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Core Gut Microbiota of Shrimp Function as a Regulator to
Maintain Immune Homeostasis in Response to WSSV Infection
Siyuan Zhang,
a
Xumei Sun
a
a
School of Marine Science, Ningbo University, Ningbo, People’s Republic of China
ABSTRACT The gut microbiota is an integral part of the host and has a functional
potential in host physiology. Numerous scientific efforts have opened new horizons in
gut microbiota research and enhanced the understanding of host-microbe interactions
in vertebrates. However, evidence on the association between the gut microbiota and
immunity in invertebrates, especially in shrimp, which is an important aquatic animal, is
limited. Herein, we conducted a comprehensive analysis based on 16S rRNA gene
sequencingandliquidchromatography-coupledmassspectrometry(LC-MS)toinvesti-
gate the correlation between them. Comparing the gut microbiota among the four dif-
ferent species of shrimp, we found huge variations and determined a core gut micro-
biota composed of 55 microbes. The environmental challenge of white spot syndrome
virus (WSSV) infection led to changes in core microbial structures, but the alteration of
coremicrobiotaamongdifferentshrimpfollowedthesametrendandshowedimmune-
related function in the prediction of its metabolic potential. In metabolomic analysis,
nine significantly upregulated metabolites found after viral infection indicated that they
have antiviral potential. Moreover, we found a tight correlation between them and
almost half of the core microbiota. These data demonstrate that these metabolites are
responsible for maintaining the immune homeostasis of the host and prove the function
of the gut microbiota and the related metabolome in antiviral immunity of shrimp.
IMPORTANCE Abundant gut microorganisms constitute a complex microecosystem
with the intestinal environment of the host, which plays a critical role in the adjust-
ment of various physiological states of the organism. Sequencing and mass spec-
trometry data collected from intestinal samples of shrimp after virus infection helped
to investigate the special function of the microecosystem and suggested that the
gut microbiota has a functional potential in maintaining immune homeostasis of the
host under environmental challenge.
KEYWORDS gut microbiota, metabolome, immune response, WSSV, shrimp
The gut microbiota is a complex microbial ecosystem with important roles in health
and development of organisms (1–3). Some deterministic and stochastic processes
are thought to shape the gut microbiota. These processes are generally driven by envi-
ronmental and biological factors. Factors such as host immune system, pH in the gut,
and dietary composition are considered to be the dominant factors in shaping the gut
microbiota, processes also termed environmental selection (4, 5). Biological factors, for
example, interspecies interactions (competitive, mutualistic, and some synergistic
interactions) may further affect the composition of the microbiota (6–8). Under the syn-
ergistic effect of these processes, the gut microbiota in different habitats can be di-
vided into core microbes and habitat-specific microbes (9–11). The gut microbiota pro-
foundly regulates homeostasis mechanisms by assisting the establishment of the
intestinal epithelial barrier and maintenance of immune homeostasis in hosts (12, 13).
In the human body, previous studies indicated intense communication between the
gut microbiota and intestinal epithelial cells and immune cells shaped specific immune
Editor Daniel R. Perez, University of Georgia
Copyright © 2022 Zhang and Sun. This is an
open-access article distributed under the terms
of the Creative Commons Attribution 4.0
International license.
Address correspondence to Xumei Sun,
sunxumei@nbu.edu.cn.
The authors declare no conflict of interest.
Received 3 December 2021
Accepted 22 March 2022
Published 12 April 2022
March/April 2022 Volume 10 Issue 2 10.1128/spectrum.02465-21 1
RESEARCH ARTICLE
responses to antigens, balancing tolerance and effector immune functions (14). For the
regulation of immunity by the gut microbiota, the wide range of secondary metabo-
lites produced by the commensal gut microbiota were proven to be crucial for host
physiology and host immunity regulation (15). To date, numerous studies of the gut
microbiota and its functional potential have been conducted on vertebrates, and infor-
mation concerning invertebrates is limited, especially in aquatic invertebrates.
Shrimp are one of the most important animals in aquatic aquaculture (16).
Although the farming industry of shrimp has increased considerably in recent years,
with the expansion of farming, threats by various environmental challenges restrict the
sustainable development of the industry worldwide, such as pathogen infection (17).
