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\section{Materials and Methods}
\subsubsection{Experimental Design}
We placed test tube racks in one smaller (185L, control) and 3 larger (370L)
flow-through mesocosms. Each mesocosm had an adjustable flow rate that resulted
in a residence time of approximately 12h. Irregular variation in inflow rate
meant that flow rate varied around that target throughout the day, however,
regular monitoring ensured that the entire volume of each system was flushed
approximately two times per day. To provide a surface for biofilm formation we
attached coverslips to glass slides using nail polish and then attached each
slide to the test tube racks using office-style binder clips. Twice daily 10 ml
of 37 mM KPO$_{4}$ and 1, 5 and 50 ml of 3.7M glucose were added to each of 3
mesocosms to achieve target C:P resource amendments of 10, 100 and 500
respectively. The control mesocosm did not receive any C or P amendments.
\subsubsection{DOC and Chlorophyll Measurements}
To assess the efficacy of the
carbon C additions we sampled each mesocosm twice
daily during the first week of the experiment to evaluate dissolved organic
carbon C (DOC) content. After the initiation of the experiment we collected
plankton on filters regularly to evaluate planktonic Chl \textit{a} and
bacterial abundance. Once it was clear that pool size of each community had
been altered (day 8) we filtered plankton onto 0.2 $\mu$m filters and harvested
coverslips to assess bacterial and algal
biofilm community composition (16S and 23S
rDNA). In addition all mesoscosms were analyzed for community composition a
second time (day 17) to assess how community composition of both the plankton
and biofilm communities had been altered over time. Control samples were only
analyzed for community composition on day 17.
Samples for dissolved organic
carbon C (DOC) analysis were collected in acid
washed 50 mL falcon tubes after filtration through a 0.2 polycarbonate membrane
filter (Millipore GTTP GTTP02500, Sigma Aldrich P9199) attached to a 60 mL
syringe. Syringes and filters were first flushed multiple times with the
control sample to prevent leaching of
carbon C from the syringe or the filter
into the sample. Samples were then frozen and analyzed for organic
carbon C
content with a Shimadzu 500 TOC analyzer
\cite{Wetzel_2000}. \citep{Wetzel_2000}. Biomass of all
biofilm samples were measured by difference in pre-(without biofilm) and
post-(with biofilm) weighed GF/F filters after oven drying overnight at 60C.
For Chl \textit{a} analysis we collected plankton on GF/F filters (Whatman,
Sigma Aldrich Cat.
# \# Z242489) by filtering between 500 mL and 1L from the
water column of each mesocosm for each treatment. For biofilm samples, all
biofilm was gently removed from the complete area of each coverslip (3
coverslips for each treatment per sampling event) before being placed in a test
tube for extraction with 90-95\% acetone for ~ 32 hours at -20C and analyzed
immediately after using a Turner 10-AU fluorometer
\cite{Wetzel_2000}. \citep{Wetzel_2000}.
We analyzed bacterial abundance of the planktonic samples using Dapi staining
and direct visualization on a Zeis Axio epifluorescence microscope after the
methods of Porter and Feig (1980). Briefly, 1-3 mL of water was filtered from
three separate water column samples through a 0.2 $\mu$m black polycarbonate
membrane filter and post stained with a combination of Dapi and Citifluor
mountant media (Ted Pella Redding, Ca) to a final concentration of
1$\mu$ ml-1.
\textit{DNA 1$\mu$L
mL-1.
\subsubsection{DNA extraction} For plankton, cells were collected by filtering
between 20
– {\textendash} 30 mL of water onto a 0.2 $\mu$m pore-size
polycarbonate filter (Whatman Nucleopore 28417598, Sigma-Aldrich
cat# cat\#
WHA110656). For biofilm communities, biomass from the entire coverslip area of
three separate slides was collected and combined in an eppendorf tube by gentle
scrapping the slip surface with an ethanol rinsed and flamed razor blade. DNA
from both the filter and the biofilm was extracted using a Mobio Power Soil DNA
isolation kit (MoBio Cat.
# \# 12888).
\subsubsection{PCR}
Samples were amplified for pyrosequencing using a forward and reverse fusion
primer. The forward primer was constructed with
(5’-3’) (5{'}-3{'}) the Roche A
linker, an 8-10bp barcode, and the forward gene specific primer sequence. The
reverse fusion primer was constructed with
(5’-3’) (5{'}-3{'}) a biotin molecule, the
Roche B linker and the reverse gene specific primer sequence. The gene specific
primer pair for bacterial SSU rRNA genes was 27F/519R
\cite{LANED.J.:1991}. \citep{LANED.J.:1991}.
