectively

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representing 14.47% and 42.95% of the total reads. 3.3 Alpha diversity of the bacterial communities

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The diversity and richness indices of all samples were calculated to illustrate the

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complexity of each sample (Table 1). The Shannon and Simpson indexes were used to quantify the diversity. The Shannon index ranged from to 3.8626 ± 0.3024 to 5.7398 ± 0.4117,

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while the Simpson index ranged from 0.0087 ± 0.0038 to 0.0608 ± 0.0381. The richness was

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calculated via Chao index and ACE index. Chao index ranged from 543.30 ± 25.85 to 1568.37 ± 242.37, while ACE index ranged from 559.09 ± 39.81 to 1504.14 ± 287.32. The richness and diversity of bacterial species in samples were ordered as follows: normal growth shrimp intestines and related sediment > slow growth shrimp intestines and related sediment (Fig. 3). The Good’s coverage of each sample to estimate the completeness of sequencing was more than 98.88% (from 0.988861 ± 0.000832 to 0.997814 ± 0.000340), which indicates that the sequences identified can represent most of the bacteria in each sample.

ACCEPTED MANUSCRIPT 3.4 Beta diversity of the microbial community Beta diversity analysis was to show the similarity and difference of microbial community in different samples. The hierarchical clustering tree of all samples was shown in Figure 4A. All samples of the same group tended to cluster together using binary_jaccard

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algorithm, indicating significant impact of the microbiome on shrimp growth. The similarity

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matrix of the samples was analyzed by PCoA with PC1 = 38.27% and PC2 = 27.49% of total

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variations (Fig. 4B). The samples in the same group were clustered closer than those intergroup. Moreover, two sediment groups (MSN vs MSS) tended to cluster closer compared

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with two intestine groups (MIN vs MIS), and the shrimp intestines and related sediment

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samples also tended to cluster closer (MIN vs MSN and MIS vs MSS). Analysis of similarities (Anosim) was performed to measure the effect of different growth performances

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on the microbial community. The P value indicated a significant difference among groups (P

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value < 0.01), while the R value represented a good separation among the groups (R values = 0.8704 > 0.75).

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3.5 Taxonomic composition of microbial community

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The microbiota composition in shrimp intestines and related sediment was compared between the normal and slow growth shrimps at phylum and genus levels (Figure 5). In the observed 53 phyla, only 5 phyla were identified at an abundance >1% in the four groups. As shown in Fig. 5A, the dominant phyla (relative abundance > 5% at least in one sample) in the four groups were Proteobacteria, Actinobacteria, Cyanobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Verrucomicrobia, and Saccharibacteria. The detail information of dominant phyla were Proteobacteria (MIN = 27.16 ± 6.21%, MIS = 44.80 ± 16.66%, MSN = 31.38 ± 7.24%

ACCEPTED MANUSCRIPT and MSS = 45.28 ± 11.39%, P = 0.2563), Actinobacteria (MIN = 28.19 ± 7.22%, MIS = 18.04 ± 2.08%, MSN = 7.75 ± 0.91% and MSS = 20.78 ± 3.75%, P = 0.0054**), Cyanobacteria (MIN = 16.46 ± 4.90%, MIS = 20.28 ± 10.69%, MSN = 7.95 ± 3.97% and MSS = 2.67 ± 1.32%, P = 0.0430*), Bacteroidetes (MIN = 3.31 ± 0.12%, MIS = 6.04 ±

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3.26%, MSN = 12.26 ± 3.65% and MSS = 14.37 ± 1.16%, P = 0.0017**), Chloroflexi (MIN

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= 2.38 ± 0.97%, MIS = 6.31 ± 3.44%, MSN = 15.66 ± 6.02% and MSS = 9.37 ± 4.29%, P =

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0.0709), Firmicutes (MIN = 3.08 ± 0.80%, MIS = 0.34 ± 0.27%, MSN = 6.87 ± 4.61% and MSS = 0.93 ± 0.12%, P = 0.0297*), Verrucomicrobia (MIN = 7.24 ± 6.96%, MIS = 0.41 ±