White spot syndrome virus (WSSV) is a typical pathogen of shrimp and with a wide
range of hosts (18). Infection with WSSV causes white spot syndrome of shrimp and
leads to 100% mortality within 7 to 10 days (19). As reported, the gut microbiota plays
a key role in regulating the immune system of the host (14). Multiple pathways such as
carbohydrate metabolism and cofactor/vitamin biosynthesis that the gut microbiota
participates in can promote host metabolism and anti-infection and anti-inflammation
processes and regulate autoimmune reactions (20). Secondary metabolites produced
by the gut microbiota include short-chain fatty acids (SCFAs), polyamines, the aryl
hydrocarbon receptor (AHR), and so on (21) and can interact with host cells through
the intestinal epithelia, thus influencing immune responses and disease risk (22). A few
studies have found that the gut microbiota is related to growth and development in
shrimp, and gut microbiota dysbiosis is responsible for shrimp white feces syndrome
(16, 23, 24). Several studies have reported the impact of WSSV infection on the intesti-
nal microbiota in Litopenaeus vannamei. However, few studies have assessed the gut
microbiota associated with host immunity responses to WSSV infection.
Here, WSSV infection was used as an environmental challenge for four different spe-
cies of shrimp (Marsupenaeus japonicus,Litopenaeus vannamei,Macrobrachium rose-
nbergii, and Procambarus clarkii). By using 16S rRNA gene sequencing and liquid chro-
matography-coupled mass spectrometry (LC-MS), the gut microbiota and metabolome
before and after virus infection were identified. We determined a group of microbes
that perform immunomodulatory functions in the gut of different species of shrimp
and assist the host in maintaining environmental adaption. These valuable findings
greatly enhanced our understanding of the functions of the gut microbiota in main-
taining host fitness under environmental challenge and provide a new strategy for the
prevention and treatment of viral infection in shrimp.
RESULTS
Gut microbiota of different shrimp. In light of exploring the shrimp gut microbiota
as a whole, four common shrimp in freshwater and seawater were used in this study:
Macrobrachium rosenbergii,Procambarus clarkia,Marsupenaeus japonicus,andLitopenaeus
vannamei. Intact intestinal tracts were sampled, and total DNA was extracted followed by
16S rRNA gene amplicon sequencing (n= 3). In total, the sequencing of the shrimp gut
microbiota yielded 383,028 reads, resulting in 1,135 operational taxonomic units (OTUs;
GenBank accession number PRJNA780955). OTUs were classified into 24 phyla and 488
genera. The composition of the microbial communities across the gut revealed significant
discrepancies between different species of shrimp (Fig. 1A andB).Thedominantbacteria
in gut of Macrobrachium rosenbergii and Procambarus clarkii were Tenericutes and
Firmicutes, respectively. The dominant bacteria of the other two shrimp were both
Proteobacteria (Fig.1A).Amongthefourshrimpspecies,Procambarus clarkii showed signif-
icantly higher species richness (paired Wilcoxon ttest, P,0.05; Fig. 1C) and diversity
(Shannon index, P,0.01), while Macrobrachium rosenbergii and Litopenaeus vannamei
showed significantly lower richness and diversity (P,0.05; Fig. 1C). The diversity and com-
position of gut microbes may be related to the habitant-intestinal environment of shrimp
and suggests that the gut serves as a strong environmental filter, enabling the establish-
ment of distinct microbial communities in the different shrimp.
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A core microbiota persists across different species of shrimp. To identify the
core gut microbiota among four species of shrimp, the niche breadth of individual
microbes was characterized at the taxonomic level of the genus first, which could reflect
the adaptation of species to the environment. In general, the larger the niche breadth of
a species, the less specialized it is, that is, the more likely it is to be a generalized species.
