FEMS Microbiology Ecology, 92, 2016, fiw005
doi: 10.1093/femsec/fiw005
Advance Access Publication Date: 5 January 2016
Research Article
RESEARCH ARTICLE
Are freshwater bacterioplankton indifferent to
variable types of amino acid substrates?
Monica Ricão Canelhas, Alexander Eiler and Stefan Bertilsson∗
Department of Ecology and Genetics, Limnology and Science for Life Laboratory, Uppsala University, SE-75236,
Uppsala, Sweden
∗
Corresponding author: Department of Ecology and Genetics, Limnology and Science for Life Laboratory, Uppsala University, Norbyv. 18D, SE-75236,
Uppsala, Sweden.
Tel: +46-18-4712712; E-mail: [email protected]
One sentence summary: Complex bacterioplankton communities are versatile at taking up different substrates, bioavailabe low-molecular substrates
such as amino acids did not select for specialized populations in this study.
Editor: Riks Laanbroek
ABSTRACT
A wide range of carbon compounds sustain bacterial activity and growth in freshwater ecosystems and the amount and
quality of these substrates influence bacterial diversity and metabolic function. Biologically labile low-molecular-weight
compounds, such as dissolved free amino acids, are particularly important substrates and can fuel as much as 20% of the
total heterotrophic production. In this study, we show that extensive laboratory incubations with variable amino acids as
substrates caused only minimal differences in bacterial growth rate, growth yield, quantitative amino acid usage,
community composition and diversity. This was in marked contrast to incubations under dark or light regimes, where
significant responses were observed in bacterial community composition and with higher diversity in the dark incubations.
While a few individual taxa still responded to amendment with specific amino acids, our results suggest that compositional
shifts in the specific supply of amino acids and possibly also other labile organic substrates have a minor impact on
heterotrophic bacterioplankton communities, at least in nutrient rich lakes and compared to other prevailing
environmental factors.
Keywords: freshwater bacteria; amino acid substrates; light regime; community composition; substrate specialization
INTRODUCTION
Dissolved organic matter (DOM) represents an important energy and nutrient source for bacteria in most aquatic ecosystems (Pomeroy 1974; Tranvik 1988). Depending on the ecosystem, DOM can be of either autochthonous (internally produced)
or allochthonous (externally produced) origin, both pools featuring high chemodiversity (Schimel and Gulledge 1998; Kellerman
et al. 2014). The diverse array of compounds that jointly makes
up the combined DOM pool can conceptually be classified into
high-molecular-weight (HMW) or low-molecular-weight (LMW)
compounds according to their molecular size with 1 kDa as a
commonly used cutoff (Benner et al. 1992). The bioavailability of
the respective fraction has been a topic of ongoing debate, where
the most recent findings indicate that the HMW fraction appears
to be overall more biologically reactive compared to the LMW
pool (Benner and Amon 2015). However, although some LMW organic carbon compounds build up to measurable concentrations
in situ because of their recalcitrance, a great variety of abundantly produced metabolites and other biomolecules are readily and rapidly assimilated and/or metabolized by heterotrophic
bacteria (Amon and Benner 1996). Such labile LMW compounds
are often the dominant fuel sustaining bacterial metabolism
in freshwater lakes (Berggren et al. 2010), but because of their
Received: 22 October 2015; Accepted: 4 January 2016
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
lability, they rarely build up to high in situ concentrations
(Bertilsson and Jones 2003).
Dissolved free amino acids (DFAAs) are among the LMW compounds that have been studied most extensively in aquatic environments. This is partly because amino acid (AA) polymers
(proteins and peptides) make up a large proportion of cellular biomass, but also because sensitive and specific detection
methods are available (Munster 1993). Earlier studies have
shown that DFAAs can contribute to as much as 20% of total
bacterial production in natural environments (Kirchman 2003;
Bertilsson et al. 2007). These LMW substrates do not only represent a source of nutrients (carbon, nitrogen, sulfur) but can
also be used for catabolic processes and in this way directly influence bacterial growth efficiency (del Giorgio and Cole 1998;
Alonso and Pernthaler 2006). Other support for the quantitative
significance of AAs in aquatic systems is that carbon in total
hydrolysable DFAAs makes up between 6% and 23% of the total organic carbon in marine sediments, while nitrogen in these
AAs at the same time represents 30%–40% of the total nitrogen
(Burdige and Martens 1988; Ingalls et al. 2003). Studies of AA contribution to sediment organic carbon are scarcer for freshwaters, but appear linked to the predominant source of the organic
carbon (allochthonous vs autochthonous) with AA contribution
to autochthonous systems ranging from 8% to 20% (Kemp and
Johnston 1979; Yao et al. 2012). Furthermore, the flux of carbon
and energy through the AA pool seems to increase in ecosystems
with overall higher productivity, such as in fertile soils (van Hees
et al. 2005).
Many groups of organisms interactively contribute to the cycling of organic carbon in aquatic ecosystems, but in the pelagic
zone of lakes, bacteria almost always dominate this process
(Jonsson et al. 2001; Wetzel 2001). For freshwater ecosystems
specifically, the limited information available suggests that heterotrophic bacteria are versatile in carbon substrate use, with
widespread ability of populations to use more than one type of
substrate (Buck et al. 2009; Salcher, Posch and Pernthaler 2013;
Salcher 2014). Metabolic traits, such as the ability to use a certain organic substrate, can also be shared by multiple populations that are not necessarily phylogenetically closely related
(Kirchman 2003; Martiny, Treseder and Pusch 2013). It has been
hypothesized that the most abundant phylogenetic groups are
also the main mediators of carbon substrate uptake, compared
to more sparsely occurring groups. However, there is growing evidence for disproportional roles of certain groups in processing
of certain types of dissolved organic compounds, implying that
also minor populations within communities could be of major
importance in controlling substrate turnover (Cottrell and Kirchman 2000; Salcher, Posch and Pernthaler 2013).
Available data to date suggest that an increased availability
of DFAAs in lake water favor members of the Alphaproteobacteria
and Betaproteobacteria, who at least at the class-level seem to be
versatile with regard to the types of carbon compounds that can
be used for growth (Schweitzer et al. 2001; Alonso-Sáez and Gasol
2007; Salcher, Posch and Pernthaler 2013). There is however also
evidence for substrate specialization among even closely related
phylogenetic lineages, exemplified by two clades of the Betaproteobacteria group (PnecC and R-BT065) that showed strong preference for specific low-molecular-mass compounds (Buck et al.
2009).
Recent efforts to metabolically characterize abundant freshwater populations have led to a better understanding of their
biogeochemical roles and have also provided insights as to
why certain populations are more successful and reach higher
abundances under given conditions (Ghylin et al. 2014; Salcher
2014). This type of information can be obtained in a number
of ways, with substrate-tracking microautoradiography coupled
to fluorescence in situ hybridization as one versatile but timeconsuming tool (Wagner et al. 2006; Alonso-Sáez and Gasol 2007;
Salcher, Posch and Pernthaler 2013). While these techniques are
under constant development to target the biogeochemical potential of an increasing number of populations (Wagner, Horn
and Daims 2003; Wagner and Haider 2012; Wright et al. 2014), the
extrapolation of such population traits to realized community
function is still not well developed. Not much is known about
the effects of specific compounds on the function and composition of entire complex and interacting microbial communities
(Peter et al. 2011) even if this has been suggested as one of several important factors structuring the composition of bacterial
communities in lakes (e.g. Crump et al. 2003; Eiler et al. 2003;
Stepanauskas et al. 2003; Jardillier et al. 2004; Lennon and Pfaff
2005)
The aim of the present study was to assess the response
of freshwater bacterial communities and populations to experimental amendments with contrasting but structurally similar
labile LMW organic substrates. We hypothesized that the addition of different DFAA would cause a change in bacterial community composition by selecting for bacterial groups that have
higher affinity for the respectively added AA, at the expense
of less able competitors. To test this, we repeatedly added low
amounts of different DFAAs to separately incubated lake water;
we followed the response in the bacterioplankton community
with regard to diversity (alpha and beta diversity measures) and
function (AA consumption and total community growth characteristics). In addition, we traced the responses of individual
populations in order to identify populations specialized on different AAs. Such taxa specialized on specific labile LMW substrates could play a key role in the cycling of carbon in lakes.
