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\begin{document}

\title{Unearthing the contributions and ecology of uncultured soil bacteria to the metabolism of xylose and cellulose in soil."}


\author{Ashley Campbell}
\affil{Affiliation not available}

  
\author{Charles Pepe-Ranney}
\affil{Affiliation not available}
  
\author{Dan Buckley}
\affil{Affiliation not available}
  



\date{\today}

\maketitle




\section{Significance} Soil microorganisms drive C flux through the terrestrial
biosphere, and models that predict terrestrial C flux can benefit by accounting
for microbial ecophysiology in soils. However, characterizing the ecophysiology
of microbes that mediate C decomposition in soil has proven difficult due to
their overwhelming diversity. We characterized microbial C metabolism in soil
and show that different types of C have distinct decomposition dynamics
governed by different microbial lineages. For example, we found that
uncharacterized microbial taxa, which are cosmopolitan in soils, assimilated
cellulose-C into DNA. These microbes may drive cellulose decomposition on
a global scale. We identify microbial lineages engaging in labile and
structural C decomposition and explore their ecological properties.


\section{Introduction}
% Fakesubsubsection:Excluding plant biomass, there are 2,300 Pg of
Soils worldwide contain 2,300 Pg of carbon (C) which accounts for nearly~80\%
of the C present in the terrestrial biosphere
\citep{Amundson_2001,BATJES_1996}. Soil microorganisms drive C flux through the
terrestrial biosphere and C respiration by soil microorganisms
produces annually tenfold more CO$_{2}$ than fossil fuel emissions
\citep{chapin2002principles}. Despite the contribution of microorganisms to
global C flux, many global C models ignore the diversity of microbial
physiology \citep{Allison2010,Six2006,Treseder2011} and we still know little
about the ecophysiology of soil microorganisms. Characterizing the
ecophysiology of microbes that mediate C decomposition in soil has proven
difficult due to their overwhelming diversity. Such knowledge should assist
the development and refinement of global C models
\citep{Bradford2008,Neff_2001,McGuire2010,Wieder2013}.

% Fakesubsubsection:Though microorganisms mediate
Though microorganisms mediate 80-90\% of the soil C-cycle
\citep{ColemanCrossley_1996,Nannipieri_2003}, and microbial community
composition can account for significant variation in C mineralization
\citep{Strickland_2009}, terrestrial C-cycle models rarely consider the
community composition of soils \citep{Zak2006,Reed2007}. Variation in microbial
community composition can be linked effectively to rates of soil processes when
diagnostic genes for specific functions are available (e.g. nitrogen fixation
\citep{Hsu2009}).  However, the lack of diagnostic genes for describing soil-C
transformations has limited progress in characterizing the contributions of
individual microorganisms to decomposition. Remarkably, we still lack basic
information on the physiology and ecology of the majority of organisms that
live in soils. For example, contributions to soil processes remain
uncharacterized for cosmopolitan bacterial phyla in soil such as
\textit{Acidobacteria}, \textit{Chloroflexi}, \textit{Planctomycetes}, and
\textit{Verrucomicrobia}. These phyla combined can comprise 32\% of soil
microbial communities (based on surveys of the SSU rRNA genes in soil)
\citep{Janssen2006,Buckley2002}. 