The niche breadth of each microbe was calculated by using the Levin’s measure, as pre-
viously described (8, 25), and the results revealed that only a few of the microbes inhab-
ited a wide range of environments along the different shrimp, and most of them had a
narrow range of occupancy across different environments (Fig. 2A), suggesting that
microbes with high niche breadth had greater odds of being core microbes. To address
this speculation, we used a Venn diagram to analyze the core microbes that exist in ev-
ery environment and found 55 microbes that fell into this definition (Fig. 2B). Notably,
when we examined the correspondence between Venn diagram and niche breadth mea-
surement, we found some inconformity. The niche breadth of some core microbes was
lower, such as Alistipes,Rikenella,Erysipelotrichaceae_uncultured, and so on (Fig. 2C),
which was due to their uneven distribution in different environments. The abundance of
FIG 1 Microbial diversity in the gut among different species of shrimp. (A) The relative abundance of the microbial communities at the genus level found
in the guts (Macrobrachium rosenbergii,Procambarus clarkii,Marsupenaeus japonicus, and Litopenaeus vannamei) of different shrimp that are from seawater
or freshwater. (B) Principal-coordinate analysis of samples based on the weighted UniFrac method. (C) The
a
-diversity comparison between different
species of shrimp (n= 3). Significant differences are indicated by asterisks (*,P,0.05; **,P,0.01).
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
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FIG 2 Fifty-five microbes were found as core microbes that persist across the guts of all shrimp species. (A) Niche breadth of the overall microbial
communities calculated using Levin’s measure. The larger the niche breadth, the more generalized the microbe. (B) Venn diagram showing the unique and
shared bacterial genera in the gut between four shrimp species. At the genus level, 55 microbes that were shared among all habitats. (C) The niche
breadth of each microbe, in descending order. The listed microbes are a part of microbes that are found in the guts of all shrimp (core microbe). (D) Heat
map showing the abundance of each core microbe across different shrimp guts at the genus level. The shade of color indicates the abundance of
microbes. (E) Proportion of core microbes and specific microbes in the guts of different shrimp species.
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
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these bacteria could reach up to 9% in Procambarus clarkii but less than 1% in the intes-
tines of the other three shrimp species (Fig. 2D). The situation showed that it was the dif-
ferent species of shrimp that shapes various gut microbial communities. Even so, some
microbes will not disappear due to this influence, and they were consistently present in
the guts of shrimp, although their abundance varied greatly. In this term, 55 such
microbes were identified as core microbes in the gut of shrimp (Fig. 2D), and others
were identified as specific microbes. Although these microbes contributed almost 90%
(except for core microbe in Macrobrachium rosenbergii that was due to differences in the
dominant microbes) of the total relative abundance, they comprised less than 10% of
the overall richness (Fig. 2E). The distribution of these microbes clearly varied in guts of
different species.
Abundance of core microbes in the shrimp gut is changed by WSSV infection. It
is recognized that the gut microbiota is related to pathogen infection in shrimp, but
detailed study of the relationship between core microbiota and pathogen infection
and their causal roles have not been clearly elucidated. Therefore, WSSV, which is a
prevalent viral pathogen and able to cause extremely high mortality in shrimp, was
used to explore the issue in this study. Based on the determined core microbiota in the
shrimp gut, we analyzed the influence of WSSV infection on core microbial abundance.
We found that the abundance of core gut microbes in Procambarus clarkii had the
greatest disturbance after virus infection, with nearly half of the core microbial abun-
dance significantly changed (P,0.01; Fig. 3). Additionally, a Sankey diagram showed
some differences in the abundance change of core gut microbes between shrimp from
seawater and shrimp from freshwater after WSSV infection (Fig. 3A). The most obvious
distinction was in the abundance of Vibrio and Photobacterium. After virus infection,
the abundance of Vibrio in the guts of seawater shrimp decreased significantly, and
the abundance of Photobacterium increased significantly, while the changes in the gut
of freshwater shrimp were opposite (Fig. 3A). A similar condition was seen in other
microorganisms, such as Citrobacter,Lactobacillus,Bacteroides, and so on. The abun-
dance changes induced by WSSV infection were consistent in some core microorgan-
isms, such as Achromobacter,Chryseobacterium, and Flavobacterium (Fig. 3B). Although
the changes were significant, their abundance in the guts of shrimp were relatively
low, so the response to virus infection may be small (Fig. 3B). Notably, the taxonomic
composition of the microbial community in shrimp after virus infection showed distinct
successional trajectories (Fig. 3C), with all microbial communities of the gut developing
toward the first and fourth quadrants of the principal-coordinate analysis (PCoA), sug-
gesting that the responses of gut microorganisms among different shrimp to viral
infection may eventually follow the same trend.
