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Submitted 15 June 2018
Accepted 25 September 2018
Published 30 October 2018
Corresponding author
Han Ming Gan,
han.gan@deakin.edu.au
Academic editor
Hauke Smidt
Additional Information and
Declarations can be found on
page 16
DOI 10.7717/peerj.5826
Copyright
2018 Md Zoqratt et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Microbiome analysis of Pacific white
shrimp gut and rearing water from
Malaysia and Vietnam: implications for
aquaculture research and management
Muhammad Zarul Hanifah Md Zoqratt1,2,*, Wilhelm Wei Han Eng1,2,
Binh Thanh Thai3, Christopher M. Austin1,2,4,5and Han Ming Gan1,2,4,5,*
1School of Science, Monash University Malaysia, Petaling Jaya, Selangor, Malaysia
2Genomics Facility, Tropical Medicine and Biology Platform, Monash University Malaysia, Petaling Jaya,
Selangor, Malaysia
3Fisheries and Technical, Economical College, Dinh Bang, Tu Son, Vietnam
4Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong,
Victoria, Australia
5Deakin Genomics Centre, Deakin University, Geelong, Victoria, Australia
*These authors contributed equally to this work.
ABSTRACT
Aquaculture production of the Pacific white shrimp is the largest in the world for
crustacean species. Crucial to the sustainable global production of this important
seafood species is a fundamental understanding of the shrimp gut microbiota and its
relationship to the microbial ecology of shrimp pond. This is especially true, given
the recently recognized role of beneficial microbes in promoting shrimp nutrient
intake and in conferring resistance against pathogens. Unfortunately, aquaculture-
related microbiome studies are scarce in Southeast Asia countries despite the severe
impact of early mortality syndrome outbreaks on shrimp production in the region.
In this study, we employed the 16S rRNA amplicon (V3–V4 region) sequencing and
amplicon sequence variants (ASV) method to investigate the microbial diversity of
shrimp guts and pond water samples collected from aquaculture farms located in
Malaysia and Vietnam. Substantial differences in the pond microbiota were observed
between countries with the presence and absence of several taxa extending to the family
level. Microbial diversity of the shrimp gut was found to be generally lower than that
of the pond environments with a few ubiquitous genera representing a majority of
the shrimp gut microbial diversity such as Vibrio and Photobacterium, indicating host-
specific selection of microbial species. Given the high sequence conservation of the 16S
rRNA gene, we assessed its veracity at distinguishing Vibrio species based on nucleotide
alignment against type strain reference sequences and demonstrated the utility of ASV
approach in uncovering a wider diversity of Vibrio species compared to the conventional
OTU clustering approach.
Subjects Aquaculture, Fisheries and Fish Science, Marine Biology, Microbiology
Keywords Metagenomics, Aquaculture, Litopenaeus vannamei, 16S ribosomal RNA amplicons
sequencing, Vibrio parahaemolyticus
How to cite this article Md Zoqratt et al. (2018), Microbiome analysis of Pacific white shrimp gut and rearing water from Malaysia and
Vietnam: implications for aquaculture research and management. PeerJ 6:e5826; DOI 10.7717/peerj.5826
INTRODUCTION
Litopenaeus vannamei (Boone, 1931), also known as the Pacific white shrimp or Whiteleg
shrimp, is a major aquaculture commodity with a production of 3.69 million tonnes valued
at 18 billion USD revenue (FAO, 2016). In recent years, significant outbreaks of acute
hepatopancreatic necrosis disease (AHPND), also known as early mortality syndrome
(EMS) have been reported in a number of white shrimp-producing countries. EMS was
first reported in China in 2009 and subsequently spread to Southeast Asian countries
including Vietnam, Malaysia, and Thailand (Foo et al., 2017;Kondo et al., 2014;Tran et
al., 2013). The causative agent of EMS has been reported to be Vibrio parahaemolyticus
strains harbouring a plasmid containing the pirA- and pirB- like genes encoding for toxins
capable of severely damaging the shrimp gut (Han et al., 2015;Lee et al., 2015). To date,
EMS has caused an estimated one billion USD of losses to the shrimp industry worldwide
(De Schryver, Defoirdt & Sorgeloos, 2014;Lee et al., 2015).
Monitoring and control of pond water quality play a crucial role in managing and
preventing disease outbreak in aquaculture. However, the current practice of water quality
monitoring usually focuses on the measurement of chemical and physical parameters such
as oxygen, pH, temperature, salinity, turbidity and nitrogen compounds. The importance
of microbial communities in influencing or responding to variation in aquaculture pond
water quality has only been recognized in recent years (Bentzon-Tilia, Sonnenschein &
Gram, 2016). This is especially relevant to managing the water quality of aquaculture
ponds and their cultured biomass because microbes carry out important biological services
in aquaculture environment including nutrient cycling, probiotic/pathogenic activity and
nutrient acquisition in addition to potentially acting as a rapid biological indicator of
critical chemical changes in the rearing water (Cardona et al., 2016;Cornejo-Granados
et al., 2017;Costa, Pérez & Kreft, 2006;Emerenciano, Gaxiola & Cuzon, 2013;Grotkjær et
al., 2016;Jinbo et al., 2017;Liu et al., 2015;Wright, Konwar & Hallam, 2012;Zeng et al.,
2017;Zhu et al., 2016). Microbes can also be used to improve the water quality of ponds.
