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ORIGINAL RESEARCH
published: 22 November 2018
doi: 10.3389/fmicb.2018.02855
Edited by:
Jaime Romero,
Universidad de Chile, Chile
Reviewed by:
Virginia Helena Albarracín,
Center for Electron Microscopy
(CIME), Argentina
Sigmund Jensen,
University of Bergen, Norway
*Correspondence:
Mark L. Lawrence
lawrence@cvm.msstate.edu
Specialty section:
This article was submitted to
Aquatic Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 06 March 2018
Accepted: 06 November 2018
Published: 22 November 2018
Citation:
Nho SW, Abdelhamed H, Paul D,
Park S, Mauel MJ, Karsi A and
Lawrence ML (2018) Taxonomic
and Functional Metagenomic Profile
of Sediment From a Commercial
Catfish Pond in Mississippi.
Front. Microbiol. 9:2855.
doi: 10.3389/fmicb.2018.02855
Taxonomic and Functional
Metagenomic Profile of Sediment
From a Commercial Catfish Pond in
Mississippi
Seong Won Nho1, Hossam Abdelhamed1, Debarati Paul2, Seongbin Park3,
Michael J. Mauel1, Attila Karsi1and Mark L. Lawrence1*
1Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, United States,
2Amity Institute of Biotechnology, Amity University, Noida, India, 3Department of Animal and Dairy Sciences, Mississippi
State University, Starkville, MS, United States
Metagenomic analyses of microbial communities from aquatic sediments are relatively
few, and there are no reported metagenomic studies on sediment from inland
ponds used for aquaculture. Catfish ponds in the southeastern U.S. are eutrophic
systems. They are fertilized to enhance algae growth and encourage natural food
production, and catfish are fed with commercial feed from spring to fall. As result,
catfish pond sediment (CPS) contains a very dense, diverse microbial community that
has significant effects on the physiochemical parameters of pond dynamics. Here
we conducted an in-depth metagenomic analysis of the taxonomic and metabolic
capabilities of a catfish pond sediment microbiome from a southeastern U.S.
aquaculture farm in Mississippi using Illumina next-generation sequencing. A total of
3.3 Gbp of sequence was obtained, 25,491,518 of which encoded predicted protein
features. The pond sediment was dominated by Proteobacteria sequences, followed
by Bacteroidetes,Firmicutes,Chloroflexi, and Actinobacteria. Enzyme pathways for
methane metabolism/methanogenesis, denitrification, and sulfate reduction appeared
nearly complete in the pond sediment metagenome profile. In particular, a large
number of Deltaproteobacteria sequences and genes encoding anaerobic functional
enzymes were found. This is the first study to characterize a catfish pond sediment
microbiome, and it is expected to be useful for characterizing specific changes in
microbial flora in response to production practices. It will also provide insight into the
taxonomic diversity and metabolic capabilities of microbial communities in aquaculture.
Furthermore, comparison with other environments (i.e., river and marine sediments)
will reveal habitat-specific characteristics and adaptations caused by differences in
nutrients, vegetation, and environmental stresses.
Keywords: metagenome, sediment, aquaculture pond, catfish, eutrophic, nitrogen metabolism, sulfur
metabolism, methanogenesis
INTRODUCTION
Channel catfish production is the largest aquaculture industry in the United States with total
sales of $380 million in 2017 (USDA, 2018). Aquaculture is one of the most rapidly growing
food production sectors (Food Agriculture Organization of the United Nations and Fisheries and
Aquaculture, 2014), but it faces several production and environmental challenges to maintain its
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Nho et al. Catfish Aquaculture Pond Sediment Metagenome
economic viability (Schreier et al., 2010). Fish production ponds
are rich in dissolved nutrients due to intensive feeding and fecal
waste. Unconsumed feed, fish feces, and senescent phytoplankton
are deposited into aquaculture sediments, which can enhance
the microbial flora in the sediments and lead to anoxic
conditions (Holmer and Kristensen, 1992). Medicated feed, such
as Terramycin (oxytetracycline), Romet-30 (sulfadimethoxine-
ormetoprim), and Aquaflor (florfenicol), as well as fertilizers
may also impact pond sediment microflora. In turn, changes in
sediment microflora impact fish health in aquaculture systems.
Fish excrete nitrogen in the form of ammonia, which is
toxic to fish; nitrification by chemolithoautotrophic bacteria in
pond sediments is an important process that prevents toxic
buildup. The microbes contributing to nitrification consist of
two functional groups: the ammonia-oxidizing bacteria (AOB)
and ammonia-oxidizing archaea (AOA) that convert toxic
ammonia to nitrite, and the nitrite-oxidizing bacteria (NOB) that
oxidize nitrite to the less toxic nitrate. AOB include the genera
Nitrosomonas,Nitrosococcus, and Nitrosospira (Kowalchuk and
Stephen, 2001). AOA include Nitrosopumilus from marine
water (Konneke et al., 2005). The NOB group includes genera
Nitrobacter, Nitrospira, and Nitrospina (Wang et al., 2014). Pond
sediments also have potential to serve as a reservoir for fish
pathogens. Therefore, it is important to understand microbial
flora in aquaculture pond sediments because of its impact on the
pond ecosystem, especially in nutrient cycling, concentrations of
organic and inorganic nutrients and toxins, and effects on fish
health.