Virus infection disrupted the initial metabolism of the shrimp gut. As reported,
the gut microbiota plays a major role in amino acid metabolism, lipid metabolism, pro-
tein digestion, and the fermentation of complex carbohydrates into short-chain fatty
acids (SCFAs) that are important for the health of organisms (26, 27). Hence, the micro-
bial composition obtained by 16S rRNA gene sequencing was used to predict the Kyoto
Encyclopedia of Genes and Genomes (KEGG) metabolic pathways that are involved, and
the differences between different samples and groups were analyzed. Based on explor-
ing the proportions of each KEGG metabolic pathway (level 2), we found some discrep-
ancies between infected and uninfected shrimp. These discrepancies had some com-
monalities in the changes of each species of shrimp after treatment with WSSV (Fig. 4).
The proportions of 12 pathways were altered in the guts of the four shrimp after virus
infection, including biosynthesis of other secondary metabolites, carbohydrate metabo-
lism, cell growth and death, the endocrine system, energy metabolism, metabolism of
terpenoids and polyketides, the immune system, infectious diseases, lipid metabolism,
membrane transport, and metabolism of cofactors and vitamins and signal transduction
(Fig. 4). Some of these pathways have been previously reported to be related to host me-
tabolism and anti-infection and anti-inflammation processes (20). These results reflect
metabolic changes that may be caused by alterations in the microbiome of the shrimp
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gut after viral infection, indicating that the response of the core microbiota to viral stim-
ulation in different shrimp intestines was consistent.
Alterations in the gut metabolome by microbiota function in maintaining
shrimp immune homeostasis. By altering the structure of the gut microbiota, infection
with WSSV may alter the function of the community. This change in function is reflected
in the gut metabolome, which includes both host- and microbial-derived metabolites. To
determine which metabolites changed in the guts of shrimp after viral infection and par-
ticipate in the anti-infection response, the gut metabolome of shrimp was explored using
mass spectrometry platforms with an untargeted approach. Shrimp intestinal contents
from different time points after infection with WSSV were used to characterize the
changes of the gut metabolome, and the top 50 metabolites with maximum fold change
and significant changes were selected for display (Fig. 5A). Most metabolites were seen
with significant decreases after WSSV infection, some of them varying by more than
50-fold, such as 4-dimethylallyl-L-tryptophan, bactoprenyl diphosphate, gymnodi-
mine,andsoon(Fig.5A).Themostsignificant increase (with 5-fold to 11-fold) was
seen in nine metabolites, including toluene-cis-dihydrodiol, eicosapentaenoic acid,
FIG 3 The abundance of core gut microorganisms changed after shrimp were infected with WSSV. (A) Sankey charts of three time points from the core
microbial community of the shrimp gut after WSSV infection. The taxonomy level, including phylum and genus, is displayed. The top 10 most abundant
genera and the relative changes over time are visible. (B) Balloon plot showing the significant changes of core gut microbial abundance across different
shrimp species after WSSV infection. The size of the circles represents the change of bacterial abundance, while the color indicates increase or decline
(n=3;*,P,0.05 and **,P,0.01). (C) Principal-coordinate analysis of samples after WSSV infection.
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
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(1)-carvone, myxalamid S, N-nitrosodimethylamine, oplophorus luciferin, traumatic
acid, 4-hydroxyretinoic acid, and 6-hydroxy-3,7-dimethyloctanoate, suggesting that
they have functional potential in anti-infection of WSSV in the shrimp gut (Fig. 5A).
Metabolites that change significantly mostly belonged to 18 KEGG metabolic path-
ways compared with the initial states, such as secondary bile acid biosynthesis, lino-
leic acid metabolism, biosynthesis of unsaturated fatty acids, necroptosis, and the
AMP-activated protein kinase (AMPK) signaling pathway that are correlated with the
immunity and health of organisms, consistent with what the sequencing predicted
(Fig. 4 and 5B). Additionally, a correlation analysis conducted with the Spearman
algorithm found that the alteration of the gut metabolome was tightly correlated
with the changes of the core gut microbiota (Fig. 5A). For example, nine significantly up-
regulated metabolites after infection strongly positively correlate with half of the core
microbes of the gut, while those significantly downregulated metabolites have a strong
positive correlation with the rest of the microbes (Fig. 5A). These results showed that de-
spite that the decrease in the large number of metabolites and the enriched KEGG path-
way that they belonged to resulted in a reduction in shrimp immunity and health after
WSSV infection, the increase of some antiviral metabolites under the influence of gut
microbes may assist host adaptation to environmental challenge.