For example, adding denitrifying bacteria to biofilters has been shown to reduce the
concentration of ammonia and its immediate derivatives, which are detrimental to shrimp
health (Saffran et al., 2001).
Recognizing the importance of microbial biomass and diversity on the health and
production of cultured invertebrates, several microbiome studies have analysed the gut
microbiome of wild-caught shrimps (Cornejo-Granados et al., 2017;Phayungsak et al.,
2018;Rungrassamee et al., 2016) as well as cultured shrimps under different abiotic and
biotic factors. However, most studies have been restricted to a specific country especially
China, focusing only on one or very few localized ponds (Cornejo-Granados et al., 2017;
Huang et al., 2016;Rungrassamee et al., 2016;Tang et al., 2014;Xiong et al., 2015;Zhang et
al., 2014;Zhu et al., 2016). A study in Thailand used denaturing gradient gel electrophoresis
profiling and barcoded pyrosequencing to demonstrate a greater survivability of Litopenaeus
vannamei and improved the resilience of its gut microbiome upon Vibrio harveyi exposure
relative to that of Penaeus monodon (Rungrassamee et al., 2016). Another more recent study
in Vietnam utilised a standard Illumina 16S rRNA gene amplicon sequencing method to
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 2/22
investigate the effect of EMS outbreak on the microbial interaction networks in shrimp guts
(Chen et al., 2017b). Thus, despite the emergence of Southeast Asia (SEA) as an aquaculture
hub, studies on any significant geographic scale are relatively scarce in this region. Further,
to our knowledge, all recent shrimp aquaculture microbiome studies still employ the
operational taxonomic units (OTU) clustering approach in marker-gene data analysis
despite recent calls for the replacement of this approach with exact sequence variants
which can resolve single-base differences among biological sequences in the sample thus
providing a more comprehensive and accurate view of microbial communities (Callahan,
McMurdie & Holmes, 2017;Eren et al., 2014;Utter, Mark Welch & Borisy, 2016).
To initiate a more broad-based investigation of shrimp microbiomes directly relevant
to the aquaculture industry in SEA, we performed Illumina 16S rRNA gene amplicon
sequencing of L. vannamei guts and rearing water from aquaculture farms located in two
SEA countries with contrasting climates at the time of sampling i.e., Malaysia (warm
and humid, 30 ◦C) and Vietnam (cool and dry, 20 ◦C). For the first time in a shrimp
aquaculture microbiome study, we applied the recently advocated ASV-method (Edgar,
2016) to: (1) compare shrimp intestinal microbial diversity and their pond environments;
(2) compare these microbial communities between Malaysia and Vietnam; and (3) assess
the performance of the 16S rRNA V3–V4 hypervariable region and clustering approaches
in capturing the genetic diversity of Vibrio.
METHODS
Sample collection
Sampling in Vietnam was performed at two separate shrimp farms in the Quang Ninh
province (approximately 20 km apart), while sampling in Malaysia was performed at
a large shrimp farm with multiple pond systems located in Perak state (Table 1). Two
mL of pond water was sampled from 2–4 distant location (corners) of each pond,
pelleted via centrifugation at 7,000 rpm for 10 min and resuspended in RNA/DNA shield
(ZymoResearch, Irvine, CA, USA). Shrimp intestinal samples were collected by dissecting
out the shrimp guts followed by homogenization in RNA/DNA shield (ZymoResearch,
Irvine, CA, USA). Sampling was performed with the permission and under the supervision
of the aquaculture manager from the respective farms. No field permit was required for
this study because samples were collected from private fields. As per the request of the
aquaculture manager, exact sampling location of some farms was not disclosed in this
study to protect the identity of the farm.
DNA extraction, amplification, purification and sequencing
Genomic DNA was extracted from the RNA/DNA shield lysate using DNA Clean &
ConcentratorTM-5 (ZymoResearch, Irvine, CA, USA) according to the manufacturer’s
instructions. The V3–V4 region of the 16S rRNA gene was amplified using forward primer
50–TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG
–30and reverse primer 50–GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG
GACTACHVGGGTATCTAATCC–30containing partial Illumina Nextera adapter. PCR
reaction (∼10 ng input DNA/ reaction) and barcode incorporation were performed as
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 3/22
Table 1 Summary of field collection and sampling design.