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0.19%, MSN = 0.69 ± 0.39% and MSS = 0.27 ± 0.15%, P = 0.3249), and Saccharibacteria

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(MIN = 5.26 ± 2.61%, MIS = 0.94 ± 0.51%, MSN = 0.11 ± 0.04% and MSS = 0.76 ± 0.42%, P = 0.0680). Moreover, distinct differences were observed in microbial composition among

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groups. Actinobacteria and Bacteroidetes exhibited statistically significant differences at

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0.001 < P ≤ 0.01, while Cyanobacteria and Firmicutes exhibited statistically significant differences at 0.01 < P ≤ 0.05 among the four groups (Fig. 6A). Additional information about

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the relative abundances at the phylum level among the four groups can been acquired in the

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Supplementary Materials S1.

Out of the observed 650 genera, 81 genera were identified P < 0.05 in the four groups. The dominant genera (relative abundance > 5% at least in one sample) were unclassified_f__Rhodobacteraceae, norank_c__Cyanobacteria, norank_f__Propionibacteriaceae, Ruegeria, Synechococcus, Vibrio, Robiginitalea, Donghicola, unclassified_c__Actinobacteria, norank_f__TM146, norank_f__Anaerolineaceae, norank_f__LD29 and norank_p__Saccharibacteria. Six of them

ACCEPTED MANUSCRIPT exhibited statistically significant differences among the four groups, norank_f__Propionibacteriaceae, Ruegeria, Robiginitalea, unclassified_c__Actinobacteria and norank_f__TM146 exhibited extremely statistically significant differences at 0.001 < P ≤ 0.01 while norank_c__Cyanobacteria exhibited statistically significant differences at 0.01 <

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P ≤ 0.05 among the four groups (Fig. 6B). Detailed additional information regarding the

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relative abundances at the genus level among the four groups can be found in Supplementary

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Materials S2. 3.6 Comparison of microbial community

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The relative abundances of bacterial taxa displayed statistically significant differences

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between two groups (MIN vs MIS, MSN vs MSS, MIN vs MSN and MIS vs MSS) at the phylum and genus levels (Figure 7 lists the first 15 abundance phyla. Figure 8 lists the first 20

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abundance genera.). On the phylum level, statistically significant different bacterial taxa

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between MIN and MIS were Saccharibacteria, Firmicutes, Planctomycetes, BRC1 and Gemmatimonadetes. Bacterial taxa between MSN and MSS were Actinobacteria,

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Acidobacteria, Latescibacteria, Spirochaetae and Parcubacteria. Bacterial taxa between MIN

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and MSN were Actinobacteria, Chloroflexi, Bacteroidetes, Saccharibacteria, Acidobacteria, Tenericutes, Spirochaetae, Latescibacteria and BRC1. Bacterial taxa between MIS and MSS were Cyanobacteria, Bacteroidetes, Firmicutes, Acidobacteria, Latescibacteria, Planctomycetes, Deinococcus-Thermus and Spirochaetae. These results showed that the intestinal microbiota differences at the phylum level mainly existed in the low abundance bacterial taxa and the related sediment microbiota were more similar between normal and slow growth performance, while the intestines and related sediment microbiota abundance

ACCEPTED MANUSCRIPT existed greater differences. On the genus level, statistically significant different microbiota between MIN and MIS were Ruegeria, unclassified_c__Actinobacteria, norank_f__TM146, norank_p__Saccharibacteria, norank_f__Propionibacteriaceae, norank_o__PeM15,

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Tropicimonas, Haloplasma. Microbiota between MSN and MSS were

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norank_f__Propionibacteriaceae, Robiginitalea, norank_f__Caldilineaceae, Tropicimonas,

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Ruegeria, Candidatus_Thiobios, norank_f__OM1_clade, and norank_p__Latescibacteria. Microbiota between MIN and MSN were unclassified_c__Actinobacteria, orank_f__TM146,