MATERIALS AND METHODS
Experimental setup
Water for incubations was collected on three separate occasions
(May, June and July, 2012) from 0.1 m depth at a near-shore site
in eutrophic Lake Ekoln, located in central Sweden (Eiler and
Bertilsson 2004). The water was filter sterilized by sequential
tangential flow filtration, using a Minitan Ultrafiltration System
(Millipore) assembled with two membranes (HVLP 0.45 μm and
GVLP 0.2 μm) connected to a second tangential flow ultrafiltration unit (Pellicon XL Biomax 50 kDa). Filter-sterilized water aliquots from the different dates were combined to a single batch for use in the experiment. Dissolved organic carbon
(DOC) in the filter-sterilized lake water was measured with a
Sievers 900 TOC analyzer (GE Healthcare, Boulder, CO, USA). A
bacterial inoculum for the experimental incubations was collected in August from the same lake, and was subject to vacuum filtration (under 3.5 psi lb/in2 ) through a GF/F glass fiber
filter (Whatman, Maidstone, Kent, UK) to remove larger eukaryotic cells and other potential predators. Prior to use, the filter had
been precombusted at 450◦ C for 4 hours to remove any organic
contaminants. Incubations (10 mL) were carried out in sterile
and acid-washed 60 mL capped glass tubes with four replicate
cultures for each treatment. The substrates used for experimental amendments were exclusively L-AAs (Sigma Chemical Company, St Louis). The following individual AAs were added to separate treatments with four replicates each: serine, aspartic acid,
valine, glutamic acid, threonine, arginine, glycine and phenylalanine. The added AAs varied with regard to side chains and
Canelhas et al.
3
Table 1. Classification of AAs according to their side chains and carbon to nitrogen ratio (C:N) and mean with standard deviation (with n > 2)
of consumed AAs from the 250 μg C/L added to every bottle at the end of the final 100 mL batch incubations.
L-Aminoacid
Classification
Serine
Aspartic acid
Valine
Glutamic acid
Threonine
Arginine
Glycine
Phenylalanine
Mix1∗
Mix2∗
Polar-neutral side chain
Acidic
Aliphatic
Acidic
Polar-neutral side chain
Basic
Hydrophobic
Aromatic
Undetermined
Undetermined
∗
C:N ratio
Consumed AA (μg C/L) light
Consumed AA (μg C/L) dark
3
4
5
1.25
4
1.5
2
9
6.5
3
231.1 ± 31.5
240.1 ± 8.5
241.7 ± 6.8
248.8 ± 0.3
247.1 ± 1.7
224.4
nd
245.5
246.3
234.8 ± 6.41
238.8 ± 11.5
248.6 ± 0.8
248.3 ± 0.6
245.1 ± 6.9
246.7 ± 5.9
nd
248.5 ± 1.4
248.9
243.4
234.2 ± 0.5
Mix1 (Arg, Phe, Thre), Mix 2(Gly, Asp, Ser).
carbon to nitrogen ratio (Table 1). Additionally, two AA mixtures
with arbitrarily selected AAs from each category of charged, polar and hydrophobic side chains were added at the same total
concentration to an additional set of cultures to test whether a
combination of compounds enhances assimilation and growth
(Jørgensen et al. 1993): mixture 1 (arginine, alanine, threonine),
and mixture 2 (glycine, aspartic acid, serine). Incubations were
inoculated with 105 cells as determined by flow cytometry. Each
of the 10 mL experimental cultures was amended with AAs contributing 0.25 mg C L−1 . A total of 88 parallel batch cultures
were maintained, representing 10 different AA amendments.
Controls without amended AAs were inoculated and incubated
in parallel. To verify that no contamination occurred during
cultivation, several treatments were incubated without added
bacteria.
The experimental cultures were incubated without shaking
at 20◦ C. This was the daytime in situ temperature for the lake
in June and July. Contrasting light regimes were used as an additional variable as it has been shown in previous studies to
change the bacterial community composition (Aasl et al. 1996;
Church, Ducklow and Karl 2004). The cultures were kept under
either continuous dark conditions or under continuous low fluorescent light with photosynthetically active radiation (PAR) of
6.16 W/m−2 measured using a Black Comet spectrometer (Black
Comet, BLK-C, StellarNet, Tampa, Florida). This corresponds
to 5% of the total solar irradiance on a sunny day in central
Sweden and is similar to the irradiance level at 5–6 m depth in
Lake Ekoln during a clear summer day (data not shown). Each
biological replicate was subjected to four repeated 6–8 day dilution culture incubations to stationary phase, followed by serial transfers. Each transfer represented a population bottleneck,
since the previous culture was diluted 10-fold into fresh filtersterilized medium with the same AA amendment as the original cultures. After the fourth incubation, 5 mL from each culture
were transferred to 100 mL fresh medium in 500 mL cultivation
flasks and incubated for 7 days under analogous conditions. The
repeated transfers and additions of the different AAs, thus, represented a selection of populations to test for a significant effect
of the specific substrates added on the evolved bacterial composition and growth parameters.
Total and active bacterial cell counts
To follow the growth of bacterial communities in the experimental incubations, individual 10 mL cultures were sampled for cell
counts every second day. After the final transfer to the larger
volume, cultures were sampled daily. Volumetric cell counting
was performed from a volume of 50 μL with a flow cytometer
(FC) equipped with a 488 nm blue solid state laser (Cyflow Space,
Partec, Görlitz, Germany). Controls of media with no inoculum
were always analyzed in parallel showing less than 5% signal
noise. Cell counts were analyzed using Flowing Software version 2.5 (Perttu Terho, Centre for Biotechnology, Turku, Finland).
For total bacterial abundance counts, cells were first fixed, with
0.2-μm-filtered formaldehyde (2% - final concentration), and
then stored dark at 4 ◦ C for less than 48 hours prior to analyses.
Upon analysis, cells were stained with the fluorescent nucleic
R
acid stain Syto13
(Molecular probes, Invitrogen, Carlsbad, CA,
USA) (del Giorgio et al. 1996) and green fluorescence was used
as trigger during measurement. The cell concentrations were
then used to calculate the growth rates using the slope of the
growth curve during exponential growth. The specific growth
rate (μ) was obtained using Equation 1, where change in bacterial abundance is calculated using the initial abundance (N0 ) and
the abundance at time t (Nt ). The growth yield was expressed as
the highest cell abundance obtained in each incubation:
Nt = N0 eμt
(1)
To measure the proportion of actively respiring cells, the final 100 mL cultures were additionally subject to cell counts afR
ter incubation with Redox Sensor Green
. Staining of cells from
fresh samples and incubation times were carried out as recommended by manufacturer (Baclight Redox Sensor Green Vitality
kit, Invitrogen). This vitality stain only yields a fluorescent signal
from bacterial reductase activity whereas unlike other vitality
dyes, Redox Sensor Green does not inhibit bacterial metabolism
(Kalyuzhnaya, Lidstrom and Chistoserdova 2008). Propidium iodide (provided in the redox sensor kit) was used to stain dead
and damaged cells.