DISCUSSION
In hosts, multiple factors shape the diversity of the gut microbiome, such as the de-
velopmental cycle, dietary differences, intestinal pH, and geographical location of the
host (28). The microorganisms that are prevalent among different hosts regardless of
these factors are defined as core microbes (8). A functional core microbiota was pro-
vided by abundant bacterial taxa. To date, identifications of core gut microbiota typi-
cally focus on vertebrates, and information about those of invertebrates is very limited,
especially in aquatic invertebrates. Thus, shrimp, an essential invertebrate in global
aquatic aquaculture (29) were used to explore the core microbiota in our study. Based
on 16S rRNA gene sequencing, the gut microbial composition of four different species
of shrimp was determined, and we found that the gut environment of different shrimp
is a strong habitat filter for the microbial community, resulting in the high diversity of
the gut microbial community among different species of shrimp. By calculating the
niche breadth of microorganisms and analyzing their presence or not in different guts,
a core gut microbiota containing 55 genera was identified. They were present in the guts
FIG 4 Scatter plot showing the alterations of KEGG metabolic pathways among four species of shrimp after virus infection. Purple, green, red, and blue
represent Marsupenaeus japonicus,Litopenaeus vannamei,Macrobrachium rosenbergii, and Procambarus clarkia, respectively.
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
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of each shrimp species regardless of the environment changes, even though their abun-
dance varies (inhomogeneity) from high to low. Therefore, our study provides a character-
ization of the core microbiota among different species of shrimp for the first time, and we
speculate that they perform a vital role in host response to environmental stimuli.
Recent large-scale studies have provided insights into gut microbial structure and
functional potential (30). As reported, the gut microbiota profoundly influences host
fitness, and it is able to influence various host physiological processes by regulating
multiple processes, including nutrient absorption, immune function, oxidative stress,
inflammation, and anabolic balance, when the host is stimulated by the environment
(31, 32). The gut microbiota performs its function on some different landscapes in the
host, including metabolic, protective, structural, and neurological functions. For
instance, some gut microbial species are the main producers of short-chain fatty acids
FIG 5 Changes in the intestinal metabolome of Marsupenaeus japonicus after WSSV infection. (A) Gut metabolites with significant changes after treatment
with WSSV and their correlation with gut core microbiota. The Z-score = (x2
m
)/
s
, where xrepresents a specific score,
m
represents the mean value, and
s
represents the standard deviation. Red boxes indicate positive correlations, while the blue boxes show negative correlations. (B) Enrichment of
metabolites in KEGG pathways in response to WSSV infection. The color of the dot represents the Pvalue, and the bluer the dot is, the more significant
the enrichment is. The size of the dot represents rich number.
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
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(SCFAs; formate, acetate, propionate, butyrate, valerate, isovalerate, and hexanoate) and
function in biodegradation of various undigested organics, bile salt, choline, polyphe-
nols, and so on (33–35). Activities of the gut microbiota have an influential role in modu-
lating the gut-brain axis and in maintaining gut homeostasis in the human body via pro-
duction, expression, and turnover of these metabolites (36–38). Additionally, activation
of goblet cells to secrete mucin is associated with fucose, a product that is cleaved from
glycans by gut microbes (39). In shrimp, biofilm formation by gut bacteria was found to
be related to regulating homeostasis in invertebrates (40). Shrimp white feces syndrome
was also reported to be related to intestinal microbiota dysbiosis (16). Wang’sstudyhad
shown that the growth and developmental of shrimp is accompanied by alterations in
the gut microbiota, and some pivotal microbes are crucial in the growth of shrimp (24).