Metagenomic analysis allows assessment of mixed
environmental microbial communities by directly sequencing
DNA from environmental samples (Dinsdale et al., 2008). This
approach provides a picture of the diversity and microbiome
structure present in the environment (Simon and Daniel, 2009),
and it enables studies to understand how microbial diversity
is modulated in response to environmental or anthropogenic
impacts (Larsen et al., 2012). Thus, it is particularly appropriate
for assessing the taxonomic and functional microbial diversity
in a pond sediment biome and monitoring community changes
over space and time (Nogales et al., 2011).
Previous culture-independent studies investigating sediment
microbial phylogenetic structure showed that microbial
communities are indicators of both the physicochemical status
of freshwater sediments (Logue et al., 2008;Gibbons et al.,
2014) and ecological degradation (Feris et al., 2009). A few
metagenomes have been published from deep-sea sediment
(Kimes et al., 2013), mangrove (Andreote et al., 2012), and river
systems (Staley et al., 2013), and recent studies have examined
the response of fish gut-associated microbial communities
from aquaculture systems in response to lifestyle and dietary
preference (Wu et al., 2010;Xing et al., 2013). Signatures of
bacterial composition were found in shrimp farming (Sousa
et al., 2006), and pyrosequencing was used to explore bacterial
diversity and detect potential fish pathogens during production
of Scophthalmus maximus (turbot) and Solea senegalensis (sole)
(Martins et al., 2013).
In pond sediments, the microbiome is critical for maintenance
of homeostasis conditions, including toxin removal and cycling
of carbon, nitrogen, and phosphorus (Brock, 1970;Leung et al.,
1994). In particular, nitrogen is very important in aquaculture
as a nutrient and potential toxicant, and it is an essential
requirement for phytoplankton and bacterial growth in anaerobic
conditions. In the current study, we present a description of
the microbiome found in sediment from a catfish research
pond maintained under commercial production conditions. Our
results are culture-independent and based on metagenomic
sequence of total DNA extracted directly from the pond
sediment and analyzed by Illumina sequencing. We describe the
microbial taxa present in catfish pond sediment and the potential
metabolic processes that appear to be occurring in this ecosystem
affecting carbon, nitrogen, and sulfur cycling. This analysis also
provided a fundamental baseline profile of the catfish pond
sediment microbiome for comparison with sediments from other
environments.
MATERIALS AND METHODS
Sediment Sampling, DNA Extraction, and
Sequencing
Sediment sampling was performed in October 2012 (water
temperature approximately 20◦C) in sediments from a 0.8-
hectare aquaculture research pond stocked with approximately
6,000–8,000 catfish (Ictalurus punctatus) with average size of
0.5 kg at the Delta Research and Extension Center, Stoneville, MS,
United States. The pond was maintained at typical stocking and
feeding parameters used for catfish aquaculture. Three samples
were collected at 10 am using a modification of a previously
published method (Schneegurt et al., 2003). In brief, ∼500 g per
sample of pond sediment was collected at 5–10 cm of sediment
depth ∼8 m from the pond bank in a water depth of ∼1.2 m using
a sterile spatula and immediately transferred to sterile 50 ml tubes
kept on ice.
Genomic DNA was extracted from each sediment sample
separately using MoBio PowerSoil DNA Isolation Kit (Mo
Bio Laboratories, Carlsbad, CA, United States) according to
manufacturer’s protocol with 5.0 g of sediment per extraction.
A NanoDrop (Thermo Scientific, Wilmington, DE, United States)
spectrometer was used to quantify extracted DNA and to
assess DNA quality. Illumina library preparation and sequencing
followed manufacturer’s protocols (Illumina, San Diego, CA,
United States). Briefly, genomic DNA was sheared by sonication
and separated by electrophoresis on a 1% agarose gel. Gel slices
corresponding to ∼300 and ∼500 bp were excised and purified
using QIAquick Gel Extraction Kit (Qiagen). The blunt-ended
DNA fragments were A-tailed using the Quick Blunting Kit
(New England BioLabs) and purified. Sheared DNA was ligated
to sequence adapters. All ligated libraries were enriched using
standard PCR (11–12 cycles) with Illumina paired end primers
before library quantification and validation. One microgram
DNA was used in library construction. The amplified libraries
were pooled in an equimolar ratio and sequenced using an
Illumina HiSeq 2000 (Illumina, San Diego, CA, United States)
using paired end reads of 100 bases. Raw reads containing three
or more “N” bases or contaminated by adapter (>15 bp overlap)
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Nho et al. Catfish Aquaculture Pond Sediment Metagenome
were removed by Trimmomatic (Bolger et al., 2014), and the
filtered clean reads were used for metagenomic analysis.