Farm # Pond
(Replicate
per pond)
# Shrimp
Sampled
Location Collection date Mean
temperature
(◦C)
Reported age
(days post
hatching)
Aquaculture Research
Station of Fisheries College
4 (2–3) 6 Quang Yen, Quang Ninh,
Vietnam
December 2015 20 97
Private 1 (4) 4 Quang Yen, Quang Ninh,
Vietnam
December 2015 20 115
Private 9 (2–3) 14 Sitiawan, Perak, Malaysia March 2016 30 65
previously described (Watts et al., 2017). Constructed libraries were quantified, normalized,
pooled, denatured and subsequently sequenced on the Illumina MiSeq (Illumina, San
Diego, CA, USA) located at Monash University Malaysia Genomics Facility using a
2×250 bp run configuration.
Sequence data analysis and observation table construction
Primer sequences corresponding to the 16S rRNA gene were removed from the raw
paired-end reads using Cutadapt (Martin, 2011). Trimmed forward and reverse reads
were overlapped with fastq_mergepairs followed by quality and length filtering with
fastq_filter (maximum expected error =0.5; min length =250 bp) as implemented in
USearch10 (Edgar, 2010). Sequence dereplication and denoising was done using uNoise3
to generate amplicon sequence variants (ASVs) (Edgar, 2016). Aside from ‘‘-minuniquesize
200 parameter during sequence dereplication, the processes of the pipeline were done using
default parameters. Taxonomic assignment and observation table construction rarefied at
8,000 reads were performed in RDP classifier 2.12 and QIIME 1.9.1 (Caporaso et al., 2010;
Cole et al., 2009). ASVs that failed RDP taxonomic assignment were re-classified using
SINA 1.3.1 against SILVA SSU Ref database (release 132) with default parameters (Data
S3 and Data S4) (Pruesse, Peplies & Glöckner, 2012;Pruesse et al., 2007). ASVs with lower
than 0.01% fraction of the total normalized observation and/or identified as chloroplast
were not included in subsequent analyses. Normalization of sequencing depth per sample
(8,000 reads/sample), rarefaction curves construction (10 replicates/depth) as well as alpha
diversity estimation (Simpson’s evenness and Shannon diversity indices), were performed
using the ‘‘core_diversity.py’’ python script in QIIME 1.9.1. Core genera of the shrimp gut
microbiome were investigated and defined as genera with at least 0.1% relative abundance
per sample in more than 50% shrimp samples. The prevalence and relative abundance
of the shrimp gut core genera were visualised using the R ggplot2 package (Wickham &
Wickham, 2007).
Principal coordinates analysis and relative differential abundance
analysis
The proportion of sequences assigned to the lowest possible taxonomic level was calculated
based on the rarefied observation table and the implemented taxonomic assignment
method mentioned above. Principal coordinates analysis (PCoA) was also constructed
based on weighted Unifrac and unweighted Unifrac using ordination method in R phyloseq
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 4/22
package (Lozupone & Knight, 2005;McMurdie & Holmes, 2013). The resulting PCoA plots
were then visualized using R ggplot2 package (Wickham & Wickham, 2007). Strength and
significance of grouping were calculated using the compare_categories.py python script
in QIIME which implements ANOSIM analysis using the default 999 permutations.
Relative differential abundance test was also conducted at phylum and family levels using
Tukey-Kramer post-hoc in conjunction with analysis of variance (ANOVA) statistical test
in STAMP (Parks et al., 2014). Multiple hypothesis testing was done for the four generic
groups namely Malaysian Farm, Malaysian Shrimp, Vietnamese Farm and Vietnamese
Shrimp. A significant differential abundance of phylum distribution was defined as
Benjamini–Hochberg-corrected probability p-value of ≤0.01 and was observed only for
the top 10 most abundant phyla. A significant differential abundance of family distribution
was defined as Benjamini–Hochberg corrected p-value of ≤0.01 and eta squared ≥0.3.
Comparison of Vibrio diversity using different clustering methods
Vibrio diversity was compared using different methods of 16S marker-gene data analysis,
namely conventional operational taxonomic unit (OTU) clustering and amplicon sequence
variants (ASV), using UParse and uNoise3 respectively (Edgar, 2013;Edgar, 2016). Except
for -minuniquesize of 2 during sequence dereplication, both pipelines were conducted
using default parameters. ASVs and OTUs assigned to the genus Vibrio with at least
cumulative read abundance of more than 200 were retained for blastN similarity search
(E-value < 1e−100) against 16S rRNA sequences of Vibrio type strain curated in EzBioCloud
(as of 18th May 2018) (Yoon et al., 2017).