The primer pair p23SrV\_f1/p23SrV\_r1 was used to target 23S rRNA genes on
plastid genomes
\cite{Sherwood_2007}. \citep{Sherwood_2007}. Amplifications were performed in 25 ul
reactions with Qiagen HotStar Taq master mix (Qiagen Inc, Valencia,
California), 1ul of each 5uM primer, and 1ul of template. Reactions were
performed on ABI Veriti thermocyclers (Applied Biosytems, Carlsbad, California)
under the following thermal profile: 95$^{\circ}$C for 5 min, then 35 cycles of
94$^{\circ}$C for 30 sec, 54$^{\circ}$C for 40 sec, 72$^{\circ}$C for 1 min,
followed by one cycle of 72$^{\circ}$C for 10 min and 4$^{\circ}$C hold.
Amplification products were visualized with eGels (Life Technologies, Grand
Island, New York). Products were then pooled equimolar and each pool was
cleaned with Diffinity RapidTip (Diffinity Genomics, West Henrietta, New York),
and size selected using Agencourt AMPure XP (BeckmanCoulter, Indianapolis,
Indiana) following Roche 454 protocols (454 Life Sciences, Branford,
Connecticut). Size selected pools were then quantified and 150 ng of DNA were
hybridized to Dynabeads M-270 (Life Technologies) to create single stranded DNA
following Roche 454 protocols (454 Life Sciences). Single stranded DNA was
diluted and used in emPCR reactions, which were performed and subsequently
enriched. Sequencing followed established manufacture protocols (454 Life
Sciences).
\subsection{Sequence Quality Control and Analysis}
\subsubsection{Quality Control}
The 16S/23S sequence collections were demultiplexed and sequences with sample
barcodes not matching expected barcodes were discarded. We used the maximum
expected error metric \citep{23955772} calculated from sequence quality scores
to cull poor quality sequences from the dataset. Specifically, we discarded any
sequence with a maximum expected error count greater than 1 after truncating to
175 nt. The forward primer and barcode was trimmed from the remaining reads. We
checked that all primer trimmed, error screened and truncated sequences were
derived from the same region of the LSU or SSU rRNA gene (23S and 16S
sequences, respectively) by aligning the reads to Silva LSU or SSU rRNA gene
alignment ({\textquotedblleft}Ref{\textquotedblright} collection, release 115)
with the Mothur \citep{19801464} NAST-algorithm \citep{16845035} aligner and
inspecting the alignment coordinates. Reads falling outside the expected
alignment coordinates were culled from the dataset. Remaining reads were
trimmed to consistent alignment coordinates such that all reads began and ended
at the same position in the SSU rRNA gene and screened for chimeras with UChime
in {\textquotedblleft}denovo{\textquotedblright} mode \citep{21700674} via the
Mothur UChime wrapper.
\subsubsection{Taxonomic annotations} Sequences were taxonomically classified
using the UClust \citep{20709691} based classifier in the QIIME package
\citep{20383131} with the Greengenes database and taxonomic nomenclature
(version "gg\_13\_5" provided by QIIME developers, 97\% OTU
representative sequences and corresponding taxonomic annotations,
\citep{22134646}) for 16S reads or the Silva LSU database (Ref
set, version 115, EMBL taxonomic annotations, \citep{23193283}) for the 23S
reads as reference. We used the default parameters for the algorithm (i.e.
minimum consensus of 51\% at any rank, minimum sequence identity for hits at
90\% and the maximum accepted hits value was set to 3).
\subsubsection{Clustering}
Reads were clustered into OTUs following the UParse pipeline. Specifically
USearch (version 7.0.1001) was used to establish cluster centroids at a 97\%
sequence identity level from the quality controlled data and map quality
controlled reads to the centroids. The initial centroid establishment algorithm
incorporates a quality control step wherein potentially chimeric reads are not
allowed to become cluster seeds. Additionally, we discarded singleton reads
because it is difficult to asses the quality of singleton reads and this
quality control parameter in addition to maximum expected error screening has
proven to be similarly useful if not superior for reducing 454 sequencing error
as {\textquotedblleft}denoising{\textquotedblright} \citep{23955772}. Moreover,
two popular {\textquotedblleft}denoising{\textquotedblright} algorithms have
been shown to add sequencing errors while correcting others sometimes in a
nearly equal ratio \citep{22543370}. Eighty-eight and 98\% of quality
controlled reads could be mapped back to our cluster seeds at a 97\% identity
cutoff for the 16S and 23S sequences, respectively.