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Ruegeria, norank_p__Saccharibacteria, Mycobacterium, Vibrio, norank_c__KD4-96,

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norank_o__PeM15, Ilumatobacter, Robiginitalea and Haloplasma. Microbiota between MIS and MSS were norank_f__Propionibacteriaceae, Ruegeria, Ilumatobacter,

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norank_f__Unknown_Family_o__Gammaproteobacteria_Incertae_Sedis, Mycobacterium,

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Candidatus_Thiobios, norank_f__OM1_clade, norank_f__TM146, unclassified_c__Actinobacteria, and Filomicrobium. These findings indicated that the relative

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abundances of dominant genera were obviously different between two groups. Overall, the

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composition and abundance of dominant genera were distinctly different among MIN, MSN, MIS and MSS.

4. Discussion Intestinal microbiota plays a key role in the physiology and development of the host. Therefore, intestinal microbiota in L. vannamei with different growth performances was detected in the present study. High-throughput sequencing technology provides a tool to

ACCEPTED MANUSCRIPT visualize the microbiota (Kozich et al., 2013). Our findings showed statistically significant diverse microbiota compositions at the phylum and genus levels in the shrimp intestines with different growth performances. To our knowledge, this was the first microbiome analysis of L. vannamei intestinal microbiota with different growth performances. The results provide a

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comparative reference for bacterial characteristics in the shrimp intestines.

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In this study, Proteobacteria, Actinobacteria, Cyanobacteria, Bacteroidetes, Chloroflexi,

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Firmicutes, Verrucomicrobia and Saccharibacteria were been identified as the dominant phyla in shrimp intestines and related sediment with different growth performances. The result

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agreed well with our previous study (except Acidobacteria), which the groups of normal

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growth performance at the marine cultured environment in the present study were the same as those in our previous study (Fan et al., 2019). As the most abundant and efficient colonizers

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in shrimp intestines, the relative abundances of Proteobacteria are higher in the shrimp

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intestines and related sediment with slow growth performance. The previous studies have showed that Proteobacteria were dominant intestinal microbiota of shrimp (Gao et al., 2019;

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Su et al., 2018; Wang et al., 2018). Generally, the relative abundance of Proteobacteria as

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gram-negative bacteria may reflect the state of health in aquaculture (Blandford et al., 2018; Wang et al., 2018). It was proposed that an increased abundance of Proteobacteria was a potential risk of disease (Rizzatti et al., 2017; Shin et al., 2015). In the present study, Proteobacteria in intestines of slow growth shrimps (44.80 ± 16.66%) was more abundant than that of the normal growth shrimps (27.16 ± 6.21%). Actinobacteria, as a group of gram-positive bacteria, can be used for producing probiotics and antibiotics (Anandan et al., 2016; Barka et al., 2015; Das et al., 2008).

ACCEPTED MANUSCRIPT Additionally, Actinobacteria play important role in the maintenance of intestinal homeostasis (Binda et al., 2018). In our results, Actinobacteria in the intestines of the normal growth shrimps was more abundant than that of the slow growth shrimps, although no significant differences were detected between shrimp intestines with normal and slow growth

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performance. Interestingly, although the abundance of Actinobacteria in the sediment from

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the pond where the slow growth shrimps was greater than that in the sediment from the

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normal growth pond, it might be difficult for Actinobacteria to be colonized in the intestines of the slow growth shrimps in comparison with those of the normal growth shrimps, which

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indicated that the immunity of the normal growth shrimps is stronger than slow growth

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shrimps. Moreover, no significant differences were found between shrimp intestines with normal and slow growth performance in Cyanobacteria, Bacteroidetes, Chloroflexi,

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Verrucomicrobia, whereas the changes of samples in the same group are large.