Taxonomic Distribution and Functional
Analysis of Metagenomic Sequence
The taxonomic analysis was performed using BLASTX against
the SEED and Pfam databases (Altschul et al., 1997) on the MG-
RAST server1using a cut-off E-value of 1e-5, minimum identity
of 60%, and a minimum alignment length of 15 bp (Meyer et al.,
2008). BLASTX was also conducted using MetaGenome Analyzer
software (MEGAN v5) with the lowest common ancestor (LCA)
algorithm used to visualize results (Huson et al., 2007). Using
BLASTX and BLASTN, reads were compared against the NR and
NT NCBI databases. Analysis was performed comparing distinct
hierarchical levels, and a directed homogeneity test was used to
identify significant differences in sample comparisons. Multiple
testing correction analysis was not applied, and all unassigned
reads were ignored.
Statistical analysis was performed using results from the
MG-RAST annotation system, and results were visualized using
Statistical Analyses of Metagenomic Profiles (STAMP) (Parks and
Beiko, 2010) to detect biologically relevant differences in the
relative proportion of sequences. Paired metagenomic samples
were used for the analysis, and statistical significance of the
differences between samples was assessed by the Two-sided
Fisher’s Exact test. Story’s false discovery rate (FDR) was used for
multiple test correction as recommended by STAMP. Results with
q-value (<0.05) were considered significant, and unclassified
reads were removed from the analysis.
Functional classification was conducted using BLASTX (cut-
off E-value of 1e-5) against COGs (Tatusov et al., 2001), which
was downloaded from hierarchical classification in MG-RAST
server using NCBI database. BLASTX and subsystem analysis
were used against the SEED-NR database in MG-RAST for
functional sequence annotation with the same parameters as a
taxonomic distribution. A functional analysis using the SEED
(Overbeek et al., 2005) and KEGG (Kanehisa et al., 2004)
databases was conducted using MG-RAST sever. Each sequence
was associated with its SEED functional role using the best
BLAST score to protein sequences without known functional
roles. A similar procedure was used to match each sequence to a
KEGG orthology (KO) accession number. Results were matched
with each protein’s RefSeq database, and relative abundance
was used to identify enzymes in important metabolic pathways.
Matches with alignment scores higher than 80 were retained.
To the best of our knowledge, there is no previous information
about structure and function of bacterial communities
in aquaculture pond sediments. Therefore, the microbial
community from aquaculture sediment was compared to
freshwater sediment from the Tongue River in Southeastern
Montana (MG-RAST ID 4481977.3) (Gibbons et al., 2014)
and deep-sea sediment of the Gulf of Mexico (MG-RAST
ID 4465489.3) (Kimes et al., 2013). A classification was used
to determine the sample that most closely clustered to the
taxonomic composition or metabolic potential of the sediment
1http://metagenomics.anl.gov/
metagenome (E-value of 1e-5, the minimum identity of 60%, and
a minimum alignment length of 15 bp).
RESULTS AND DISCUSSION
Sequence Generation
Whole community microbial DNA from catfish pond sediment
(CPS) was sequenced, making this study the first metagenomic
survey of a catfish aquaculture environment (MG-RAST ID
4583113.3). Of the 29,278,265 sequences (totaling 2,927,826,500
bps) that passed quality control, 3,690,631 sequences (11.2% of
total) were identified as artificial duplicate reads (ADRs), which
are nearly identical sequences that result from sequencing two or
more copies of the exact DNA fragment (Niu et al., 2010). Of the
sequences without rRNA genes, 26,127,903 contained predicted
protein features, 4,424,138 (16.9%) of which were assigned an
annotation using at least one protein database, and 21,703,765
(82.9% of features) contain predicted proteins with unknown
function. A total of 2,855,527 sequences (64.5% of annotated
proteins) were assigned to functional categories (Supplementary
Table S1). At the domain level, Bacteria (96.8%) dominated,
while the Archaea (1.2%) and the Eukaryota (1.5%) contributed
substantially less to the CPS community.
Taxonomic Profiles
The numbers of sequences affiliated with each bacterial taxon in
CPS were similar with other sediment samples, with a dominance
of Proteobacteria (46.0%) and an abundance of Bacteroidetes
(15.9%), Firmicutes (8.6%), Chlorflexi (5.1%), and Actinobacteria
(5.0%) (Table 1). Minor groups represented at the phylum
level included Cyanobacteria,Verrucomicrobia,Planctomycetes,
Chlorobi, and Acidobacteria. The four most abundant classes
TABLE 1 | Bacterial classifications and abundance in the CPS metagenome.