RESULTS
Shrimp intestinal and pond microbial communities are distinct
A total of 2,731,818 successfully merged reads were generated in this study with 2,144,192
reads (median of 32,648 reads/sample; min =9,948; max =66,590) confidently mapped to
the ASVs. 92.12% and 7.77% of the mapped reads correspond to RDP-classified and SINA-
classified ASVs respectively, while the remaining mapped reads belong to ASVs without
confident taxonomic assignment at the kingdom rank. A majority of reads recovered from
shrimp intestine and ponds were assigned to members from the phyla Proteobacteria,
Actinobacteria, Bacteroidetes and Fusobacteria (Fig. 1). Significant differences in the
relative abundance of bacteria phyla were observed among samples isolated from ponds
and shrimp guts. Reads mapping to Actinobacteria and Bacteroidetes are more abundant
in ponds (p<0.01 and p<0.001, respectively), while shrimp guts have a significantly
higher relative abundance of Proteobacteria (p<0.001). At a finer level, Malaysian shrimp
intestinal microbiome contains more reads mapping to the phylum Fusobacteria (p<0.01).
Notable, this phylum is also near absent in three out of four Malaysian shrimps noted to
be unhealthy based on morphological observation by the aquaculture manager (Fig. 1).
Rarefaction curves based on alpha diversity metrics, number of observed ASVs and
PD (Phylogenetic diversity) whole tree, indicated that 8,000 sequences per sample are
sufficient for capturing the alpha diversity of microbial communities in both shrimp guts
and ponds. Inverse Simpson’s and Shannon’s indices showed higher species richness and
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 5/22
Figure 1 Distribution of abundant phyla in all samples. Significantly abundant phyla are annotated
at the legends. Phyla abundance of pond water replicates were collapsed according to the location of
sampling (see Table S1). Sample type (MF, Malaysian rearing water; VF, Vietnamese rearing water; MS,
Malaysian shrimp; VS, Vietnamese shrimp) with significantly higher phylum abundance than that of
another group (Superscript) were shown in bracket next to their associated phylum legend. For example,
Actinobacteria (MF*; VFVS, MS) indicates that this phylum is significantly more abundant in Malaysian
rearing water samples compared to all three other groups and that it is more abundant in Vietnamese
rearing water samples compared to shrimp samples from both countries. Hash and asterisk signs next to
y-axis labels indicate mussel-infested and suspected diseased samples, respectively.
Full-size DOI: 10.7717/peerj.5826/fig-1
evenness in the pond microbiome compared to that of shrimp gut (Figs. 2A and 2B). Beta
diversity analyses based on both weighted and unweighted UniFrac indicated that shrimp
gut and pond microbial communities from the same sampling site are significantly different
(Vietnam: R>0.67, p-value < 0.001; Malaysia: R>0.89, p-value < 0.001) (Table S2). An
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 6/22
Figure 2 Rarefaction curves and alpha diversity plots of each sample group. (A) Rarefaction curve
constructed based on observed ASVs. (B) Rarefaction curve constructed based on phylogenetic distance
(PD_whole_tree). (C) Shannon’s evenness index box plot. (D) Shannon index box plot.
Full-size DOI: 10.7717/peerj.5826/fig-2
even stronger separation was also observed among pond samples from different sampling
sites/ countries (Fig. 3,Table S2). Furthermore, samples from one of the Malaysian ponds
(MF_Pond11) noted by the shrimp farmer to be infested by mussels (Table S1) was distinct
from other Malaysian pond sample samples (Fig. 3). It is worth noting that rearing water
samples collected from different parts of the same pond have minimal spatial variation
in microbial composition as evidenced by the general tight clustering of pond replicates,
suggesting homogenous microbial community in the rearing water and indicating that
the sampling protocols are efficient for capturing pond diversity. Although the separation
between Malaysian and Vietnamese shrimp gut samples was less obvious in both PCoA
plots with occasional overlap, their microbial community structure appears to differ
moderately (R<0.67) with good statistical support (p-value < 0.001) based on ANOSIM
analysis (Table S2).
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 7/22
Figure 3 Principal component analysis of (A) weighted Unifrac and (B) unweighted Unifrac distances.
Red circular outline shows ‘Malaysian pond’ cluster, yellow circular outline shows ‘Vietnamese pond’ clus-
ter, blue circular outline marks ‘Malaysian shrimp’ cluster, and green circular outline represents ‘Viet-
namese shrimp sample. The sample points were coloured according to ‘‘Country_Source_Location’’ in-
formation (Table S1). Country and source information of each points were abbreviated; for example,
Malaysian farm sample from Pond 1 was labelled as MF_Pond1.
