4 | Discussion
In this study, our main objectives were to assess the level of
correspondence between the taxonomic and functional profiles derived
from amplicon-based 16S rRNA metabarcoding data and shotgun
metagenomics, evaluate the strengths and weaknesses of both approaches,
and determine whether functional profiles from HSP methods can be used
as a substitute to metagenomics for monitoring functional changes
associated with fish farming in marine environments.
The overall taxonomic richness recovered by 16S rRNA metabarcoding was
up to 20-fold higher (depending on the taxonomic level) than
metagenomics. This is due, in part, to the differences in the ability of
identifying sequences of different lengths (~ 450 bp
[metabarcoding] versus ~ 150 bp [metagenomics])
and by differences in the taxonomic assignment methods used. However, it
is also well recognized that metagenomics requires much more sequencing
effort than metabarcoding to reach equivalent 16S rRNA coverage as it
captures all DNA material (Cottier et al., 2018; Singer, Shekarriz,
McCarthy, Fahner, & Hajibabaei, 2020). This is especially problematic
in highly diverse communities such as the marine sediment samples
assessed in this study. Small differences in taxonomic composition were
anticipated because 16S rRNA primer sets are never truly universal
(Pollock et al., 2018). While both datasets were relatively similar in
terms of dominant Phyla and Families, several taxa such as BD2-2
(Bacteroidetes), Sulfurovaceae, Thermoanaerobaculaceae and
Rubritaleaceae were more predominant in the metabarcoding data. These
results clearly illustrate the advantage of using targeted amplicon 16S
profiling over metagenomics when it comes to providing a comprehensive
overview of communities in complex environments, although biases due to
preferential PCR amplification and primer specificity are inevitably
introduced.
Despite the differences in taxonomic richness, functional richness
between the HSP methods and metagenomics was relatively similar. This
counter intuitive result occurs because most ECs/KOs are typically
redundant across microbial communities (Louca et al., 2018; Starke et
al., 2020), many of which performing functions that are essential to
cellular activities. Additionally, several non-ubiquitous functions, and
especially those occurring at shallow phylogenetic depth, are difficult
to accurately predict (Martiny et al., 2015) and may therefore be
omitted by HSP methods (Bowman & Ducklow, 2015). These false negatives
were prominent for Paprica, which had the lowest specificity.
The opposite scenario where functional richness is artificially
increased due to false positive predictions is also a possibility. For
example, phylogenetic plasticity and genomic variability can result in
loss of functions within taxa that can’t be detected by HSP methods.
However, because of the substantial differences in terms of recovered
taxonomic diversity between metagenomics and 16S metabarcoding methods
and limits imposed by sequencing depth, it is also possible that some
functions predicted by the HSP methods were not detected by
metagenomics. As such, the true sensitivity of the HSP methods, which
was lowest for Tax4Fun2 and highest for paprica, was
likely underestimated.
In general, we observed little correlation and/or no significant
difference with the null expectation datasets based on the abundance of
functions shared between HSP methods and metagenomics. Other studies
have reported weak correlations of HSP derived pathways abundance with
metagenomics when compared to null expectation datasets, with decreasing
performance for more complex and/or less characterized environments
(Douglas et al., 2019; Sun, Jones & Fodor, 2020). These low
correlations could be due to preferential amplification of certain DNA
sequences, primers biases, and varying gene copy numbers of 16S rRNA per
taxa, although HSP methods typically try to correct this bias (Bowman &
Ducklow, 2015; Wemheuer et al., 2018; Douglas et al., 2019). The
increased detection sensitivity of 16S rRNA metabarcoding can also
create a bias in the number of contributing taxa to certain functions,
which can negatively affect correlations with functions derived from
metagenomics. Considering that amplicon-based 16S rRNA metabarcoding and
metagenomics uncovered a substantially different bacterial diversity,
the weak correlation in functional abundance between the two methods is
expected.
An alternative and possibly more appropriate approach to comparing
functional profiles of HSP and metagenomics approaches is by contrasting
their correlation with metadata (Sun et al., 2020) or by evaluating the
correspondence between the ordination of their functional communities.
Using procrustes analyses, there was a very strong and significant
correlation between HSP methods and metagenomics, especially when using
presence/absence data. The ASV communities showed no significant
relationship with the functional profiles, suggesting that the taxonomic
and functional communities were influenced differently by the biological
and/or environmental conditions. We also tested the correspondence of
the bacterial taxonomic and functional profiles with macrofaunal
communities and physico-chemical data. While both profiles correlated
relatively well to physico-chemical data (r ≥ 0.5), with the EC-based
data performing best, only functional profiles correlated strongly and
significantly with the macrofaunal communities. A higher association of
functional versus taxonomic beta-diversity with macrofaunal data was
also reported by Laroche et al. (2018), which suggest that interactions
between these communities are especially driven by microbial metabolic
capabilities rather than specific phylogenetic association.