Taxonomic group Number of Abundance Number of
reads (%) genera
Proteobacteria (phylum) 2,971,341 46.0
Deltaproteobacteria 1,408,697 21,8 66
Betaproteobacteria 596,878 9.2 87
Gammaproteobacteria 501,097 7.8 165
Alphaproteobacteria 415,93 6.4 119
Epsilonproteobacteria 36,824 0.6 13
Bacteriodetes (phylum) 1,027,970 15.9
Bacteroidia 415,272 6.2 8
Flavobacteriia 254,455 3.9 29
Cytophagia 184,562 2.9 10
Spingobacteriia 130,637 2.0 6
Firmicutes (phylum) 557,836 8.6
Clostridia 360,863 5.6 70
Bacilli 167,236 2.6 43
Chloroflexi (phylum) 331,544 5.1
Chloroflexi 132,079 2.1 4
Actinobacteria (phylum) 326,195 5.0
Actinobacteria 326,195 5.0 106
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of bacteria were Deltaproteobacteria,Bacteroidia,Clostridia, and
Actinobacteria according to MG-RAST analysis (Table 1).
The Proteobacteria associated with each sample were
examined more closely to evaluate the potential of both aerobic
and anaerobic biodegradation. In aquaculture ponds and lakes,
the top sediment layer down to a few millimeters is typically
aerobic, but below this depth sediment is anaerobic (Boyd and
Tucker, 1998). The high occurrence of Deltaproteobacteria in
CPS, which is not commonly observed in metagenomes from
water or sediment samples (Figure 1), might be related to
the catfish habitat, where eutrophic and anaerobic conditions
could drive selection for specific microbial groups such
as sulfate associated bacteria (Harrison et al., 2009). The
Deltaproteobacteria in our sample was mostly comprised of
a branch of strictly anaerobic genera containing many of
the known sulfate- and sulfur-reducing bacteria including
Desulfovibrio,Desulfobacter,Desulfococcus,Desulfonema,
and Desulfuromonas spp. High organic loads and anaerobic
conditions in pond sediments yield ideal conditions for sulfate
reduction and sulfide production (Boyd and Tucker, 1998). Many
of the Deltaproteobacteria were also species involved in methane
transformation, which was paralleled by the presence of other
bacteria with anaerobic physiology such as ferric iron-reducing
Geobacter spp. (Supplementary Table S2).
Deltaproteobacteria, in particular, are capable of fumarate
addition to both aromatic and aliphatic hydrocarbons, hence
activating the anaerobic hydrocarbon biodegradation pathway.
The increases in Deltaproteobacteria correlated with an increase
in other protein-coding genes involved in anaerobic degradation
of hydrocarbons, such as benzylsuccinate synthase (BSS), acetyl-
CoA acetyltransferase, and benzoyl-CoA reductase (Kimes et al.,
2013). Anthropogenic hydrocarbon loading from catfish feed
may enrich for microbial species with anaerobic hydrocarbon
degradation capabilities. Other types of Proteobacteria, including
Beta-,Gamma-, and Alphaproteobacteria had relatively lower
representation compared with microbial populations from
other aquatic sediments from river or deep sea environments
(Figure 1). Moreover, the CPS had a higher prevalence of
Epsilonproteobacteria than the river and deep-sea sediments.
Epsilonproteobacteria are prevalent in the digestive tracts of
animals and serve as symbionts or pathogens; their energy
metabolism involves oxidizing reduced sulfur, formate, or
hydrogen coupled with the reduction of nitrate or oxygen (Takai
et al., 2005).
The phylum Bacteroidetes is very diverse and includes
Cytophaga,Flexibacter, and Bacteroides (Woese, 1987;
Woese et al., 1990). The Bacteroidetes phylum is comprised
of four classes: Bacteroidia,Flavobacteria,Cytophage, and
Sphingobacteria, which include around 7,000 different species
(Bergey et al., 2011). The Bacteroidetes phylum in CPS included
the Flavobacteria, which has many aquatic species (Table 1), and
it also contained opportunistic human pathogens (Bernardet and
Nakagawa, 2006), including the genera Elizabethkingia,Weeksella
and Capnocytophaga (Kim et al., 2005; Leadbetter, 2006)
(Supplementary Table S2). F. psychrophilum,F. columnare,
and F. branchiophilum are some Bacteroidetes species that have
economic impacts on freshwater fish, causing infections that can
have severe effects on farmed and wild fish (Hawke and Thune,
1992;Loch and Faisal, 2015). Flavobacterium infections were first
reported a century ago in aquaria (Davis, 1922).
Previously, Firmicutes was observed as a dominant phylum in
the digestive tract of many marine and freshwater fish species
(Austin, 2006;Wu et al., 2012). In the present study, Firmicutes
was found to be prevalent in CPS. In particular, Clostridium sp.
was the most abundant genus within Firmicutes and represented
5.6% of the identified sequences (Table 1), making it more
abundant in CPS compared to marine and river sediments
(Figure 1). Clostridium sp. are commonly found in human
and animal guts and can form endospores, allowing survival
under unfavorable environments (Davies et al., 1995;Mueller-
Spitz et al., 2010). Clostridium sp. contribute to hydrolytic
enzyme production, suggesting a possible role in degradation of
organic matter. In eutrophic marine cage aquaculture sediments,
metabolism is dominated by anaerobic decomposition (Holmer
and Kristensen, 1992); therefore, enrichment of Clostridium sp.
may be a good indicator of the impact of organic matter in
aquatic sediments. Interestingly, some lactic acid bacteria were
detected in the Firmicutes phylum in CPS, including the genera
Lactococcus,Streptococcus, and Enterococcus (Supplementary
Table S2). Lactic acid bacteria are generally considered to be
non-pathogenic (Ringø and Gatesoupe, 1998). However, some
species including Lactococcus garvieae,Streptococcus shiloi, and
Streptococcus difficile were reported as fish pathogens (Eldar et al.,
1994, 1996).