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Saccharibacteria (former TM7), which was more abundant in the normal growth shrimp intestines, played a role in sugar metabolisms, particularly for sugars (Albertsen et al., 2013;

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Kindaichi et al., 2016). We can speculate that Saccharibacteria may enhance the carbohydrate

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absorption and further improve the growth rate of L. vannamei. However, little is known about the roles of Saccharibacteria in the shrimp intestines. The phyla Firmicutes, Planctomycetes, BRC1 and Gemmatimonadetes varied significantly between the intestines of normal and slow growth shrimps, although they were not dominant. Among them, Firmicutes, Planctomycetes and BRC1 are more abundant in the normal growth shrimp intestines, while Gemmatimonadetes were more abundant in the slow growth shrimp intestines. However, an attention was paid to two interesting phyla, Firmicutes

ACCEPTED MANUSCRIPT and Bacteroidetes. Normally, the intestinal microbial community has three major metabolic processes: fermentation, sulfate reduction and methanogenesis. Most species, belonging to the phyla Firmicutes and Bacteroidetes, were involved in fermentation (Gillilland et al., 2012). Therefore, intestinal microbiome provided nutrient sources for the host as well

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(Wexler, 2007). In general, the Bacteroidetes and Firmicutes are directly involved with

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obesity (Ley et al., 2006). It has been reported that Firmicutes, associated with the phylum

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Bacteroidetes, influence fatty acid absorption and lipid metabolism in zebrafish (Semova et al., 2012). An increasing ratio of Firmicutes to Bacteroidetes is regarded as obese. Conversely,

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a decreased Firmicutes/Bacteroidetes ratio was related to weight loss (Ley et al., 2006;

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Turnbaugh et al., 2006). Another interesting study on monozygotic and dizygotic twins showed a higher abundance of Bacteroidetes and an enrichment of genes related to

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carbohydrate metabolism in lean individual microbiomes, while Firmicutes dominated the

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obese microbiome and was enriched with genes related to nutrient transporters (Turnbaugh et al., 2008). These may indicate that Firmicutes increases energy harvest, while Bacteroidetes

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increases the energy metabolism. Our results showed that the Firmicutes/Bacteroidetes ratio

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in the normal growth shrimp intestines was 3.08/3.31 in comparison with 0.34/6.04 in the slow growth shrimp intestines, suggesting that the normal growth shrimps can absorb the nutrient better than the slow growth shrimps and then had a better growth performance. This may provide a valuable information for the shrimp even aquaculture farming. At a genus level, we observed 650 genera, six of the 13 dominant genera exhibited statistically significant differences among the four groups. Among them, we focused our interest in Ruegeria and Robiginitalea. Ruegeria tended to be colonized in the shrimp

ACCEPTED MANUSCRIPT intestines from the related sediment (Fig. 6B). Significant differences at 0.01 < P ≤ 0.05 occurred between MIN and MIS, MSN and MSS, and MIN and MSN, while significant differences at 0.001 < P ≤ 0.01 were observed between MIS and MSS, which suggest an easier enrichment of Ruegeria in the slow growth shrimp intestines than in the normal growth

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shrimp intestines. Ruegeria, with various metabolic capabilities and mainly isolated from

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many different marine habitats (Luo and Moran, 2014) , was first proposed by Uchino et al.

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as a member of the class Alphaproteobacteria (Uchino et al., 1998). Ruegeria, being gram-negative, was widely used as a probiotic and symbiotic with unicellular eukaryotic

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phytoplankton (Barreto-Curiel et al., 2018). Moreover, Ruegeria with an antagonistic effect

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against Vibrio anguillarum was verified to improve the survival of Atlantic cod larvae (Fjellheim et al., 2010). From this point, whether this genus can be used as a probiotic in

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aquatic animals should be identified in the future, and this is an interesting focus.

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Robiginitalea, also mainly isolated from marine environments, were contrary to Ruegeria that do not colonize well in the shrimp intestines. However, little was known about the function of

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further studies.