AA measurements
The total concentration of free AAs in the 10 mL cultures
was monitored every second day by direct fluorescence after a
derivatization of the AAs with o-phthaldialdehyde (OPA; SigmaAldrich Corp., St Louis, MO). Bulk concentrations were measured
with a fluorescence microplate reader (Tecan Ultra 384, Austria GmbH), according to the method described by Jones, Owen
and Farrar (2002); NH4 + concentrations were measured separately to account for any interference with the OPA method
and accounted for in the analysis. The cultures were serial
transferred to fresh media with amended AAs when the AA
concentration became undetectable in the cultures. The final
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
concentration of individual AAs in the larger cultures was analyzed by high-performance liquid chromatography (HPLC) of
the OPA AA derivatives according to the Agilent application note
5990-4547EN.
Bacterial community composition
Bacterial cells from the final experimental cultures (90 mL) were
filtered onto 0.2 μm Supor 200 filters (Pall Corporation, Port
Washington, NY, USA). Filters were stored at – 80◦ C until extraction was performed using the Power Soil DNA isolation kit as
recommended by the manufacturer (MoBio Laboratories, Carlsbad, CA, USA). DNA was quantified by Nanodrop (Thermo Scientific). Amplicon sequences of bacterial 16S rRNA genes were obtained as previously described (Sinclair et al. 2015). In short, each
sample was first amplified in triplicate using bacterial primers
targeting the variable regions V3 and V4 (341F: CCTACGGGNGGCWGCAG and 805R: GACTACHVGGGTATCTAATCC). Triplicates
were pooled and used as template in a second PCR step using
the same primers additionally equipped with a unique 7 bp barcode at the 5 end of each forward and reverse primer. A total of
50 different barcoded primer pairs differing by a minimum of 2
bp were used. PCR products were purified using Agencourt AMPure XP (Beckman Coulter) as recommended by the manufacturer, and then quantified by fluorescence with the PicoGreen
assay (Quant-iT PicoGReen, Invitrogen). Subsequently, all amplicon samples were pooled in equimolar amounts (50 samples/pool) and once again purified using the Agencourt AMPure
XP kit. The pooled samples were sequenced at the SciLifeLab
SNP/SEQ next generation sequencing facility (located on Uppsala University campus) using Illumina MiSeq with 2×250 bp
chemistry.
Raw sequence data were analyzed with an in-house pipeline
for demultiplexing and sequence-pair assembly. Quality filtering removed any sequences with missing primers, or unassigned
base pairs, and discarded sequences with a PHRED quality score
below 5 or with an overlapping region longer than 100 base pairs.
Sequences were then assigned to operational taxonomic units
(OTUs) based on a 97% identity clustering with UPARSE (Edgar
2013). Taxonomic annotation was performed using the SilvaMod
database provided by the online resource SILVA. For further information on the quality control and annotation pipeline, see
Sinclair et al. (2015). The raw sequence data were deposited at
Genbank SRA under accession number SRP063825.
Data analyses
All statistical analyses were performed in R (R Core Team 2014).
The packages vegan (Oksanen et al. 2015), ggplot2 (Wickham
2009), spaa (Zhang 2013), outliers (Komsta 2011) and edgeR
(Robinson, McCarthy and Smyth 2010) were used. Within edgeR,
the statistical method applied for differential gene expressions
was used to rank OTUs consistently present in replicates as representative for a treatment (Robinson and Smyth 2008).
For alpha diversity analysis, only samples with more than
5000 reads were used (>86% of the samples) represented by a
subset of 78 samples that were rarefied to the sample with the
smallest size. To measure richness, the bias-corrected Chao1,
ACE (abundance-based coverage estimator) and Pielou’s species
evenness estimates were calculated. For beta diversity, all samples were included in the analysis, samples were rarefied to the
sample with the lowest number of sequences encountered in
any sample (920) that was considered within the range for robust beta-diversity analysis (Lundin et al. 2012). The Bray–Curtis
dissimilarity calculation was used to visualize similarity in community composition (beta diversity). As the variability in com-
munity composition measured between the replicates for the AA
treatments was high, we plotted a dendrogram derived from the
Bray–Curtis dissimilarity (Fig. 2) used in combination with furthest neighbor-joining algorithm. We displayed branching uncertainty by computing multiscale bootstrap resampling for approximated unbiased (au) p-values (bootstrap = 10 000). To test
for treatment effects on bacterial community composition, the
OTU table was subsampled to the lowest number of reads (920).
The total number of OTUs was 307 and this dataset was used
to test for effects of the experimental treatments using permutational MANOVA (PERMANOVA, function adonis, vegan package, permutations = 999). We also tested the effect of AA treatment on the common to rare subset of the OTUs to verify that
the most abundant OTUs did not mask patterns in more rare
OTUs. Rare OTUs were classified as having relative abundance
lower than 1% (Lindh et al. 2015) and consisted of 294 OTUs, representing 95.7% of the OTUs. We estimated the dispersion of our
data to avoid outlier-driven results and then ranked OTUs according to their significant association to particular treatments.
By using the tagwise dispersion function (edgeR, R package),
we could rank the OTUs according to their consistency among
replicates from individual AA treatments, and then analyzed
which OTUs differed significantly between the light and dark
treatment. Differences were considered statistically significant
at p < 0.01. By using a generalized linear model, we tested
for differential representation of OTUs between samples using
the Toptag function, an analysis quite similar to an ANOVA.
The analysis applies a log2 -counts per million (logCPM) that is
used for estimating relative representation in the community,
where a low value within a range from 1 to 100 is considered
high relative abundance. The analysis also reports logFC that is
the x-fold change in OTU contribution to the community. The
change in log2 CPM gives a measure of the consistency of the
replicates.
Additionally, habitat breadth was estimated for OTUs as a
measure of the degree of specialization (Levins 1968). The Levins
niche breadth was calculated using R package spaa, which is
based on the sum of the proportion (Pij ) of contribution of species
(i) in different habitats (j) given by
Bij = N
1
i=1
Pij 2
Species with a higher B-value (breadth) were considered generalists whereas low B-values indicate specialist substrate acquisition strategies. The species in this case are the assigned
OTUs and the habitats the different AA treatments. For this analysis, we considered all OTUs above 3.16×10−4 in order not to
bias the assigned specialists towards the rare subset of the community (Logares et al. 2013). Based on inspection of the niche
breadth values, the arbitrarily defined limits for ‘generalists’
were set to OTUs with B > 25 and for specialists it was set to
OTUs with B < 5. To identify OTUs with a preference for certain
AA treatments, an indicator value analysis was performed (indval function package labdsv; Roberts 2013). This analysis is based
on the specificity of an OTU or its dominance (fidelity) within a
particular treatment.
RESULTS
Growth patterns and AA concentrations
The lake water at the start of the incubation had a total DOC
concentration of 11.5 mg L−1 . Bacterial growth in dark cultures
followed typical dilution culture growth characteristics with a
Canelhas et al.
5
Table 2. Summary of statistics analysis (anova) of treatment effects on growth in cultivations.