% Fakesubsubsection: To predict whether and how
To predict whether and how biogeochemical processes vary in response to
microbial community structure, it is necessary to characterize functional niches
within soil communities. Functional niches defined on the basis of microbial
physiological characteristics have been successfully incorporated into
biogeochemical process models (E.g. \citep{Wieder2013, Kaiser2014a}). In some
C-cycle models physiological parameters such as growth rate and substrate
specificity are used to define functional niche behavior \citep{Wieder2013}.
However, it is challenging to establish the phylogenetic breadth of functional
traits. Functional traits are often inferred from the distribution of
diagnostic genes across genomes \citep{Berlemont2013} or from the physiology of
isolates cultured on laboratory media \citep{Martiny2013}. For instance, the
wide distribution of the glycolysis operon in microbial genomes is interpreted
as evidence that many soil microorganisms participate in glucose turnover
\citep{McGuire2010}. However, the functional niche may depend less on the
distribution of diagnostic genes across genomes and more on life history traits
that allow organisms to compete for a given substrate as it occurs in the
environment. For instance, rapid resuscitation and fast growth are traits that
may allow microorganisms to compete effectively for glucose in environments
that exhibit high temporal variability. Alternatively, metabolic efficiency and
slow growth rates may be traits that allow microbes to compete effectively for
glucose in environments characterized by low temporal variability in glucose
supply. These different competitive strategies would not be apparent from
genome analysis, or when strains are grown in isolation.  Hence, life history
traits, rather than genomic capacity for a given pathway, are likely to
constrain the diversity of microbes that metabolize a given C source in the
soil under a given set of conditions. Therefore, to generate an understanding
of functional niche as it relates to biogeochemical processes in soils it is
important to characterize microbial functional traits as they occur \textit{in
situ} or in microcosm experiments.

% Fakesubsubsection:Nucleic acid SIP
Nucleic acid stable-isotope probing (SIP) links genetic identity and activity
without the need diagnostic genetic markers or cultivation and has expanded our
knowledge of microbial processes
\citep{Chen_Murrell_2010}. Nucleic acid SIP has notable complications, however,
including the need to add large amounts of labeled substrate
\citep{radajewski2000stable}, label dilution resulting in partial labeling of
nucleic acids \citep{radajewski2000stable}, the potential for cross-feeding and
secondary label incorporation \citep{DeRito2005}, and variation in genome G$+$C
content \citep{Buckley_2007}.  As a result, most applications of SIP have
targeted specialized microorganisms (for instance, methylotrophs
\citep{lueders2004b}, syntrophs \citep{lueders2004}, or microorganisms that
target pollutants \citep{derito2005}). Exploring the soil-C cycle with SIP has
proven to be more challenging because SIP has lacked the resolution necessary
to characterize the specific contributions of individual microbial groups to
the decomposition of plant biomass. High throughput DNA sequencing technology,
however, improves the resolving power of SIP \citep{Aoyagi2015}. It is now
possible to use far less isotopically labeled substrate resulting in more
environmentally realistic experimental conditions. It is also possible to
sequence rRNA genes from numerous density gradient fractions across multiple
samples thereby increasing the resolution of a typical nucleic acid SIP
experiment \citep{Verastegui_2014}. With this improved resolution the activity
of more soil microorganisms can be assessed. Further, since microbial
activities can be more comprehensively assessed, we can begin to determine the
ecological properties of functional groups defined by a specific activity in a
DNA-SIP experiment. We have employed such a high resolution DNA stable isotope
probing approach to explore the assimilation of both xylose and cellulose into
bacterial DNA in an agricultural soil. 

% Fakesubsubsection: We add a complex amendment
We added to soil a complex amendment representative of organic
matter derived from fresh plant biomass. All treatments received the same
amendment but the identity of isotopically labeled substrates was varied
between treatments. Specifically, we set up a control treatment where all
components were unlabeled, a treatment with $^{13}$C-xylose instead of
unlabeled xylose, and a treatment with $^{13}$C-cellulose instead of unlabeled
cellulose. Soil was sampled at days~1,~3,~7,~14,~and~30 and we identified
microorganisms that assimilated $^{13}$C into DNA at each point in time. We
designed the experiment to test of the degradative succession
hypothesis as it applies to soil bacteria, to identify soil bacteria that
metabolize xylose and cellulose, and to characterize temporal dynamics of
xylose and cellulose metabolism in soil. 