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Ruegeria and Robiginitalea to the growth of the shrimp, their function identifications need

5. Conclusions

The intestinal microbiota between shrimps differing growth performances in a marine farming environment were compared via the 16S rRNA gene sequencing. Actinobacteria and Saccharibacteria in the normal growth shrimp intestines were more abundant that those in the slow growth shrimp, which may be related with immunity and digestion. An increased

ACCEPTED MANUSCRIPT abundance of Proteobacteria in the slow growth shrimp intestines was a potential risk of disease. Since L. vannamei was cultured in the marine environment, some marine bacterial genera such as Ruegeria and Robiginitalea were observed. Furthermore, the Firmicutes to Bacteroidetes ratio reflects that the normal growth shrimp had a better growth performance.

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Overall, the microbiota analysis for L. vannamei intestines and related sediments was a first

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step to understand the intestinal health, immunity, growth performance and environmental

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influence. The findings can help understand the characteristics of the intestinal microbiota for

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the shrimp healthy farming.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China

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(31600322, 41706186), Guangdong Province Universities and Colleges Pearl River Scholar

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Funded Scheme (2018), the program of Joint Laboratory of Guangdong Province and Hong Kong Region (201704), and USDA (Hatch project HAW5032-R). We acknowledge Shanghai

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Majorbio Bio-pharm Technology Co., Ltd., for the free online use of Majorbio I-Sanger

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Cloud Platform (www.i-sanger.com) for data analysis. LF was a China Scholarship Council scholarship recipient.

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34428±928

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47912±6208

MSS

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±

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0.997814

±

0.000340 0.995310

±

0.000129 0.988954

±

0.001124

67727±2734

0.988861 0.000832

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Chao

572.50±25.58

575.76±28.50

559.09±39.81

543.30±25.85

1504.14

±

287.32 ±

1231.64

Shannon

1568.37 242.37

±

51.01

±

4.2669

Simpson ±

0.1507 3.8626 5.7398

±

59.89

±

4.7993 0.0562

0.0608

±

0.0087

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± ±

0.0038 ±

0.0277 0.0015

OTUs were defined at the 97% similarity level (Threshold is 0.03) and data were presented as the mean ± SD. M stands for Maoming, I stands for shrimp intestines, middle S stands for sediment, N stands for normal growth and S stands for slow growth.

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0.4117

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0.3024

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Fig. 2. Venn diagram for the comparison of the shrimp intestine and related sediment from different growth performance.

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Fig. 3. Richness and diversities of bacterial species in the four groups according to the Shannon (A), Simpson (B), Ace (C) and Chao (D) indexes of OTU level. * 0.01 < P ≤ 0.05, ** 0.001 < P ≤ 0.01 and *** P ≤ 0.001.

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Fig. 4. Hierarchical clustering tree (A) and principal co-ordinates analysis (PCoA) of the bacterial community (B) on the OUT level. The hierarchical clustering tree was calculated using the UPGMA (Unweighted Pair-group Method with Arithmetic Mean) method, and the relationship between samples was determined by Bray distance and the average clustering method.

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Fig. 5. Microbiota compositions of the groups MIN, MIS, MSN and MSS at phylum level (A) and genus level (B). There were 3 samples in each group and 10 shrimps per sample.

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Fig. 6. Comparison of bacterial abundances among the four groups (MIN, MIS, MSN and MSS) at the phylum level (A) and at genus level (B). * 0.01 < P ≤ 0.05, ** 0.001 < P ≤ 0.01 and *** P ≤ 0.001.

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Fig. 7. Comparison of microbial community between MIN and MIS (A); MSN and MSS (B); MIN and MSN (C) and between MIS and MSS (D) at the phylum level. * 0.01 < P ≤ 0.05, ** 0.001 < P ≤ 0.01 and *** P ≤ 0.001.

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Fig. 8. Comparison of microbial community between MIN and MIS (A); MSN and MSS (B); MIN and MSN (C) and between MIS and MSS (D) at the genus level. * 0.01 < P ≤ 0.05, ** 0.001 < P ≤ 0.01 and *** P ≤ 0.001.

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