However, little is known about the response of the gut microbiota to environmental
changes, such as viral infection, and its effects on the host. Herein, we found that the
core gut microbiota in different species of shrimp adjusts and develops toward the same
trend when the host is exposed to virus infection. Such an alteration ultimately leads to
downregulation of a large number of metabolites in the gut when the shrimp was
infected with viruses, and these metabolites are involved in multiple immune-related
pathways. However, the upregulation of nine metabolites and the closely correlated
core microbes suggests that they have antiviral function potential and play an important
role in host resistance to viral stimulation. This reflects an adaptive adjustment of gut
microbes to the environmental challenges of shrimp, which may be manifested as a
coadaptation of the host and gut microbes under environmental challenges.
Conclusions. In this study, WSSV infection was used as an environmental challenge
for four different species of shrimp (Marsupenaeus japonicus,Litopenaeus vannamei,
Macrobrachium rosenbergii, and Procambarus clarkii). By using 16S rRNA gene sequenc-
ing and liquid chromatography-coupled mass spectrometry (LC-MS), the gut micro-
biota and metabolome before and after virus infection were identified. Our findings
provided the first attempt to compare the gut microbiota among the four different
species of shrimp (Marsupenaeus japonicus,Litopenaeus vannamei,Macrobrachium
rosenbergii, and Procambarus clarkii), found huge variations, and determined a core gut
microbiota composed of 55 microbes. The environmental challenge of WSSV infection
led to changes in core microbial structures, but the alteration of core microbiota
among different shrimp species followed the same trend and showed immune-related
function in the prediction of its metabolic potential. In metabolomic analysis, nine sig-
nificantly upregulated metabolites that were found after viral infection suggested that
they have antiviral potential. Moreover, the tight correlation between them and almost
half of the core microbiota demonstrated that they were responsible for maintaining
immune homeostasis. These valuable findings greatly enhanced our understanding of
the gut microbiota in maintaining host fitness under environmental challenge and pro-
vide a new strategy for prevention and treatment of viral infection in shrimp.
MATERIALS AND METHODS
Shrimp culture and WSSV infection. Shrimp were cultured as previously described (18), and three
from each group were randomly selected for PCR detection of WSSV with specific primers (59-
TTGGTTTCATGCCCGAGATT-39and 59-CCTTGGTCAGCCCCTTGA-39) to ensure that the shrimp used for
experiments were WSSV free. WSSV-free shrimp were infected with WSSV (10
5
copies/mL) by injection
(100
m
L of WSSV inoculum/shrimp) into the lateral area of the fourth abdominal segment. The WSSV-
infected shrimp were collected for later experiments at different times after infection.
Sample collection and DNA extraction. Three shrimp were randomly selected from each group for
aseptically collecting intestines after sterilizing the surface of shrimp with 70% ethanol. The intestine
was dissected using sterile instruments, and microbial DNA was isolated from gut samples using the
bacterial genome DNA extraction kit (Generay, China) following the manufacturer’s protocols.
Sequencing and data analysis of microbial 16S rRNA. Amplicon sequencing covering the V4-V5
regions of bacterial 16S rRNA gene was performed by Mingke Biotechnology Co., Ltd. (Hangzhou, China), using
universal bacterial primers 515F (59-GTGCCAGCMGCCGCGG-39) and 907R (59-CCGTCAATTCMTTTRAGTTT-39).
Sequencing was performed using an Illumina PE250 (Illumina, USA), and the barcoded library was constructed
using an Illumina TruSeq DNA library kit (Illumina, USA) (41). Sequencing data were uploaded to NCBI
(GenBank accession number PRJNA780955).
The paired-end reads were overlapped to assemble the sequences using the Flash program. After
Potential Immune Function of Microbiota in Shrimp Gut Microbiology Spectrum
March/April 2022 Volume 10 Issue 2 10.1128/spectrum.02465-21 9
removal of low-quality fragments, spacers, primers, and the sequences shorter than 50 bp, the remaining
sequences were denoised and screened for chimeric sequences with the pre.cluster command and
chimera.uchime command in Mothur. The candidate sequences were classified into operational taxo-
nomic units (OTUs) by 97% sequence similarity using the Usearch program.
Principal-coordinates analysis. Beta diversity was evaluated by principal-coordinates analysis (PCoA)
plots based on unweighted UniFrac metrics using the vegan of package R (version 3.4.4; https://www.r
-project.org/). The potential principal components that affect the difference of sample community compo-
sition were found out through dimension reduction based on Euclidean distance and other distances.