\section{Results}
% Fakesubsubsection:We tracked the flow of C from xylose
After adding the organic matter amendment to soil, we tracked the flow of
$^{13}$C from $^{13}$C-xylose or $^{13}$C-cellulose into microbial DNA over
time using DNA-SIP (Figure~\ref{fig:setup}). The amendment consisted of
compounds found in of plant biomass including cellulose, lignin, sugars
found in hemicellulose, amino acids, and inorganic nutrients (see Supplemental
Information (SI)). The amendment was added at~2.9 mg C g$^{-1}$ soil dry weight
(d.w.), and this comprised 19\% of the total C in the soil. The cellulose-C
(0.88 mg C g$^{-1}$ soil d.w.) and xylose-C (0.42 mg C g$^{-1}$ soil d.w.) in
the amendment comprised 6\% and 3\% of the total C in the soil, respectively.
The soil microbial community respired 65\% of the xylose within one day and
29\% of the added xylose remained in the soil at day~30 (Figure~\ref{fig:13C}).
In contrast, cellulose-C declined at a rate of approximately 18 $\mu$g
C d $^{-1}$ g $^{-1}$ soil d.w. and 40\% of added cellulose-C remained in the
soil at day~30 (Figure~\ref{fig:13C}).

\subsection{Community-level signal of $^{13}$C-assimilation in relation to substrate and time}
% Fakesubsubsection: We assessed assimilation of
We assessed assimilation of $^{13}$C into microbial DNA by comparing the SSU
rRNA gene sequence composition of SIP density gradient fractions between
$^{13}$C treatments and the unlabeled control (see Methods and SI).  Our main
focus is to identify evidence of isotope incorporation into the DNA of specific
OTUs (as described below), but it is instructive to begin by observing overall
patterns of variance in the SSU rRNA gene sequence composition of gradient
fractions. In the unlabeled control treatment, fraction density represented the
majority of the variance in SSU rRNA gene composition (Figure~\ref{fig:ord}). This
result is expected because Genome G$+$C content correlates positively with DNA
buoyant density and influences SSU rRNA gene composition in gradient fractions
\citep{Buckley_2007}. In contrast, isotope assimilation into DNA will cause
variation in gene sequence composition between corresponding density fractions
from controls and labeled treatments. For example, the SSU rRNA gene
composition in gradient fractions from the $^{13}$C-cellulose
treatment deviated from corresponding control fractions on days~14 and~30
and this difference was observed only in the high
density fractions ($>$1.7125 g mL$^{-1}$, Figure~\ref{fig:ord}). Likewise, SSU
rRNA gene composition in gradient fractions from the $^{13}$C-xylose treatment
also deviated from corresponding control fractions but on days~1,~3,~and~7
as opposed to 14 and 30 (Figure~\ref{fig:ord}). The $^{13}$C-cellulose
and $^{13}$C-xylose treatments also differed from each other in corresponding
high density gradient fractions indicating that different
microorganisms were labeled across time these treatments (Figure~\ref{fig:ord}).
These results are generally consistent with predictions of the degradative
succession hypothesis.

We can observe further differences in the pattern of isotope incorporation over
time for each treatment. For example the SSU rRNA gene sequence
composition in the $^{13}$C-cellulose treatment was similar on days 14 and 30 in 
corresponding high density fractions indicating similar patterns of isotope
incorporation into DNA on the days. In contrast, in the $^{13}$C-xylose treatment,
the SSU rRNA gene composition varied between
days~1,~3,~and~7 in corresponding high density fractions indicating different patterns
of isotope incorporation into DNA on these days. In the
$^{13}$C-xylose treatment on days 14 and 40 the SSU gene composition
was similar to control on days~14~and~30 for corresponding high density
fractions (Figure~\ref{fig:ord}) indicating that $^{13}$C was no longer
detectable in bacterial DNA on these days for this treatment. These results
show that the dynamics of isotope incorporation into DNA varied considerably for
organisms that assimilated C from either xylose or cellulose.