The highest proportion of archaeal reads within the CPS
metagenome was Euryarchaeota (87.6%), which was composed
of several classes: Archaeoglobi (1,159 reads), Halobacteria (3,001
reads), Methanobacteria (1,956 reads), Methanococci (2,334
reads), Methanomicrobia (12,467 reads), and Thermococci (1,593
reads) (Figure 2). Methanosarcina was the most abundant genus
and accounted for 31% of the total archaeal sequences. This genus
includes many methanogens involved in both acetotrophic and
hydrogenotrophic methanogenesis, which is a type of anaerobic
respiration that generates methane and is considered the terminal
step in organic decomposition. Most Methanosarcina are non-
motile and mesophilic, and they are unique among archaeal
bacteria in that most can utilize multiple substrates as electron
acceptors, including methanol (Kandler and Hippe, 1977). Thus,
Methanosarcina is among the most adaptable and flexible of the
methanogens (Maeder et al., 2006).
The eukaryotic sequences represented 23 phyla from the
Animalia,Fungi,Plantae, and Protista. The Animalia phylum
Chordata (20.76 – 28.52%) showed predominant abundance
in the three sediments, followed by Athropoda,Ascomycota,
and Steptophyta.Chordata was the most abundant in deep-
sea sediment, but the Arthropoda was lower compared to
CPS and river sediment. The Bacillariophyta,Cnidaria, and
Echinodermata phyla had greater abundance in CPS compared
to river sediment, and Apicomplexa was in greater abundance in
river sediment (Figure 3).
Functional Categories
Environmental DNA from CPS had matches in 24
COG and 28 KEGG functional categories, respectively
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FIGURE 1 | Taxonomic distribution of bacterial classes from the CPS and other aquatic sediments from freshwater and deep-sea environments.
FIGURE 2 | Taxonomic affiliation of archaeal reads in the CPS metagenome at the phylum (A) and class (B) level.
(Supplementary Table S3). The dominant COG functions were
prokaryotic, with high abundance of sequence reads in energy
production and conversion as well as amino acid transport and
metabolism. In particular, CPS had a high number of sequences
in signal transduction mechanisms, carbohydrate transport
and metabolism, inorganic ion transport and metabolism,
and general function prediction. A lower percent of reads
was found for functions associated with eukaryotic organisms
(RNA procession and modification, chromatin structure and
dynamics, cell motility, and cytoskeleton and extracellular
structures) (Figure 4A). The most abundant KEGG functional
categories were carbohydrate metabolism, clustering-based
subsystems, miscellaneous, amino acids and derivatives, and
protein metabolism (Figure 4B).
The metabolic potential of the CPS metagenome was
compared with two freshwater and deep-sea sediment
metagenomes publicly available on the MG-RAST server.
A heat map showed that the CPS metagenome is similar to
the deep-sea sediment metagenome. CPS had more sequences
within phosphorous metabolism, protein metabolism, and
membrane transport than the other aquatic sediments (Figure 5).
Phosphorus is relatively abundant in catfish ponds because
producers often fertilize ponds to encourage algal blooms,
and protein is also relatively abundant due to application of
commercial feeds.
Based on our taxonomic analysis, we expected to detect
genes encoding enzymes involved in methanogenesis, which
is considered the final step in decomposition. As expected, we
detected methanogenesis genes encoding an F420-dependent
N(5),N(10)-methylenetetrahydromethanopterin reductase
(EC 1.5.99.11), N(5),N(10)-methenyltetrahydromethanopterin
cyclohydrolase (EC 3.5.4.27), formylmethanofuran
dehydrogenase (EC1.2.99.5), CoB–CoM heterodisulfide
reductase (EC1.8.98.1), coenzyme F420 hydrogenase (EC
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FIGURE 3 | Comparison of the relative abundance of eukaryotic reads within the 23 represented phyla in CPS and two previously published marine sediments.
FIGURE 4 | Functional assignment of metagenome sequences. (A) BLASTX analysis against the COGs database: percent was assigned to specific COG functional
categories, and (B) BLASTX analysis against GenBank conducted using MG-RAST; percent abundance was assigned to specific KEGG identifiers.
1.12.98.1), N5-methyltetrahydromethanopterin (EC 2.1.1.86),
and the enzyme responsible for the last step of methanogenesis,
methyl coenzyme M reductase (EC 2.8.4.1) (Table 2).