Effects
Df
Sum sq
Mean sq
F value
Pr (>F)
AA on growth rate
AA on growth yield
Ligh/dark on growth yield
Light/dark on growth rate
Residuals
10
10
1
1
66
3.11E + 10
1.95E + 12
3.91E + 12
4.17E + 10
2.19E + 11
3.11E + 09
1.95E + 12
3.91E + 12
4.17E + 10
3.31E + 09
0.938
0.599
10.748
12.608
0.504709
0.8093
0.00163
0.000714
Figure 1. Estimated richness (Chao 1) of the different AA treatments shown in box plot with dark treatments (gray boxes) versus the light treatment (white boxes) after
36 days of incubation.
short lag phase, exponential growth and stationary phase, bacterial abundance in cultures maintained in light stabilized after
the second transfer until transfer into the final 100 mL batch cultures (Figs S1 and 2, Supporting Information).
In the 100 mL batch cultures, stationary growth phase was
reached within 4 days for both light and dark incubations. Neither growth rate nor growth yield was significantly related to
the type of AA received by the community (Table 2). In contrast, bacterial growth characteristics differed significantly between the light (mean ± SD, growth rate = 1.65 × 105 d−1 ±
7.56 × 104 ) and dark incubations (mean ± SD, growth rate =
2.10 × 105 d−1 ± 5.16×104 ) (anova, p-value < 0.01) (Table 2)
with mean growth significantly higher in the ‘dark’ treatment
(t test, p < 0.01).
In contrast to the apparent indifference of the bacterial community to the specific AA amendment, the proportion of active
cells was significantly different between the ‘light’ and ‘dark’
treatments at the beginning and end of the 100 mL incubation
(paired t-test, p-value < 0.05). The proportion of active cells in
the light-exposed treatment was initially higher to then rapidly
decline, dark incubations featured a stable number of active cells
that gradually decreased to lower proportions over time (Table
S1, Supporting Information).
After the last incubation in the 100 mL batch cultures, analysis of specific AAs with HPLC showed that of the 250 μg C/L
added of each specific AA or AA mixture added, at least 90% had
been assimilated or degraded (Table 1). AA consumption did not
differ significantly between the AA treatments, but analogously
to cell counts, there was a difference in total amount of AAs after
the dark and light treatment (t test, p-value < 0.005). In the light
treatment, there was approximately twice as much AA remaining with the total mean of 9.1 ± 11.48 μg C in the light versus
4.09 ± 2.62 μg C in the dark treatment.
Responses in alpha and beta diversity
Sequencing of the 88 batch cultures yielded a total of 702 453
high-quality assembled amplicons (from here onwards called
reads) with counts ranging from 920 to 15 847 reads per sample.
For alpha diversity or richness estimates, we used the sequence dataset that grouped into 359 OTUs with a range of 17–80
OTUs per sample. The community alpha diversity did not differ
significantly between the contrasting AA treatments while there
was a significant difference in alpha diversity between light and
dark incubations, with higher estimated richness in darkness
(Fig. 1; permutational-anova, p-value < 0.01). Evenness, as determined by Pielou’s index, did not differ between any treatments
(Table S2, Supporting Information).
For beta diversity analyses, the subsampled sequence
dataset clustered into 307 OTUs (range 14–72 OTUs per sample). Contrasting AA additions did not cause significant differences in community composition as seen in the Permanova analysis where differences between AA treatments
were not considered significant (Permanova, F: 1.43, p-value:
0.013, R2 : 0.14). The same analysis revealed significant differences between light and dark treatments (Permanova, F: 8.72,
p-value: 0.0009, R2 : 0.086). The dendrogram derived from the
Bray–Curtis dissimilarity (Fig. 2) illustrates the high variability
between replicates, and that communities did not group according to AA treatments although Light and Dark clustering can be
seen.
To go beyond the influence of the abundant OTUs (>1% relative abundance, n = 4) on community composition, as inferred
by the Bray–Curtis distance measure, we performed additional
tests using only the rare subset of OTUs consisting of OTUs making up less than 1% of the total bacterial community. Also in this
case, the Permanova did not reveal any significant impact of the
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
contrasting AA amendments (Permanova, F: 1.16, p-value: 0.06,
R2 : 0.12).
Indicators and substrate specialists
To identify individual OTUs associated with specific AA amendments, we performed an indicator analysis. The indicator value
is based on specificity (relative abundance) and fidelity (relative
frequency) of the OTU relative to sample category. An OTU that
occurs predominantly in one of the treatments would thus indicate preference for the added AA. We found that only six OTUs
were characteristic for specific treatments (Table 3) although we
adjusted the p-value for multiple comparisons, false positives
or type II errors are considered a possibility. There is thus little
evidence for any OTUs being specifically associated with any of
the AAs added to the incubations.
From the tagwise dispersion analysis (Table 4), differential
representation of OTUs was not observed for the AA treatments.
For the light treatment, we could extract the following representative OTUs: Brevundimonas, Flavobacterium and Curvibacter.
Conversely, the dark treatment was characterized by members
of the Limnohabitans, Sphingobacteriaceae, Bacteriovorax, Planctomyces, Rhizomicrobium and Verrucomicrobia (OPB35 soil group).
An analysis of niche breadth further indicated whether individual OTUs display generalist or specialist patterns in their occurrences across the different treatments. If OTUs occurred in
many different AA treatments, they would have a higher niche
breadth (B-value) whereas specialists would occur in fewer treatments. To visualize the distribution of OTUs in the different
treatments, we plotted the niche breadth with the mean relative abundance of the OTUs in the sample (Fig. 3). The analysis
of the niche breadth results reveals that the AA treatments did
not significantly select for OTUs that were either specialists or
generalists (anova, F: 0.353, p-value: 0.962).
DISCUSSION
Figure 2. Dendrogram of the Bray–Curtis dissimilarity with furthest neighborjoining clustering, with approximately unbiased (au) p-values (%) calculated
from multiscale bootstrap resampling (n = 10 000) shown in black on each
branch. The AA treatments show the replicates (n = 4), with color coding, where
L and D represent light (gray) and dark (black) treatment.
As proteins are major biopolymers of all living cells, the AA
monomers forming these central molecules of life are believed
to be significant or dominant heterotrophic substrates in a
wide range of ecosystems (e.g. freshwaters; Guillemette and del
Giorgio 2011). Because of the fast and efficient biological uptake, free AAs rarely build up to high concentrations (Schweitzer
et al. 2001), but instead fuel the active microbiota at the base of
the food web (Bertilsson et al. 2007). The diverse AA molecules
share certain chemical features such as amine (-NH2) and carboxylic acid (-COOH) functional groups, but also differ in chemical properties according to side chains that are specific to each
AA species. This side-chain diversity could, at least hypothetically, select for specific bacterioplankton groups with uptake
mechanisms and cell metabolism tuned towards the specific
DFAAs.
By repeatedly adding different DFAAs to natural lake water,
we evolved bacterioplankton communities over five dilutionculture transfers. Despite this extensive pre-selection, we did
not detect any significant responses in community composition
or growth characteristics resulting from the variable DFAA substrates made available and thus found no support for our original hypothesis that a shift in the bacterial community would
occur towards a selection of populations with higher affinity for
specific AAs. This stands in stark contrast to the suggested impact of a wide range of biologically labile LMW organic compounds on diversity, growth characteristics and metabolism of
bacterial communities (Judd, Crump and Kling 2006; Kritzberg,
Canelhas et al.