The sensitivity of the different datasets in detecting the effect of
fish farm activities was evaluated by comparing changes in community
composition between near-field (0 m from pen) and far-field samples
(>= 1,200 m from pen). In general, we found higher
sensitivity for the HSP methods, and especially for Paprica and
Tax4Fun2, compared to metagenomics and ASV communities. These
results indicate that despite the lower accuracy and increased detection
sensitivity of HSP methods, they may be more accurate in assessing how
microbial communities respond to environmental changes than
metagenomics. This is likely enhanced when complex microbial communities
are present, such as in marine sediments. The results improved when
transforming functional abundance data, including those of metagenomics,
to presence/absence data, as it reduced within group variability. In
addition, we observed higher stochasticity of microbial taxonomic shifts
in response to a contamination gradient compared to functional community
changes. This observation has also been reported by Hornick & Buschmann
(2018), Laroche et al. (2018) and Ren et al. (2016) and is likely due to
several taxa sharing the same metabolic capabilities and their
succession in the ecosystem may have less to do with environmental
changes than with biological properties (e.g. growth cycle and bacterial
interactions) and geo-topographic factors (e.g. depth and geographic
distance). As such, functional profiles may be slightly more robust and
sensitive in detecting environmental alterations caused by fish farm
activities, although further research is needed to properly test this
assumption.
Benthic environments under cage fin-fish aquaculture are usually
enriched in organic waste and nutrients such as phosphorus and nitrogen
compounds (from faeces and uneaten fish feed for example), which can
lead to eutrophic conditions, microbial anaerobic activities and the
production of ammonia and hydrogen sulfide gasses (Brooks & Mahnken,
2003; Buschmann et al., 2006; Valdemarsen, Kristensen & Holmer, 2009;
Wang et al., 2012). In the present study, we were particularly
interested in comparing the response of classes of pathways associated
to the nitrogen and sulfur cycles between the pen and reference sites,
and between the metagenomics and HSP-based data. Overall, results from
both approaches were very similar, with an increase near the pens of
pathways associated to nitrate reduction and glycosaminoglycan
degradation, and a decrease of pathways affiliated to allantoin
degradation and sulfur oxidation. Additionally, the Paprica
analysis showed a decrease in pathway abundance associated with sulfur
reduction, sulfite and sulfide reduction, and an increase of
dimethylsulfide degradation near the pens. While we expected pathways of
nitrate-reduction to be in higher abundance near the fish farms, due to
enriched nutrients and possibly anoxic conditions, it was somewhat
surprising that pathways associated with sulfur compounds were less
abundant in both the metagenomics and HSP datasets. However, sulfite
respiration was found to be associated with the pens when using
taxonomic information of the 16S rRNA data and literature-based
functional association (Faprotax methodology). It is likely
that certain pathways associated to the sulfur cycle, such as sulfite
oxidation and reduction, were indeed more prominent near the pens but
were not fully picked-up by metagenomics and HSP-based functional
profiling, possibly due to the incompleteness of reference databases.
For example, pathways associated to sulfite respiration were absent from
both the Humann2 and Picrust2
datasets. Glycosaminoglycan degradation is responsible for the
degradation of long linear polysaccharides made of repeating
disaccharide units, also referred to as mucopolysaccharides (Ernst et
al., 1995). It is probable that high quantities of mucopolysaccharides
originate from mucus produced and excreted by the caged salmon (see
Reverter et al., 2018; Jacobsen et al., 2019) and this is being
catabolized by a specialized group of bacteria. Allantoin represents a
product of uric acid, an important metabolic intermediate compound
produced by both animals and bacteria. Under limited nutrient
conditions, allantoin can be degraded into ammonia by some bacteria, to
serve as a secondary source of nitrogen (Switzer et al., 2020).
Correspondingly, our results suggest that pathways associated to
allantoin degradation are less abundant near the pens, where strong
nutrient enrichment occurs. Overall, these results show congruence
between metagenomics and HSP methods for the classes of pathways of
particular interest, and highlight both the potential and caveats of the
current functional profiling methods in providing further understanding
of the metabolic and environmental changes occurring in benthic
ecosystems. Further research involving more samples and taking into
account regional and temporal variability is needed to confidently
identify potential functional indicators of fish farm ecological impacts
that could be eventually integrated into benthic health indexes.
Collectively our results suggest that the lower specificity of HSP
methods may be offset by the ability of amplicon-based metabarcoding to
provide a much more exhaustive assessment of the taxonomic community,
and hence of functions that have low genomic variability. This allows
HSP methods to provide functional profiles that are relatively similar
to those of metagenomics, and which respond similarly to environmental
changes. Although the accuracy and sensitivity of HSP methods are still
strongly affected by the incompleteness of reference databases, our
results demonstrate that they provide a useful functional profiling
alternative to metagenomics, and a valuable tool in detecting and
evaluating the effects of salmon farming on benthic ecosystems.