Full-size DOI: 10.7717/peerj.5826/fig-3
Comparison of relative abundance at the microbial family level
reveals fine-level microbiota dynamics
Given that more than 70% and 85% of the reads derived from pond and shrimp intestinal
samples, respectively, could be assigned to the family level (Data S3 and S4), we defined
the core and unique microbiomes among sample groups at this taxonomic level and
used STAMP to identify statistically significant differences in the relative abundance of
microbial families. A total of six microbial families (Alcaligenaceae, Flavobacteriaceae,
Microbacteriaceae, Acidimicrobiaceae and Rhodobacteraceae) are shared across shrimp
and rearing water samples (Fig. 4A and Data S5) with seven and 10 microbial families
uniquely present in Malaysian and Vietnamese rearing water, respectively, corroborating
with their higher alpha diversity (Figs. 2C and 2D). In addition, four microbial families are
uniquely shared by the Malaysian and Vietnamese rearing water samples, suggesting their
common affiliation with shrimp rearing water. Certain microbial families are significantly
more abundant in samples of the same isolation source, regardless of the country of
origin. For example, Microbacteriaceae and Flavobacteriaceae are more abundant in
pond water, while Vibrionaceae is highly enriched in the shrimp gut (Fig. 4B). On the
other hand, Rhodobacteraceae is more abundant in Vietnamese samples (shrimp gut and
rearing water) while Cyanobacteria-Family II is more abundant in Malaysian samples,
suggesting possible affiliation to regional climate and/or pond environment. In addition,
four microbial families (Acidomicrobiaceae, Actinomarinaceae, Comamonadaceae, and
Fusobacteriaceae) showed significantly higher abundance only in a specific country and
isolation source (Fig. 4B).
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 8/22
Figure 4 Microbial community dynamics at the family level. (A) Venn diagram illustrating the num-
ber of unique and overlapping microbial families among shrimp guts and rearing water. To be considered
as present, a microbial family must be detected in at least 90% of the samples from the same group. (B)
Tukey post-hoc pairwise comparison in conjunction with analysis of variance (ANOVA) between ‘‘Coun-
try_Source’’ groups of nine significant microbial families. Mean proportion values of families that are sig-
nificantly different are shown in the bar plots on the left section, while the differences in the pairwise com-
parison mean proportion with 95% confidence interval are shown on the right section.
Full-size DOI: 10.7717/peerj.5826/fig-4
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 9/22
Figure 5 Abundance and prevalence of core genera in shrimp guts. (A) Normalized read counts of the
selected genera among Malaysian and Vietnamese shrimp gut samples (B) Percentage of shrimp gut sam-
ples harbouring the core genera
Full-size DOI: 10.7717/peerj.5826/fig-5
High prevalence and relative abundance of ASVs belonging to the
genera Vibrio in shrimp gut
16S rRNA reads corresponding to 11 bacterial genera belonging to five phyla
(Actinobacteria, Chloroflexi, Cyanobacteria, Planctomycetes and Proteobacteria) were
detected in more than 50% of the shrimp gut samples (Fig. 5) with Vibrio being the
only genus present in all shrimp gut samples with relatively similar relative abundance
across samples(mean/median relative abundance per sample =33.7%/ 27.7%). On the
contrary, Photobacterium, a genus related to Vibrio at the family level (Vibrionaceae)
was detected in all but two Vietnamese shrimps (mean/median relative abundance per
sample =11.6%/ 6.5%). The prevalence of the core genera was independently correlated
with relative abundance e.g., the higher the prevalence of a core genus, the more likely
it is to have a higher relative abundance. Rhodopirellua and Gimesia all belonging to the
phylum Planctomycetes are more prevalent and abundant in Vietnamese shrimps than in
the Malaysian shrimps, consistent with statistical analysis showing a significantly higher
abundance of Planctomycetes in Vietnamese shrimps compared to Malaysian shrimps
(Fig. 1).
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 10/22
Substantial underestimation of Vibrio diversity using the OTU
clustering approach
The high cumulative abundance of reads assigned to the genus Vibrio in shrimp guts
indicates that some members of this genus are endogenous to the shrimp gut microbiota.
UPARSE using the default 97% sequence similarity cut-off setting identified two abundant
OTUs assigned to Vibrio. This contrasts greatly with the ASV approach which identified
substantially more biological sequences classified as Vibrio (Fig. 6,Data S3 and S4). A
majority of the constructed ASVs do not have an exact sequence match to the constructed
OTUs and more importantly, some could be assigned to a single Vibrio species (ASV22,
Vibrio jasicida TCFB 0772T; ASV19, Vibrio neocaledonicus NC470T). OTU2 and OTU10 are
identical in both sequence length and identity to ASV2 and ASV14, respectively. Similarity
search of OTU2/ASV2 revealed an exact sequence match to V. rotiferianus LMG21460Tand
V. campbellii CAIM519T, indicating a limitation to the use of V3–V4 hypervariable region
in delimiting some Vibrio species (Fig. 6). The high ratios of ASVs-to-OTUs observed for
OTU2 and OTU10 strongly suggests that imposing a fixed dissimilarity threshold using
conventional OTU clustering underestimates the true microbial diversity for Vibrio species
in this study and that resolving amplicon sequence variants (ASV) from amplicons data
which is sensitive down to single-nucleotide differences, through read de-replication and
error correction, substantially increased the number of observed Vibrio species from the
identical dataset. Unlike the ASVs associated with OTU2, nearly all ASVs associated with
OTU10 have at least two mismatches to known Vibrio species, indicating the presence of
unculturable or yet-to-be-cultured Vibrio strains in the shrimp gut. Of even more interest,
the ASV approach revealed the presence of V. parahaemolyticus (ASV235 in Fig. 6) that
was missed by the OTU clustering approach presumably due to its overall low relative
abundance across samples (Table S5).