7
Table 3. The Indicator species analysis test, for species that were different between AA treatments. In the table treatment signifies which AA
that was added to the sample. Indval is the index value of the strength of the association between the OTU and the sample. The p-value is
based on a permutational test (significance level: 0.01). Freq is the number of times the OTU appeared among samples.
Taxonomy
Treatment
indval
p-value
freq
α-Proteobacteria (Novosphingobium)
β-Proteobacteria (Paucibacter)
Bacteroidetes (Bacteroidales)
β-Proteobacteria (Polynucleobacter)
α-Proteobacteria (Rhodobacteraceae)
α-Proteobacteria (Rhizobiales)
Serine
Threonine
Valine
Valine
Control
Control
0.422
0.462
0.375
0.332
0.435
0.434
0.007
0.008
0.008
0.002
0.002
0.01
13
10
3
15
42
22
Table 4. Comparison of tagwise dispersion (edgeR, R package) analysis of the OTUs that were not representative for the AA treatment but that
were different between the light and dark treatment.
Taxonomy
logFC
logCPM
p-value
FDR
Regime
α-Proteobacteria (Brevundimonas)
β-Proteobacteria (Limnohabitans)
β-Proteobacteria (Limnohabitans)
Bacteroidetes (Sphingobacteriaceae)
Bacteroidetes (Flavobacterium)
β-Proteobacteria (Curvibacter)
δ-Proteobacteria (Bacteriovorax)
Planctomycetes (Planctomyces)
α-Proteobacteria (Rhizomicrobium)
Verrucomicrobia (OPB35)
4.958
–4.495
–2.168
–3.058
3.976
6.984
–6.622
–2.735
–2.667
–1.903
12.193
12.125
11.794
11.930
13.744
13.238
12.928
11.398
11.398
14.723
8.45E-08
1.76E-07
1.78E-07
5.30E-07
7.64E-07
1.24E-06
1.41E-06
4.01E-06
6.73E-06
1.28E-05
8.88E-06
8.88E-06
8.88E-06
1.99E-05
2.29E-05
3.03E-05
3.03E-05
7.52E-05
0.000112
0.000193
Light
Dark
Dark
Dark
Light
Light
Dark
Dark
Dark
Dark
Figure 3. Plotted niche breadth with mean relative abundance of OTUs indicating
generalist in red (B > 25) or specialist in blue (B < 5) OTUs.
Langenheder and Lindstrom 2006; Alonso-Sáez and Gasol 2007;
Buck et al. 2009). We suggest that this apparent indifference to
substrate quality is due to generalist mechanisms of uptake
for the tested AAs and their involvement in the central cell
metabolism. Since different AAs have been shown to compete
for the same uptake site (Suttle, Chan and Fuhrman 1991) and
since AAs can be used directly for synthesis while also representing a labile source of carbon and nitrogen (Malmstrom
et al. 2005), widespread ability to take up a wide array of different AAs is likely. Additionally, AAs are tightly linked to a central metabolism where the catabolism of AAs feed directly into
glucogenesis and the TCA cycle.
Biosynthesis of most AAs requires few enzymatic reactions and is fueled by a few intermediates in the central
metabolic pathways. Common intermediates include oxaloacetate, pyruvate, a-ketoglutarate, glycerate-3-P, erythrose-4-P and
PRPP (Davis 1955). Still, not all bacteria can produce all essential
AAs de novo and instead rely on exogenous AA uptake to fuel
their basic cell requirements (Owen and Jones 2001). This need
for exogenous AAs and AA precursors seems to be widespread
among aquatic bacterioplankton (Tripp et al. 2008; Garcia et al.
2015), suggesting a tight coupling between individual community members while also emphasizing the ubiquity of efficient
AA uptake mechanisms in bacterioplankton cells.
Our observations corroborate the assumption that strategies
for efficient uptake of a wide range of substrates would be advantageous under carbon and energy-limited conditions. This
also agrees with observations that bacteria will spend energy on
synthesis of transport machinery and components to take up
AAs from the surrounding environment if cofactors for biosynthesis are limiting in the environment (Gianoulis et al. 2009).
However, the latter case, i.e. that these bacteria are often able
to adapt their physiology and regulate their catabolic pathways
to metabolize many different carbon sources, is challenged by
the dominance of streamlined bacteria in both marine (Swan
et al. 2013; Giovannoni, Thrash and Temperton 2014) and freshwater environments (Eiler et al. 2014; Ghylin et al. 2014). Still the
type of substrates that are readily available to bacteria appears
to be largely overlapping between taxa (Egli 2010) and in the case
of AAs, universal transporter systems have previously been described (Gianoulis et al. 2009). This is in agreement with the indifferences to variable AA additions observed in our experiments.
Specialized taxa
In the present study, we found that the phylum with highest
contribution was the proteobacteria with the β-proteobacteria
class contributing 27% to the total community. The proteobacteria were also dominant in the control cultures, not amended
with AAs (48%). In both dark and light treatments, there were
only a few genera that were abundant; e.g. Limnohabitans and
8
FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
Leadbetterella with the highest percentage contribution of 23%
and 9% in the dark and 13% and 10% in the light, respectively.
The genus Limnohabitans has previously been shown to efficiently incorporate LMW DOM (Simek et al. 2010). However, Leadbetterella is a recently described genus of the Bacteroidetes phylum
(Parfenova, Gladkikh and Belykh 2013). Earlier studies on bacterial uptake of DFAA have in general revealed dominance by individual taxa. The α-proteobacteria and β-proteobacteria have for
example been shown to dominate DFAA uptake in marine and
freshwaters where such substrates are present in high concentration (Cottrell and Kirchman 2000; Schweitzer et al. 2001). This
has also been shown for closely related subclasses within the
β-proteobacteria and the α-proteobacteria that apparently differ in
the tendency to incorporate specific radiolabeled AAs, a pattern
believed to promote cooccurrence of abundant and ecologically
similar populations (Salcher et al. 2008; Salcher, Posch and Pernthaler 2013).
Only a few OTUs were identified as indicators for the individual AA treatments. These six genera characterized by
high fidelity for the respective type of AA, belonged to the
α-proteobacteria and β-proteobacteria class, agreeing with previous
findings where these groups have been shown to expand under
increased availability of DFAA (Schweitzer et al. 2001). Among
the six OTUs identified to be AA indicators, two were coupled to
the use of valine, which was the only branched AA included in
the study (Table 4). Still, in comparison, many more such indicator OTUs were identified relative to the light regime. For example, two OTUs of the Limnohabitans genus were characteristic for the dark treatment (Table 4). This is a genus previously
shown to benefit from dark conditions (Hornak et al. 2012). Another OTU characteristic for dark conditions was the OPB35 soil
group within the Verrucomicrobia (Table 4). This is a taxon known
to be abundant in wetlands (Deng et al. 2014). Three OTUs were
representative for light conditions (Table 4). These OTUs affiliated with Brevundimonas and Flavobacterium are considered to be
typical freshwater bacteria (Zwart et al. 2002) while Curvibacter
have previously been found to be well adapted to stressful environments where they often compete successfully as diversity
decreases (Gulliver, Lowry and Gregory 2014). This is in accordance with the lower diversity found in the light-exposed incubations compared to incubations maintained in darkness. When
we measured the niche breadth of the OTUs identified in the
community, 19.1% of the OTUs seemed to be generalists (wide
niche breadth). Since these populations appear to dominate the
communities, they could mask differential distributions of subsets of the community characterized by narrow niche breadth,
as indicated by the indifference in community responses to the
AA treatments.