\subsection{Temporal dynamics of OTU relative abundance in non-fractionated DNA from soil}
% Fakesubsubsection:We monitored the soil microbial community
We monitored the soil microbial community over the course of the
experiment by surveying SSU rRNA genes in non-fractionated DNA from the
soil. The SSU rRNA gene composition of the non-fractionated DNA
changed with time (Figure~\ref{fig:bulk_ord}, P-value~$=$~0.023, R$^{2}$
$=$~0.63, Adonis test \citep{Anderson2001a}). In contrast, the microbial
community could not be shown to change with treatment
 (P-value~0.23, Adonis test) (Figure~\ref{fig:bulk_ord}). The latter
result demonstrates the substitution of $^{13}$C-labeled substrates for
unlabeled equivalents could not be shown to alter the soil microbial community
composition. Twenty-nine OTUs exhibited sufficient statistical evidence
(adjusted P-value $<$ 0.10, Wald test) to conclude they changed in relative
abundance in the non-fractionated DNA over the course of the experiment
(Figure~\ref{fig:time}). When SSU rRNA gene abundances were combined at the
taxonomic rank of "class", the classes that changed in abundance (adjusted
P-value~ $<$~0.10, Wald test) were the \textit{Bacilli} (decreased),
\textit{Flavobacteria} (decreased), \textit{Gammaproteobacteria} (decreased),
and \textit{Herpetosiphonales} (increased) (Figure~\ref{fig:time_class}). Of
the~29 OTUs that changed in relative abundance over time, 14 putatively
incorporated $^{13}$C into DNA (see below and Figure~\ref{fig:time}). OTUs that likely
assimilated $^{13}$C from $^{13}$C-cellulose tended to increase in
relative abundance with time whereas OTUs that assimilated $^{13}$C from
$^{13}$C-xylose tended to decrease (Figure~\ref{fig:babund}). OTUs that
responded to both substrates did not exhibit a consistent relative abundance
response over time as a group (Figure~\ref{fig:time}~and~\ref{fig:babund}).

\subsection{Changes in the phylogenetic composition of $^{13}$C-labeled OTUs with substrate and time}
% Fakesubsubsection:If an OTU exhibited
If an OTU exhibited strong evidence for assimilating $^{13}$C into DNA, we
refer to that OTU as a ``responder'' (see Methods and SI for our operational
definition of ``responder''). The SSU rRNA gene sequences produced in this study
were binned into 5,940 OTUs and we assessed evidence of $^{13}$C-labeling from
both $^{13}$C-cellulose and $^{13}$C-xylose for each OTU. Forty-one OTUs
responded to $^{13}$C-xylose,~55 OTUs responded to $^{13}$C-cellulose, and
8 OTUs responded to both xylose and cellulose (Figure~\ref{fig:l2fc},
Figure~\ref{fig:tiledtree}, Figure~\ref{fig:genspec}, Table~\ref{tab:xyl}, and
Table~\ref{tab:cell}). The number of xylose responders peaked at days~1 and~3
and declined with time. In contrast, the number of cellulose responders
increased with time peaking at days~14 and~30 (Figure~\ref{fig:rspndr_count}). 