DISCUSSION
Despite the immense scale of shrimp aquaculture in South East Asia and the major impacts
of aquaculture disease outbreaks in tropical regions, we are only starting to understand the
microbial composition of the shrimp guts and their relationship to rearing water in the
region (Leung & Bates, 2013). Most shrimp-related microbiome studies have been limited
to a few farms in a particular country; most of which have been conducted in countries
outside of SEA such as China and Mexico. Litopenaeus vannamei microbiome studies
have so far investigated the microbial composition of wild-type shrimps serving as an
important baseline for future comparative studies (Cornejo-Granados et al., 2017) as well
as the impacts of disease exposure (Chen et al., 2017b;Cornejo-Granados et al., 2017;Jinbo
et al., 2017;Rungrassamee et al., 2016;Xiong et al., 2015;Zhu et al., 2016), developmental
stages (Huang et al., 2016), nutrition (Zhang et al., 2014) and temperature (Tang et al.,
2014) on shrimp intestinal microbiome. We have contributed new findings to the growing
literature by providing the first data on gut and pond water microbiome of Malaysian
cultured shrimps. Furthermore, we compared bacterial communities of shrimp guts and
pond water from multiple aquaculture farms in two distinct climatic regions (Malaysia
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 11/22
Figure 6 Cumulative normalized read counts of OTUs/ASVs classified as Vibrio and similarity table
against selected type-strain Vibrio sequences. Empty and filled bars indicate shrimp gut and rearing wa-
ter samples, respectively. Different Vibrio groups consisting of one OTU and its associated ASVs were sep-
arated by grey horizontal lines.
Full-size DOI: 10.7717/peerj.5826/fig-6
and north Vietnam) while standardizing DNA extraction and sequencing protocols.
This provides a new perspective on our current understanding on the range of normal
microbial composition of a healthy shrimp gut microbiome community. Our principal
findings provide evidence supporting microbiota plasticity in shrimp ponds, but in
contradistinction, find a much more limited diversity of the adult shrimp intestinal
microbiota.
Comparison of the pond water microbiome between the Malaysian and Vietnamese
samples reveals significant dissimilarities at the phylum level as shown in Fig. 1. Although
Planctomycetes was identified as a significant phylum in this study, it was not commonly
found in other metagenomic studies of similar environments (Li et al., 2016;Xiong et al.,
2015;Zeng et al., 2017) (Table S4). The only sample site with near-zero relative abundance
of Plantomycetes was a mussel-infested Malaysian aquaculture pond (Pond11 in Fig. 1).
Mussels are known ecosystem engineers that can substantially modify their habitats through
biological processes such as sediment filtration and biodeposition of fecal matters (Bril et
al., 2014). The presence of mussels has been reported to cause to a 4-fold decrease in the
relative abundance of Plantomycetes in a freshwater system in Mississippi, USA (Black,
Chimenti & Just, 2017).
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 12/22
Although Flavobacteriaceae and Microbacteriaceae are prevalent in the rearing water
and shrimp gut samples, they exhibited significantly higher relative abundance in the
rearing water samples. Albeit initially associated with a fairly general ecological function
e.g., simple mineralization-based commensalism (Kirchman, 2002), emerging evidence
suggests that some members within the marine Flavobactericeae clade are algal-associated
species that exhibit growth promoting and inhibiting effects to its host and other algal
species, respectively (Bowman, 2006). Genera such as Cellulophaga, Psychroserpens and
Formoasa have been previously reported to produce toxic secondary metabolites against
dinoflagellates, commonly associated with algal bloom (Adachi et al., 2002;Egan et al.,
2000). Thus, the significant abundance of Flavobacteriacea in both rearing water could be
linked to the natural occurrence of algal and diatom species in the rearing water some of
which were co-amplified by the V3–V4 primers in this study (Data S3 and Table S3). On the
contrary, most described members from the family Microbacteriaceae were not associated
with marine environmental and were typically isolated from the terrestrial environment
(Evtushenko & Takeuchi, 2017). High abundance of Microbacteriaceae (unclassified at the
genus level) in shrimp rearing water particularly during the post-larvae stage has been
previously observed in a commercial marine shrimp hatchery in Hainan, China (Zeng et
al., 2017) which was suggested to be a temporal-specific bacterial family caused by changes
in the shrimp diet during different growth stages. On the contrary, four microbial families
namely, Cryomorphaceae, Rhodospirillaceae, Bacteriovoracaceae and Saprospiraceae, are
exclusively found across both Malaysian and Vietnamese shrimp rearing water samples,
indicating their specific adaptation to shrimp rearing water or more generally the marine
aquatic environment. For example, members of the family Rhodospirillaceae are purple
non-sulfur and mostly nitrogen-fixing photosynthetic bacteria. Their absence in the shrimp
gut is consistent with their strict requirement for light to grow and proliferate which is not
sufficiently present in the shrimp gut environment.