Incubation conditions
In our experiment, we repeatedly added AAs to a final concentration of 250 μg C L−1 in each of our incubations. This concentration is approximately 40-fold higher than the concentrations of individual DFAAs reported for the eutrophic lake used in
this study (Bertilsson et al. 2007), but only between 1- and 5-fold
higher than of free AA concentrations observed in diurnal studies in eutrophic lakes (Jørgensen 1987; Munster 1993). This elevated concentration together with serial transfers to fresh media
with repeated amendments of the same AAs was chosen to be
realistic yet ensure a strong selection towards utilization of the
respective AA.
Some of the AAs taken up are likely directly assimilated into
proteins, but in addition to this, a fraction of the AAs could also
serve as a general carbon and nitrogen source. Uptake of AAs
when used as a nitrogen source relies on AA oxidases that are
inhibited by high concentrations of ammonium ions (Coudert
and Vandecasteele 1975; Sanchez-Amat, Solano and Lucas-Elı́o
2010). High NH4 concentrations in our incubations (115 ± 55.8 μg
L−1 , mean concentration and standard deviation) indeed suggest
that the added AAs would rather be used as a carbon or energy
source in our experiments than as a nitrogen source.
We acknowledge that in vitro incubations, although reproducible and aiming at disentangling environmental variables,
do not mimic in situ conditions. Certain procedures, such as removal of large particles through filtration, the wavelength distribution and intensity of PAR used in our light incubations as
well as removal of grazers, were performed to focus on the effect
of AAs and light as potential controlling factors for the bacterial community. Furthermore, the water used in the incubations
come from eutrophic Lake Ekoln and further testing would be
needed to determine if our findings can be extrapolated beyond
such productive systems.
Through serial transfers we selected for bacteria competitive in taking up AAs, but also, by allowing the cultures to reach
stationary phase, we do not exclude bacteria that are slow responders to the AA amendment or those that benefit from cross
feeding. We believe that this scenario is more likely to prevail in
natural environments where complex interaction between different groups of organisms take place that allow some bacteria
to take up intermediates produced by other bacteria (Beier and
Bertilsson 2011; Pande et al. 2014).
Previous studies have shown that bacterial community patterns can be shaped by environmental parameters such as
cyanobacteria blooms (Bertilsson et al. 2007), pH and temperature (Lindström, Kamst-van Agterveld and Zwart 2005). Nutrient and organic matter concentrations can also influence abundant microbial populations in freshwater systems (Eiler et al.
2003; Newton et al. 2011; Eiler, Heinrich and Bertilsson 2012). In
our incubations, the light regime with continuous PAR caused
lower growth rates and also fostered communities with lower
richness compared to the dark treatment. Although studies have
shown that bacteria that possess light harvesting pigments are
stimulated (Sharma et al. 2009), other studies have shown that
light can hamper bacterial growth and even directly inhibit the
uptake of AAs, at least when phytoplankton is absent (Aasl
et al. 1996). Without phytoplankton-derived bioavailable substrate, photoinhibition often seems to prevail.
Overall, our experiment demonstrated that different AA
amendments did not significantly affect overall bacterial community features. This was in contrast to the parallel experiments with light-regime manipulations where significant
responses were observed in bacterial diversity and functional community features, corroborating earlier reported lightregime-dependent responses (Aasl et al. 1996; Church, Ducklow
and Karl 2004). With some of the most abundantly produced
biomolecules (DFAAs) as model substrates, our study demonstrates that changes in organic substrate quality do not always
trigger observable responses in the heterotrophic community.
This does by no means exclude the possibility that some populations outperform others, but rather imply that this effect was not
seen at the level of the combined communities. Studies targeting
individual populations have in fact uncovered substrate specialization of bacterioplankton populations (Alonso-Sáez and Gasol
2007; Salcher, Posch and Pernthaler 2013), but these methods apply short incubation periods (2–4 hours) and do not target populations at the highly resolved level made possible by the 16S
sequencing approach.
Canelhas et al.
When seeking to represent a population from an environment, there can be potential methodological bias. In this case,
sequencing depth could be one such factor. However, the sequencing depth obtained in this study exceeds previously identified thresholds for robust beta-community analysis (Lundin et al.
2012). Further improvement of this type of study could nevertheless include more replicates and performing the study with
water from other types of lakes that are more oligotrophic that
might react stronger to the amended AAs.
A more likely explanation for our observations is however
that specialization with regard to uptake of specific AAs is limited or absent in freshwater bacterioplankton. It has previously
been argued that complex bacterial communities often feature functionally redundant generalists versatile in their uptake of different substrates (Langenheder, Lindström and Tranvik 2005). This seems to be the case for AAs due to their lability and value as major and ubiquitous sources of energy and
macronutrients (Mary et al. 2008; Tripp et al. 2008). Degradation of proteinaceous material represents a predictable and balanced input of the full variety of naturally occurring AAs. In a
substrate-limited system, parallel use of multiple AAs should
thus also confer a major competitive advantage compared to
those hypothetically specialized on a single or a few such substrates. We suggest future studies that investigate the effect of
labile LMW organic matter to take into consideration how these
substrates are regulated in the environment. This could lead to
interesting questions of when and how substrate concentrations
and composition play a role in dictating bacterial community
function.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank Moritz Buck for many thoughtful discussions and
statistical support, Christoffer Bergvall for HPLC measurements
and analysis and Lucas Sinclair for bioinformatic tools. We
also thank the Uppsala Multidisciplinary Center for Advanced
Computational Science (UPPMAX) for providing data storage resources. We thank the SciLifeLab SNP/SEQ facility hosted by Uppsala University for the Illumina Miseq sequencing.
FUNDING
This work was funded by the Swedish Research Council and the
Swedish Research Council Formas (grants to S.B.)
Conflict of interest. None declared.
REFERENCES
Aasl P, Lyons MM, Pledger R et al. Inhibition of bacterial activities by solar radiation in nearshore waters and the Gulf of
Mexico. Aquat Microb Ecol 1996;11:229–38.
Alonso C, Pernthaler J. Concentration-dependent patterns of
leucine incorporation by coastal picoplankton. Appl Environ
Microb 2006;72:2141–7.
Alonso-Sáez L, Gasol JM. Seasonal variations in the contributions of different bacterial groups to the uptake of
low-molecular-weight compounds in Northwestern Mediterranean coastal waters. Appl Environ Microb 2007;73:3528–35.
9
Amon RMW, Benner R. Bacterial utilization of different
size classes of dissolved organic matter. Limnol Oceanogr
1996;41:41–51.
Beier S, Bertilsson S. Uncoupling of chitinase activity and uptake
of hydrolysis products in freshwater bacterioplankton. Limnol Oceanogr 2011;56:1179–88.
Benner R, Amon RMW. The size-reactivity continuum of major
bioelements in the ocean. Ann Rev Mar Sci 2015;225:185–205.
Benner R, Pakulski JD, McCarthy M et al. Bulk chemical characteristics of dissolved organic matter in the Ocean. Science (80-)
1992;225:1561–4.
Berggren M, Laudon H, Haei M et al. Efficient aquatic bacterial
metabolism of dissolved low-molecular-weight compounds
from terrestrial sources. ISME J 2010;4:408–16.
Bertilsson S, Eiler A, Nordqvist A et al. Links between bacterial
production, amino-acid utilization and community composition in productive lakes. ISME J 2007;1:532–44.