The transformation of methane and methanol into
formaldehyde and then formate in CPS was suggested
by metabolic reconstruction, mainly from the activity of
particulate methane monooxygenase (EC 1.14.13.25), methanol
dehydrogenase (EC 1.1.99.8), and S-formylglutathione hydrolase
(EC 3.1.2.12). We also detected genes encoding enzymes
involved in formaldehyde fixation, including genes for enzymes
that incorporate methane into organic compounds via the serine
pathway or the ribulose monophosphate pathway, indicating
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FIGURE 5 | Hierarchical clustering combined with heat mapping based on functional subsystem classifications for CPS, freshwater sediment of the Tongue River in
Southeastern Montana, and deep-sea sediment of the Gulf of Mexico.
that the CPS microbiome is able to metabolize methane as
their source of carbon and energy to survive. Compared to the
metagenome of freshwater sediment from the Tongue River, both
CPS and the freshwater sediment microbiomes encode processes
for formaldehyde fixation; the distinct sediments differ only
in the particular pathways used. In CPS, methane metabolism
is encoded by the Gammaproteobacteria using the ribulose
monophosphate pathway to assimilate carbon; in addition,
part of the Alphaproteobacteria utilized the serine pathway of
carbon assimilation. Oxidation of formate yields carbon dioxide,
and a high abundance of genes encoding proteins involved in
the conversion of carbon dioxide into carbon monoxide and
later into acetyl-CoA was detected (Figure 6A). Formate also
contributes to oxidative stress response; oxidase stress catalase
(EC 1.11.1.6) and peroxidase (EC 1.11.1.7) were detected in
CPS, both of which are antioxidant enzymes that contribute to
limiting oxidative damage by reactive oxgen species (ROS) such
as H2O2(Table 2).
Analysis of nitrogen metabolism revealed genes encoding
nitrogen immobilization and mineralization in CPS (Figure 6B).
Sequences encoding nitrogen fixation were detected, including
atmospheric nitrogen fixation using a nitrogenase (EC 1.18.6.1)
that converts nitrogen gas to ammonia. A high abundance
of nitrification species was detected in the metagenome
(Supplementary Table S3) such as Nitrosomonas spp.,
Nitrobacter spp., and Nitrococcus spp. These species oxidize
ammonia to nitrate, preventing toxic buildup of ammonia
that can affect fish health. Genes encoding nitrification
such as hydroxylamine oxidoreductase (EC 1.7.3.4) for
oxidation of hydroxylamine were found. Genes encoding
denitrification enzymes, including nitrate reductase (EC
1.7.99.4), nitrite reductase (EC 1.7.1.4, EC 1.7.7.1 and
1.7.2.1), cytochrome c552 precursor (EC 1.7.2.2), nitric-
oxide reductase (EC 1.7.99.7) and nitrous-oxide reductase
(EC 1.7.99.6) were observed (Table 2). Denitrification is
the dissimilatory reduction of nitrate into nitric oxide,
dinitrogen oxide, and nitrogen. The balance among these
pathways is affected greatly by environmental conditions
including oxygenation, temperature, nitrate concentration, and
organic matter content in the sediment (Saunders and Kalff,
2001).
The predominant type of sulfur metabolism encoded in
the CPS generates the reductive form of sulfite and hydrogen
sulfide (H2S) (Figure 6C). Most of genes observed were involved
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TABLE 2 | Numbers of represented gene variants in the CPS metagenome for different functions.
Metabolism Function No of No of
Methane sequence genes
reads
Particulate methane monooxygenase (EC 1.14.13.25) Oxidation of ammonia, methane, halogenated
hydrocarbons, and aromatic molecules
20 11
Methanol dehydrogenase (EC 1.1.99.8) Methanol to formaldehyde. 271 41
S-formylglutathione hydrolase (EC 3.1.2.12) S-formylglutathione to formic acid and glutathione 73 44
F420-dependent reductase (EC 1.5.99.11) CO2to methane 209 25
N(5), N(10)-methenyltetrahydromethanopterin cyclohydrolase (EC
3.5.4.27)
CO2 to methane 30 12
Formylmethanofuran dehydrogenase (EC1.2.99.5) CO2 and methanofuran to N-formylmethanofuran. 229 77
CoB–CoM heterodisulfide reductase (EC1.8.98.1) Reduction of the heterodisulfide of the methanogenic
thiol-coenzymes, coenzyme M, and coenzyme B
5,015 274
Coenzyme F420 hydrogenase (EC 1.12.98.1) CO2to methane 59 22
N5-methyltetrahydromethanopterin (EC 2.1.1.86) Transfer of the methyl group from N5-
methyltetrahydromethanopterin to coenzyme M
83 44
Methyl-coenzyme M reductase (EC 2.8.4.1) Methyl-coenzyme M and coenzyme B to methane
(anaerobic oxidation).