Alpha diversity analysis. Alpha diversity includes a series of statistical analysis indices to estimate
the species abundance and diversity of the environmental community. Community richness was calcu-
lated using the following indices: Chao (http://www.mothur.org/wiki/Chao) and Ace (http://www
.mothur.org/wiki/Ace). The indices used to calculate community diversity were Shannon (http://www
.mothur.org/wiki/Shannon) and Simpson (http://www.mothur.org/wiki/Simpson).
The calculation of niche breadth. The niche breadth was calculated as the formula described. The
core bacteria inhabit a wide range of environments along the different samples, such as environmental
types, and specific bacteria have a narrower range of occupancy across these different environments. B
i
represents niche breadth (1 #B
i
#n), nrepresents the number of habitats, and irepresents the microbial
genus. P
ij
is the ratio of genus iin the nth habitat to the total number of this genus in all habitats (25).
Bi¼1=X
n
j¼1
Pij
ðÞ
2
Pij ¼nij=Pi1
Functional prediction by PICRUSt2. The PICRUSt2 (phylogenetic investigation of communities by
reconstruction of observed states, v2.1.0-b) pipeline was used to predict functional potentials of the gut
microbiota. Functional profiles were predicted using the script picrust2_pipeline.py, generating a table
of KEGG orthologs (KOs). KEGG Mapper was used to reconstruct KEGG reference categories (KEGG level
1) and modules (KEGG level 2) according to the KO annotations.
Metabolome analysis of shrimp intestinal contents based on LC-MS. Three shrimp were randomly
selected from each group, and their intestines were dissected. Intestines (100 mg) were diluted with 1 mL
of a mixture of methanol-acetonitrile-water (2:2:1 [vol/vol]), followed by centrifugation at 13,000 rpm for
15 min at 4°C. Subsequently, 100
m
L of the supernatant was harvested for liquid chromatography-coupled
mass spectrometry (LC-MS) analysis. The metabolic profiles were performed by Mingke Biotechnology Co.,
Ltd. (Hangzhou, China), on an Agilent 1290 Infinity LC system and Accurate-Mass QTOF/MS-6545 (Agilent
Technologies, USA). For chromatographic separation, a C
18
(2.1 mm 100 mm) reversed-phase column
(Thermo Scientific, USA) preheated at 35°C was used. A prepared sample of 1
m
L was injected and main-
tained at 35°C for analysis. The gradient conditions for elution were 95% acetonitrile for 2 min, 95 to 90%
from 2 to 3 min of linear gradient, 90 to 30% from 3 to 9 min, 10% from 10 to 12 min, 10 to 95% from 12
to 12.1 min, and 95% from 12.1 to 14 min. The mobile phase for negative ion mode (ES2) and positive ion
mode (ES1) was composed of water with 0.04% formic acid as solvent A and acetonitrile with 0.04% for-
mic acid as solvent B, and the flow rate was at 0.3 mL/min.
Metabolome data processing. The original data were converted into m/zformat by ProteoWizard,
and peak detection, alignment, and retention time correction were carried out by the XCMS program.
The “SVR”method was used to correct the peak positions, and the peaks with a loss rate of .50% in
each group were filtered. After correcting the screened peaks, the metabolite identification was
obtained by searching the Metlin metabolite database. Statistical analysis was performed by the R
program.
KEGG annotation of differential metabolites. Different metabolites interact with each other in
organisms and form different pathways. The KEGG database was used to annotate the metabolic path-
ways of the differential metabolites involved. KEGG pathway enrichment was conducted according to
the results of differential metabolites. Metabolites with significant alterations between groups were
defined as differential metabolites and were obtained at a variable influence on projection (VIP) of .1.5,
with a Pvalue of ,0.05 (ttest statistics) based on the peak intensities.
Data availability. The data we obtained from next-generation sequencing were uploaded to the
NCBI database under GenBank accession number PRJNA780955.
ACKNOWLEDGMENTS
This work was supported by National Natural Science Foundation of China (32102832)
and China Postdoctoral Science Foundation (2021M701795).
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