Bertilsson S, Jones JB. Supply of dissolved organic matter to
aquatic ecosystems: autochtonous sources. In: Findlay SEG,
Sinsabaugh RL (eds). Aquatic Ecosystems: Interactivity of Dissolved Organic Matter. San Diego: Academic Press, Elsevier Science, 2003, 3–24.
Buck U, Grossart H-P, Amann R et al. Substrate incorporation
patterns of bacterioplankton populations in stratified and
mixed waters of a humic lake. Environ Microbiol 2009;11:1854–
65.
Burdige DJ, Martens CS. Biogeochemical cycling in organic-rich
coastal marine basin: 10. The role of amino acids in sedimentary carbon and nitrogen cycling. Geochim Cosmochim Ac
1988;52:1571–84.
Church MJ, Ducklow HW, Karl DM. Light dependence of [3 H]
leucine incorporation in the oligotrophic North Pacific ocean.
Appl Environ Microb 2004;70:4079–87.
Cottrell MT, Kirchman DL. Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter natural assemblages of marine Proteobacteria
and members of the Cytophaga-Flavobacter cluster C. Appl
Environ Microb 2000;66:1692–7.
Coudert M, Vandecasteele J-P. Charcterization and physiological function of a soluble L-amino acid oxidase in Corynebacterium. Arch Microbiol 1975;102:151–3.
Crump BC, Kling GW, Bahr M et al. Bacterioplankton community shifts in an arctic lake correlate with seasonal changes
in organic matter source. Appl Environ Microb 2003;69:
2253–68.
Davis BD. Intermediates in amino acid biosynthesis. Adv Enzymol
1955;16:247–312.
del Giorgio PA, Bird D, Prairie Y et al. Flow cytometric determination of bacterial abundance in lake plamkton with green
nucleic acid stain SYTO 13. Limnol Oceanogr 1996;41:783–9.
del Giorgio PA, Cole JJ. Bacterial growth efficiency in natural
aquatic systems. Annu Rev Ecol Syst 1998;29:503–41.
Deng Y, Cui X, Hernández M et al. Microbial diversity in hummock and hollow soils of three wetlands on the QinghaiTibetan Plateau revealed by 16S rRNA pyrosequencing. PLoS
One 2014;9:1–10.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 2013;10:996–8.
Egli T. How to live at very low substrate concentration. Water Res
2010;44:4826–37.
Eiler A, Bertilsson S. Composition of freshwater bacterial communities associated with cyanobacterial blooms in four
Swedish lakes. Environ Microbiol 2004;6:1228–43.
10
FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
Eiler A, Heinrich F, Bertilsson S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J
2012;6:330–42.
Eiler A, Langenheder S, Bertilsson S et al. Heterotrophic bacterial growth efficiency and community structure at different
natural organic carbon concentrations. Appl Environ Microb
2003;69:3701–9.
Eiler A, Zaremba-niedzwiedzka K, Martı́nez-garcı́a M et al. Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics. Environ
Microbiol 2014;16:2682–98.
Garcia SL, Buck M, Mcmahon KD et al. Auxotrophy and intrapopulation complementary in the ‘interactome’ of a cultivated
freshwater model community. Mol Ecol 2015;24:4449–59.
Ghylin TW, Garcia SL, Moya F et al. Comparative single-cell genomics reveals potential ecological niches for the freshwater
acI Actinobacteria lineage. ISME J 2014;8:2503–16.
Gianoulis TA, Raes J, Patel PV et al. Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc
Natl Acad Sci 2009;106:1374–9.
Giovannoni SJ, Thrash JC, Temperton B. Implications of streamlining theory for microbial ecology. ISME J 2014;8:1553–65.
Guillemette F, del Giorgio PA. Reconstructing the various facets
of dissolved organic carbon bioavailability in freshwater
ecosystems. Limnol Oceanogr 2011;56:734–48.
Gulliver DM, Lowry GV, Gregory KB. Effect of CO2(aq) exposure
on a freshwtaer aquifer microbial community from simulated geological carbon storage leakage. Environ Sci Technol
Lett 2014;1:479–83.
Horňák K, Zeder M, Blom JF et al. Suboptimal light conditions
negatively affect the heterotrophy of Planktothrix rubescens
but are beneficial for accompanying Limnohabitans spp. Environ Microbiol 2012;14:765–78.
Ingalls AE, Lee C, Wakeham SG et al. The role of biominerals
in the sinking flux and preservation of amino acids in the
Southern Ocean along 170W. Deep Res 2003;50:713–38.
Jardillier L, Basset M, Domaizon I et al. Bottom-up and top-down
control of bacterial community composition in the euphotic
zone of a reservoir. Aquat Microb Ecol 2004;35:259–73.
Jones DL, Owen AG, Farrar JF. Simple method to enable the
high resolution determination of total free amino acids in
soil solutions and soil extracts. Soil Biol Biochem 2002;34:
1893–902.
Jonsson A, Meili M, Bergström A-K et al. Whole-lake mineralization of allochthonous and autochthonous organic carbon in
a large humic lake (Örträsket, N. Sweden). Limnol Oceanogr
2001;46:1691–700.
Jørgensen NOG. Free amino acids in lakes: Concentrations and
assimilation rates in relation to phytoplankton and bacterial
production. Limnol Oceanogr 1987;32:97–111.
Jørgensen NOG, Kroer N, Coffin RB et al. Dissolved free amino
acids, combined amino acids, and DNA as sources of carbon and nitrogen to marine bacteria. Mar Ecol Prog Ser
1993;98:135–48.
Judd KE, Crump BC, Kling GW. Variation in dissolved organic
matter controls bacterial production and community composition. Ecology 2006;87:2068–79.
Kalyuzhnaya MG, Lidstrom ME, Chistoserdova L. Real-time detection of actively metabolizing microbes by redox sensing
as applied to methylotroph populations in Lake Washington.
ISME J 2008;2:696–706.
Kellerman AM, Dittmar T, Kothawala DN et al. Chemodiversity
of dissolved organic matter in lakes driven by climate and
hydrology. Nat Commun 2014;5:e3804.
Kemp ALW, Johnston LM. Diagenesis of organic matter in the
sediments of Lakes Ontario, Erie, and Huron. J Great Lakes Res
1979;5:1–10.
Kirchman DL. The contribution of monomers and other lowmolecular weight compounds to the flux of dissolved organic
material in aquatic ecosystems. In: Findlay SEG, Sinsabaugh
RL (eds). Aquatic Ecosystems: Interactivity of Dissolved Organic
Matter. San Diego: Academic Press, Elsevier Science, 2003,
217–41.
Komsta L. outliers: Tests for Outliers. R Package version 0.14. 2011.
http://CRAN.R-project.org/package=outliers.
Kritzberg ES, Langenheder S, Lindstrom ES. Influence of dissolved organic matter source on lake bacterioplankton structure and function - implications for seasonal dynamics of
community composition. FEMS Microbiol Ecol 2006;56:406–17.
Langenheder S, Lindström ES, Tranvik LJ. Weak coupling between community composition and functioning of aquatic
bacteria. Limnol Oceanogr 2005;50:957–67.
Lennon JT, Pfaff LE. Source and supply of terrestrial organic matter affects aquatic microbial metabolism. Aquat Microb Ecol
2005;39:107–19.
Levins R. Evolution in Changing Environments: Some Theoretical Explorations. Princeton, NJ: Princeton University Press, 1968.