73 25
Nitrogen
Nitrogenase (EC 1.18.6.1) Nitrogen to ammonia 2,226 172
Nitrate reductase (EC 1.7.99.4) Nitrate to nitrite 3,573 456
Nitrite reductase (1.7.1.4, EC 1.7.7.1 and 1.7.2.1) Reduction of nitrite 1,517 270
Cytochrome c552 precursor (EC 1.7.2.2) Nitrite to ammonia 2,418 104
Nitric-oxide reductase (EC 1.7.99.7) Nitric oxide to nitrous oxide 3,067 134
Nitrous-oxide reductase (EC 1.7.99.6) Nitrous oxide to dinitrogen 1,027 49
Hydroxylamine reductase (EC 1.7.3.4) Hydroxylamine to ammonia and water 4 3
Sulfur
Sulfate adenylyltransferase (EC 2.7.7.4) Transfer of the adenylyl group from ATP to inorganic sulfate,
generating adenosine 50-phosphosulfate and
pyrophosphate.
7,133 884
Adenylyl sulfate kinase (EC 2.7.1.25) Catalyze the synthesis of activated sulfate 2,144 276
Phosphoadenylyl sulfate reductase (EC 1.8.4.8) Reduction of activated sulfate into sulfite 413 122
Adenylyl sulfate reductase (EC 1.8.99.2) Adenosine 50-phosphosulfate (APS) to sulfite and AMP 1,186 50
Sulfite reductase (EC 1.8.99.1, 1.8.1.2 and 1.8.7.1) Sulfite to sulfide 1,559 305
Other
Oxidase stress catalase (EC 1.11.1.6) Hydrogen peroxide to water and oxygen 5,846 454
Peroxidase (EC 1.11.1.7) Oxidation of organic compounds 5.183 269
in conversion of sulfate into adenylylsulfate and to sulfite
and H2S, including sulfate adenylyltransferase (EC 2.7.7.4),
adenylylsulfate kinase (EC 2.7.1.25), phosphoadenylyl sulfate
reductase (EC 1.8.4.8), adenylylsulfate reductase (EC 1.8.99.2),
and sulfite reductase (EC 1.8.99.1, 1.8.1.2 and 1.8.7.1) (Table 2).
Enzymes mediating reduction of sulfate and adenylylsulfate
into H2S dominated sulfur metabolism in CPS (Figure 6C).
Generated H2S can influence the reductive carboxylate cycle
(CO2assimilation) and might be released by volatilization,
producing the typical smell of mangrove swamps (Andreote
et al., 2012). Sulfate-reducing bacteria obtain energy by oxidizing
organic matter or hydrogen using sulfate (or other sulfur
molecules) as electron acceptors, yielding H2S. They are prevalent
in environments such as swamps and standing waters that
have low oxygen. Sulfur-reducing bacteria and some archaea
are similar, but they use elemental sulfur as an electron
acceptor, and they also produce H2S. During catabolism of
organic matter, hydrogen sulfide is also released by other
anaerobic bacteria when sulfur-containing amino acids are
digested. In CPS, Deltaproteobacteria was the most abundant,
possibly indicating the importance of sulfate reduction in this
environment (Wrighton et al., 2014).
Overall, the metabolism of carbon, nitrogen, and sulfur
are interlinked within the microbial population, and this is
particularly true for the metabolism of sulfur and carbon. The
abundance of organic matter in the anaerobic environment of
CPS yields an optimal environment for several anaerobic bacteria
such as sulfate-reducing bacteria and methanogens (Dar et al.,
2008). These groups share similar environmental niches, and
their relative abundance is controlled by substrate availability
(Oremland and Polcin, 1982). Simple substrates are important
for methanogens, while sulfate-reducing bacteria are capable of
degrading more complex substrates, including long chain and
aromatic hydrocarbons (Muyzer and Stams, 2008).
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Nho et al. Catfish Aquaculture Pond Sediment Metagenome
FIGURE 6 | Part of a SEED-based functional analysis of the CPS metagenome. (A) Carbon fixation and methane metabolism; (B) nitrogen metabolism; and (C)
sulfur metabolism. Blue boxes are proteins that were represented in CPS.
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Nho et al. Catfish Aquaculture Pond Sediment Metagenome
TABLE 3 | Proteins in the CPS metagenome in the resistance to antibiotics and
toxic compounds (RATC) category.
Function No. of No. of
sequences∗hits†
Adaptation to D-cysteine (catalyze the
transformation of D-cysteine into pyruvate, H2S,
and NH3)
63 41
Aminoglycoside adenylyltransferases (confers
resistance to kanamycin, gentamicin, and
tobramycin)
13 12
Arsenic resistance 5,506 598
Beta-lactamase (inactivates beta-lactam
antibiotics including penicillins and
cephalosportins)
5,912 1112
Bile hydrolysis 97 47
BlaR1 family regulatory sensor (controls
expression of beta-lactamase)
9,705 1,257
Cadmium resistance 195 65
Cobalt-zinc-cadmium resistance 58,584 3,252
Copper homeostasis 12,481 1,788
Erythromycin resistance 352 147
Mercury resistance operon 360 67
Methicillin resistance in Staphylococci 3,029 656
MexE-MexF-OprN multidrug efflux system 499 67
Multidrug resistance efflux pumps 23,910 1,881
Resistance to vancomycin 129 64
Resistance to chromium compounds 519 106
Resistance to fluoroquinolones 21,073 1,744
The mdtABCD multidrug resistance cluster 954 204
Zinc resistance 5,771 204
∗The number of sequences that contain a given annotation. †No. of hits refer to
the number of unique database sequences that were found in the similarity search
without double counting.