Despite the conspicuous difference in the shrimp pond microbiota between two
countries, the shrimp intestinal microbiota are more similar to each other which is
presumably due to host selection for microbial strains that adapt to or exploit the shrimp
gut environment as corroborated by their lower alpha diversity indices compared to that
of rearing water samples (Xiong et al., 2017). However, the presence of several microbial
families in both shrimp and rearing water samples indicates that the shrimp gut microbiota
maybe significantly affected by the microbial communities present in their aquatic
environment e.g., rearing water and pond sediments (Chen et al., 2017a;Cornejo-Granados
et al., 2017) as opposed to being maternally influenced as observed in some mammals
and other animals with forms of parental care (Jakobsson et al., 2014;Kohl & Dearing,
2012;Zhang et al., 2014). Moulting e.g., shedding of shrimp ectodermal gut tissue also
provides a new opportunity for shrimp stomach and guts to be colonised by the bacterial
community of the pond (Moss, LeaMaster & Sweeney, 2000). Furthermore, crustaceans,
including L. vannamei, also consume their exuvia, which provides another opportunity
for microbial recolonization of the shrimp guts and also the transmission of intestinal
microbiome across shrimps throughout their developmental stages (Martínez-Córdova &
Peña Messina, 2005).
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 13/22
Although the micro-clustering of shrimp gut samples based on country and/or farm
of origin may be associated with the difference in their respective growth environment,
variation in developmental stage may also contribute to the observed clustering as the
shrimps in this study were collected at different adult growth stages (Table 1). The
abundance of members from the family Fusobacteriaceae is highly dependent on the
shrimp developmental stage with a near-zero abundance in young shrimp larvae gut and
subsequently making up a substantial portion of the microbiome in the adult stage (75-day
post-hatching) (Chen et al., 2017b;Zeng et al., 2017). Intriguingly, although most of the
Vietnamaese shrimps were 97-day post-hatching during sampling, the low abundance and
prevalence of Fusobactericeae in the their guts indicates microbiome resemblance to that of
younger shrimps (Zeng et al., 2017). In contrast, Fusobacteriaceae is prevalent in Malaysian
shrimps that are relatively young e.g., 65-day post- hatching. However, it is worth noting
that Sitiawan, the closest city to where the Malaysian farm is located, is warm (30 oC)
throughout the year. Such a climate may support faster shrimp growth thus enabling them
to reach adulthood earlier (Kumlu, Türkmen & Kumlu, 2010;Wyban, Walsh & Godin,
1995). Members from the family Fusobacteriaceae are microaerotolerant to obligate
anaerobic Gram-negative rods bacteria that derive energy through the fermentation of a
variety of carbohydrates, amino acids and peptides. Such a metabolic profile is consistent
with the higher prevalence and abundance of Fusobacteriaceae in mature shrimp intestinal
systems that typically exhibit a better digestive ability (Parte et al., 2011;Schock et al., 2013).
Unfortunately, despite exhibiting a wide ecological diversity as evidenced by their diverse
isolation source, the symbiotic relationship of Fusobacteriaceae towards its host has yet
been properly demonstrated (Nelson, Rogers & Brown, 2013). Future work consisting of
metatranscriptome and metagenome sequencing of the shrimp gut microbiota will be
necessary to shed light on the role of Fusobactericeae in the shrimp gut.
Vibrio and Photobacterium belonging to the Vibrionaceae family are both abundant
and prevalent in nearly all of the shrimp samples, an observation that is consistent with
previous reports (Cornejo-Granados et al., 2017;Rungrassamee et al., 2016;Xiong et al.,
2017). In contrast, Zeng et al. (2017) did not identify any Vibrio-specific OTUs in their
sampling. Such anomalies are unlikely to be biological but rather due to technical and
analytical differences, such as the choice of the 16S rRNA gene region sequenced (V4- vs.