Lindh M V., Figueroa D, Sjöstedt J et al. Transplant experiments
uncover Baltic Sea basin-specific responses in bacterioplankton community composition and metabolic activities. Front
Ecol Environ 2015;6:1–18.
Lindström ES, Kamst-van Agterveld MP, Zwart G. Distribution
of typical freshwater bacterial groups is associated with pH,
temperature, and lake water retention time. Appl Environ Microb 2005;71:8201–6.
Logares R, Lindström ES, Paterson H et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. Microb Ecol 2013;7:937–48.
Lundin D, Severin I, Logue JB et al. Which sequencing depth is
sufficient to describe patterns in bacterial a- and b-diversity?
Environ Microbiol Rep 2012;4:367–72.
Malmstrom RR, Cottrell MT, Elifantz H et al. Biomass production and assimilation of dissolved organic matter by SAR11
bacteria in the Northwest Atlantic ocean. Appl Environ Microb
2005;71:2979–86.
Martiny AC, Treseder K, Pusch G. Phylogenetic conservatism of
functional traits in microorganisms. ISME J 2013;7:830–8.
Mary I, Tarran GA, Warwick PE et al. Light enhanced amino acid
uptake by dominant bacterioplankton groups in surface waters of the Atlantic Ocean. FEMS Microbiol Ecol 2008;63:36–45.
Munster U. Concentrations and fluxes of organic carbon
substrates in the aquatic environment. Antonie Leeuw
1993;63:243–74.
Newton RJ, Jones SE, Eiler A et al. A guide to the natural history
of freshwater lake bacteria. Microbiol Mol Biol R 2011;75:14–49.
Oksanen J, Guillaume Blanchet F, Kindt R et al. vegan: Community Ecology Package. R Package Version 2.2-1, 2015.
http://CRAN.R-project.org/package=vegan (19 January 2016,
date last accessed).
Owen AG, Jones DL. Competition for amino acids between wheat
roots and rhizosphere microorganisms and the role of amino
acids in plant N acquisition. Soil Biol Biochem 2001;33:651–7.
Pande S, Merker H, Bohl K et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss
in bacteria. ISME J 2014;8:953–62.
Parfenova VV, Gladkikh AS, Belykh OI. Comparative analysis of
biodiversity in the planktonic and biofilm bacterial communities in Lake Baikal. Microbiology 2013;82:91–101.
Canelhas et al.
Peter H, Beier S, Bertilsson S et al. Function-specific response to
depletion of microbial diversity. ISME J 2011;5:351–61.
Pomeroy LR. The ocean’s food web, a changing paradigm. Bioscience 1974;24:499–504.
R Core Team. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing,
2014. http://www.R-project.org/ (19 January 2016, date last
accessed).
Roberts DW. labdsv: Ordination and Multivariate Analysis for Ecology. R Package Version 1.6-1. 2013. http://CRAN.R-project.org/
package=labdsv (19 January 2016, date last accessed).
Robinson MD, McCarthy, DJ, Smyth GK. edgeR: a Bioconductor
package for differential expression analysis of digital gene
expression data. Bioinformatics 2010;26:139–40.
Robinson MD, Smyth GK. Small sample estimation of negative
binomial dispersion, with application to SAGE data. Biostatistics 2008;9:321–32.
Salcher MM. Same same but different: ecological niche partitioning of planktonic freshwater prokaryotes. J Limnol
2014;73:74–87.
Salcher MM, Pernthaler J, Zeder M et al. Spatio-temporal niche
separation of planktonic Betaproteobacteria in an oligomesotrophic lake. Environ Microbiol 2008;10:2074–86.
Salcher MM, Posch T, Pernthaler J. In situ substrate preferences
of abundant bacterioplankton populations in a prealpine
freshwater lake. ISME J 2013;7:896–907.
Sanchez-Amat A, Solano F, Lucas-Elı́o P. Finding new enzymes
from bacterial physiology: a successful approach illustrated
by the detection of novel oxidases in Marinomonas mediterranea. Mar Drugs 2010;8:519–41.
Schimel JP, Gulledge J. Microbial community structure and global
trace gases. Glob Chang Biol 1998;4:745–58.
Schweitzer B, Huber I, Amann R et al. α - and β - Proteobacteria control the consumption and release of amino
acids on lake snow aggregates. Appl Environ Microb 2001;67:
632–45.
Sharma AK, Sommerfeld K, Bullerjahn GS et al. Actinorhodopsin
genes discovered in diverse freshwater habitats and
among cultivated freshwater Actinobacteria. ISME J 2009;3:
726–37.
Simek K, Kasalicky V, Jezbera J et al. Broad habitat range of
the phylogenetically narrow R-BT065 cluster, representing
a core group of the betaproteobacterial. Appl Environ Microb
2010;76:631–9.
Sinclair L, Osman OA, Bertilsson S et al. Microbial community
composition and diversity via 16S rRNA gene amplicons:
evaluating the illumina platform. PLoS One 2015;10:E0116955.
11
Stepanauskas R, Moran MA, Bergamaschi BA et al. Covariance of
bacterioplankton composition and environmental variables
in a temperate delta system. Aquat Microb Ecol 2003;31:85–98.
Suttle CA, Chan AM, Fuhrman JA. Dissolved free amino acids
in the Sargasso Sea: uptake and respiration rates, turnover
times, and concentrations. Mar Ecol Prog Ser 1991;70:189–99.
Swan BK, Tupper B, Sczyrba A et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the
surface ocean. Proc Natl Acad Sci 2013;110:11463–8.
Tranvik LJ. Availability of dissolved organic carbon for planktonic
bacteria in oligotrophic lakes of differing humic content. Microb Ecol 1988;16:311–22.
Tripp HJ, Kitner JB, Schwalbach MS et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature
2008;452:741–4.
van Hees, PAW, Jones DL, Finlay R et al. The carbon we do not see
— the impact of low molecular weight compounds on carbon
dynamics and respiration in forest soils : a review. Soil Biol
Biochem 2005;37:1–13.
Wagner M, Haider S. New trends in fluorescence in situ hybridization for identification and functional analyses of microbes. Curr Opin Biotechnol 2012;23:96–102.
Wagner M, Horn M, Daims H. Fluorescence in situ hybridisation for the identification and characterisation of prokaryotes. Curr Opin Microbiol 2003;6:302–9.
Wagner M, Nielsen PH, Loy A et al. Linking microbial community
structure with function : fluorescence in situ hybridizationmicroautoradiography and isotope arrays. Curr Opin Biotechnol 2006;17:83–91.
Wetzel RG (ed). Bacteriolplankton. In: Limnology: Lake and River
Ecosystems. London: Academic Press, 2001, 490–527.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York:
Springer, 2009.
Wright ES, Yilmaz LS, Corcoran AM et al. Automated design of
probes for rRNA-targeted fluorescence in situ hybridization
reveals the advantages of using dual probes for accurate
identification. Appl Environ Microb 2014;80:5124–33.
Yao X, Zhu G, Cai L et al. Geochemical characteristics of amino
acids in sediments of Lake Taihu, a large, shallow, eutrophic
freshwater lake of China. Aquat Geochemistry 2012;18:263–80.
Zhang J. spaa: SPecies Association Analysis. R Package version
0.2.1, 2013. http://CRAN.R-project.org/package=spaa (19 January 2016, date last accessed).
Zwart G, Crump BC, Kamst-van Agterveld MP et al. Typical freshwater bacteria: an analysis of available 16S rRNA gene sequences from plankton of lakes and rivers. Aquat Microb Ecol
2002;28:141–55.