Genes encoding resistance to antibiotics and toxic compounds
(RATC) represents a subset of virulence genes that made up
3.45% of the classified metagenome in CPS. By comparison,
genes encoding RATC proteins generally make up ∼2–2.24%
of the classified metagenome in other aquatic ecosystems.
Compared to other aquatic sediments, the CPS metagenome
encoded a higher proportion of proteins in copper homeostasis,
cobalt-zinc-cadmium resistance, multidrug resistance efflux
pumps, and resistance to fluoroquinolones; however, the CPS
metagenome encoded a lower proportion of arsenic resistance,
beta-lactamase, erythromycin resistance, methicillin resistance,
and resistance to vancomycin (Table 3). In particular, genes
encoding cobalt-zinc-cadmium energy-dependent efflux pump
had the highest proportion in the RATC category in CPS.
By contrast, the fish gut microbiome encodes a much higher
proportion of mercury resistance, mercuric reductase, and
cobalt-zinc-cadmium genes than other metagenomes (Durso
et al., 2012). Fluoroquinolone is of particular interest regarding
the use of antibiotics in animal agriculture. Two enzymes
are the principal targets for the antibacterial activity of
fluoroquinolone: DNA gyrase and topoisomerase IV, which
introduce negative supercoiling and prevent the accumulation
of excess supercoiling (Collignon and Angulo, 2006). The
DNA gyrase subunit B gene and topoisomerase IV subunit A
were most frequently associated with Clostridia,Actinobacteria,
Bacteroidetes and with Proteobacteria in the CPS metagenome,
but whether they carry mutations associated with resistance
is not currently known. Beta-lactamase genes were most
frequently associated with Alpha- and Gammaproteobacteria,
which encode resistance to beta-lactam antibiotics (penicillins
and cephalosporins). The true risk to public health from
antimicrobial use and subsequent resistance in aquaculture is
speculative. However, the presence of antibiotic resistance genes
and elements in pond sediments is a threat to public health
if the resistance genes are transferrable to clinically significant
pathogens.
Of these RATC categories, beta-lactamase resistance,
multidrug resistance efflux pumps, fluoroquinolone resistance,
cobalt/zinc/cadmium resistance, and acriflavine resistance genes
are present in other metagenomes such as lake sediment, soil,
feces, and marine environment (Durso et al., 2012). This broad
distribution across agricultural, environmental, and human-
associated samples indicates that these mechanisms are generally
distributed and suggests they are functionally important in
diverse habitats.
CONCLUSION
Catfish pond sediment is a highly eutrophic, nutrient-rich,
and diverse ecosystem that has significant effects on the
physiochemical parameters of pond dynamics. This study is a
pacesetting metagenomics analysis using Illumina sequencing for
sediment in an intensive aquaculture system, and it revealed
significant coupling between phylogeny and functional potential.
The community structure suggests that the distribution of
particular taxa is driven by their metabolic capabilities in
response to the environment. For example, Deltaproteobacteria
was the most abundant class, which is unique in the CPS
metagenome compared to other aquatic sediments, and it
demonstrates capability of anaerobic hydrocarbon metabolism.
Functionally, our analysis revealed that the metagenome likely
has significant impacts on nitrogen, phosphorus, and sulfur
dynamics in catfish production ponds.
Further work to assess pond sediment could yield a complete
description of the potential metabolic pathways in the CPS
metagenome. The current work establishes a critical baseline
for the CPS metagenome for comparison with other distinct
environments. Also, future work could assess the effects
of environmental changes (for example, feeding changes or
antimicrobial use) in catfish production ponds on the sediment
metagenome.
AUTHOR CONTRIBUTIONS
ML and MM supervised the study. ML, MM, and AK designed
the experiments. HA, SN, SP, DP, and MM performed the
experiments. SN, SP, and ML analyzed and interpreted the data.
All authors wrote and approved the manuscript.
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fmicb-09-02855 November 20, 2018 Time: 15:9 # 11
Nho et al. Catfish Aquaculture Pond Sediment Metagenome
FUNDING
This project was supported by the U.S. Department of
Agriculture-Catfish Health Research Initiative (MIS-371660) and
the Mississippi State University College of Veterinary Medicine.
ACKNOWLEDGMENTS
We thank Dr. Zhao Nan (Alan) for assistance in data analysis. We
also thank Amanda Cooksey for help in library preparation and
sequencing.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2018.02855/full#supplementary-material
TABLE S1 | Sequence data from the CPS metagenome.
TABLE S2 | Functional profiles (COG and KEGG) for genes identified in the CPS
metagenome.
TABLE S3 | Relative abundances of bacteria by systematic classifications within
the CPS metagenome.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
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Copyright © 2018 Nho, Abdelhamed, Paul, Park, Mauel, Karsi and Lawrence. This
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Frontiers in Microbiology | www.frontiersin.org 12 November 2018 | Volume 9 | Article 2855
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