V3–V4-hypervariable region) and bioinformatic analysis settings. High abundance and
prevalence of Vibrio and Photobacterium genera in shrimp guts suggest that they are more
likely to be endogenous rather than pathogenic strains (Kriem et al., 2015). However, this
also reflects the persistence and adaptation of members from these genera to the shrimp
gut environments and may potentially explain the susceptibility of shrimps to non-native
pathogenic Vibrio and Photobacterium strains (Kondo et al., 2014;Wang & Chen, 2006).
The use of ASVs reveals a wider diversity of Vibrio species, suggesting that previous
shrimp microbiome analyses that employed the common 97% similarity cut-off for
clustering will risk masking the true Vibrio diversity in the shrimp gut (Callahan, McMurdie
& Holmes, 2017;Chen et al., 2017b). Fortuitously, despite the observed low resolution of
the V3–V4 hypervariable region for Vibrio species, this region appears to be distinct in V.
parahaemolyticus, which exhibits at least two diagnostic nucleotides that are absent from
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 14/22
all known type strains of Vibrio species (Fig. 6). The lack of an OTU with exact match
to ASV235 suggests that analysis using the OTU clustering approach will fail to report
the presence of V. parahaemolyticus and/or undescribed Vibrio strains sharing the same
16S rRNA gene sequence with V. parahaemolyticus if they are present at low abundance
in the dataset.This can have critical implications for aquaculture microbial management
especially in the early detection of V. parahaemolyticus infection. Since shrimp gut can
harbour both pathogenic and native Vibrio species, complementing 16S rRNA-based
amplicon sequencing with an alternative genetic marker such as pyrH may enable a more
accurate quantification of Vibrio diversity and abundance in aquaculture environment
(Tall et al., 2013;Thompson et al., 2005). Given the high diversity of shrimp gut-associated
Vibrio as revealed for the first time by the ASV approach, shallow shotgun metagenome
sequencing will also be instructive to obtain species/strain-level taxonomic resolution of
the abundant endogenous microbes in shrimp guts particularly those belonging to the
genera Vibrio and Photobacterium.
Shrimp gut microbiomes vary due to biological differences (shrimp strains), differences
in environmental or farming practice (temperature, diet, probiotic, wild capture) or even
biases from different laboratory procedures such as the sequencing platform and the
different partial 16S sequence target (Cornejo-Granados et al., 2017;Tremblay et al., 2015).
Considering the crucial functions undertaken by microbial communities and the potential
use of the pond microbiome for pond health surveillance, investment in measuring a
wide variety of chemical and physical parameters would allow us to better correlate the
relationship between microbiomes and rearing water quality and therefore improving our
understanding of aquaculture microbiomes.
CONCLUSIONS
Using a standardized Illumina 16S rRNA amplicons sequencing protocol, we report for the
first time, amplicon sequence variants (ASV)-based analysis of aquaculture rearing water
and shrimp gut microbiota from two South East Asia countries with different climates.
Despite substantial difference in the microbial composition of shrimp rearing water
between farms in Malaysia and Vietnam, adult shrimp guts are more similar and exhibit a
genus level core microbiome with the genus Vibrio being the most prevalent and abundant
group. In addition, compared to OTU clustering approach, the ASV method improved
the identification of closely related and/or rare Vibrio species, which is of relevance to the
shrimp aquaculture industry. The high abundance of Vibrio in shrimp gut also suggests
that some Vibrio species are endogenous and non-virulent to shrimps with functional and
ecological roles that remain to be elucidated in the future.
ACKNOWLEDGEMENTS
We thank the Monash University Malaysia Genomics Facility for the provision of
computational resources. We are also extremely grateful to the aquaculture managers
for providing access to their farms and sharing information.
Md Zoqratt et al. (2018), PeerJ , DOI 10.7717/peerj.5826 15/22
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by the Tropical and Medicine Biology Platform, Monash
University. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Tropical and Medicine Biology Platform, Monash University.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Muhammad Zarul Hanifah Md Zoqratt performed the experiments, analyzed the data,
prepared figures and/or tables, authored or reviewed drafts of the paper, approved the
final draft.
•Wilhelm Wei Han Eng performed the experiments, approved the final draft.
•Binh Thanh Thai conceived and designed the experiments, contributed reagents/mate-
rials/analysis tools, approved the final draft.
•Christopher M. Austin conceived and designed the experiments, contributed
reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the
final draft.
•Han Ming Gan conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, approved the final draft.
DNA Deposition
The following information was supplied regarding the deposition of DNA sequences:
All FastQ raw data may be accessed through SRA accession number SRP126985 or NCBI
BioProject PRJNA422950.
Data Availability
The following information was supplied regarding data availability:
FASTA files for OTU and ASV in addition to their taxonomic assignments are available
in the Supplemental File.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.5826#supplemental-information.
Md Zoqratt et al. (2018), PeerJ, DOI 10.7717/peerj.5826 16/22
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