\subsubsection{Alpha and Beta diversity analyses}
Alpha diversity calculations were made using PyCogent Python bioinformatics
modules \citep{17708774}. Beta diversity analyses were made using Phyloseq
\citep{24699258} and its dependencies \citep{vegan}. A sparsity threshold of
25\% was used for ordination of both plastid 23S and bacterial 16S libraries.
Additionally, we discarded any OTUs from the 23S data that could not be
annotated as belonging in the Eukaryota. All DNA sequence based results were
visualized using GGPlot2 \citep{Wickham_2009}. Adonis tests and principal
coordinate ordinations were performed using the Bray-Curtis similarity measure
for pairwise library comparisons. Adonis tests employed the default value for
number of permutations (999) ("adonis" function in Vegan R package,
\citet{vegan}). Principal coordinates of OTUs were found by averaging site
principal coordinate values for each OTU with OTU relative abundance values
(within sites) as weights. The principal coordinate OTU weighted averages were
then expanded to match the site-wise variances \citep{vegan}.
\subsubsection{Identifying Enriched OTUs}
We used an RNA-Seq differential expression statistical framework to find OTUs
enriched in the given sample classes (R package DESeq2 developed by
\citet{Love_2014}) (for review of RNA-Seq differential expression statistics
applied to microbiome OTU count data see \citet{24699258}). We use the term
{\textquotedblleft}differential abundance{\textquotedblright} coined by
\citet{24699258} to denote OTUs that have different proportion means across
sample classes. We are particularly interested in two sample classes: 1)
lifestyle (biofilm or planktonic) and, 2) high C (C:P = 500) versus
not high C (C:P = 10, C:P = 100 and C:P = control). A differentially
abundant OTU would have a proportion mean in one class that is
statistically different from its proportion mean in another. This differential abundance
could mark an enrichment of the OTU in either sample class and the direction of
the enrichment is apparent in the sign (positive or negative) of the metric
used to summarize the proportion mean difference. Here we use log2 of the
proportion mean ratio (means are derived from OTU proportions for all samples
in each given class) as our differential abundance metric. It is also important
to note that the DESeq2 R package we are using to calculate the differential
abundance metric {\textquotedblleft}shrinks{\textquotedblright} the metric in
inverse proportion to the information content for each OTU. In this way the
magnitude of the differential abundance metric will be high only for OTUs which
there is a robust signal of differential abundance. Further, the metric can be
used to effectively rank OTUs by magnitude of the sample class affect (i.e.
OTUs with high proportion mean differences but also high within sample class
proportion variance will not produce misleadingly large differential abundance
metric values). The DESeq2 RNA-Seq statistical framework has been shown to
improve power and specificity when identifying differentially abundant OTUs
across sample classes in microbiome experiments \citet{24699258}.
The specific specific DESeq2 \citep{Love_2014} parameters we used were as follows:
All dispersion estimates from DESeq2 were calculated using a local fit for
mean-dispersion. Native DESeq2 independent filtering was disabled in favor of
explicit sparsity filtering. The sparsity thresholds that produced the maximum
number of OTUs with adjusted p-values for differential abundance below a false
discovery rate of 10\% were selected for biofilm versus planktonic sequence
16S/plastid 23S library comparisons. The specific sparsity threshold for
plastid 23S and 16S libraries for biofilm versus plankton comparisons was 10\%
(OTUs found in less than the sparsity threshold of samples were discarded from
the analysis). Cook's distance filtering was also disabled when calculating
p-values with DESeq2. We used the Benjamini-Hochberg method to adjust p-values
for multiple testing \citep{citeulike:1042553}. Identical DESeq2 methods were
used to assess enriched OTUs from relative abundances grouped into high (C:P =
500) or low (C:P < 500 and control) categories.
IPython Notebooks with computational methods used to create all figures and
tables are provided at the following url:
\url{http://nbviewer.ipython.org/github/chuckpr/BvP_manuscript_figures}.