% Fakesubsubsection: The phylogenetic composition
The phylogenetic composition of xylose responders changed with time
(Figure~\ref{fig:l2fc}~and~Figure~\ref{fig:xyl_count}) and 86\% of xylose responders
shared $>$ 97\% SSU rRNA gene sequence identity with bacteria cultured in
isolation (Table~\ref{tab:xyl}). On day 1, \textit{Bacilli} OTUs represented
84\% of xylose responders (Figure~\ref{fig:xyl_count}) and the majority of
these OTUs were closely related to cultured representatives of the genus
\textit{Paenibacillus} (Table~\ref{tab:xyl}, Figure~\ref{fig:tiledtree}). For
example, ``OTU.57'' (Table~\ref{tab:xyl}), annotated as \textit{Paenibacillus},
had a strong signal of $^{13}$C-labeling at day~1 coinciding with its
maximum relative abundance in non-fractionated DNA. The relative abundance
of ``OTU.57'' declined until day 14 and ``OTU.57'' did not appear to be
$^{13}$C-labeled after day~1 (Figure~\ref{fig:example}). On day~3,
\textit{Bacteroidetes} OTUs comprised 63\% of xylose responders
(Figure~\ref{fig:xyl_count}) and these OTUs were closely related to
cultured representatives of the \textit{Flavobacteriales} and
\textit{Sphingobacteriales} (Table~\ref{tab:xyl},
Figure~\ref{fig:tiledtree}). For example, ``OTU.14'', annotated as
a flavobacterium, had a strong signal for $^{13}$C-labeling in the
$^{13}$C-xylose treatment at days 1 and 3 coinciding with its maximum
relative abundance in non-fractionated DNA. The relative abundance of
``OTU.14'' then declined until day 14 and did not show evidence of
$^{13}$C-labeling beyond day~3 (Figure~\ref{fig:example}). Finally, on
day~7, \textit{Actinobacteria} OTUs represented 53\% of the xylose
responders (Figure~\ref{fig:xyl_count}) and these OTUs were closely
related to cultured representatives of \textit{Micrococcales}
(Table~\ref{tab:xyl}, Figure~\ref{fig:tiledtree}). For example, ``OTU.4'',
annotated as \textit{Agromyces}, had signal for $^{13}$C-labeling in the
$^{13}$C-xylose treatment on days~1,~3~and~7 with the strongest evidence
of $^{13}$C-labeling at day~7 and did not appear $^{13}$C-labeled at
days~14 and~30. The relative abundance of ``OTU.4'' in non-fractionated
DNA increased until day~3 and then declined until day~30
(Figure~\ref{fig:example}). \textit{Proteobacteria} were also common among
xylose responders at day~7 where they comprised 40\% of xylose responder
OTUs. Notably, \textit{Proteobacteria} represented the majority (6 of 8)
of OTUs that responded to both cellulose and xylose
(Figure~\ref{fig:genspec}). 

% Fakesubsubsection: The phylogenetic composition of cellulose responders were
The phylogenetic composition of cellulose responders did not change with time
to the same extent as the xylose responders. Also, in contrast to xylose
responders, cellulose responders often were not closely related ($<$ 97\% SSU
rRNA gene sequence identity) to cultured isolates. Both the relative abundance
and the number of cellulose responders increased over time peaking at days 14
and 30 (Figure~\ref{fig:l2fc}, Figure~\ref{fig:rspndr_count}, and Figure~\ref{fig:babund}).
Cellulose responders belonged to the \textit{Proteobacteria} (46\%),
\textit{Verrucomicrobia} (16\%), \textit{Planctomycetes} (16\%),
\textit{Chloroflexi} (8\%), \textit{Bacteroidetes} (8\%),
\textit{Actinobacteria} (3\%), and \textit{Melainabacteria} (1 OTU)
(Table~\ref{tab:cell}). 

% Fakesubsubsection:The majority (85\%) of cellulose
The majority (85\%) of cellulose responders outside of the
\textit{Proteobacteria} shared  $<$ 97\% SSU rRNA gene sequence identity to
bacteria cultured in isolation. For example, 70\% of the
\textit{Verrucomicrobia} cellulose responders fell within unidentified
\textit{Spartobacteria} clades (Figure~\ref{fig:tiledtree}), and these shared $<$
85\% SSU rRNA gene sequence identity to any characterized isolate. The
\textit{Spartobacteria} OTU ``OTU.2192'' exemplified many cellulose responders
(Table~\ref{tab:cell}, Figure~\ref{fig:example}).
``OTU.2192'' increased in non-fractionated DNA relative abundance with time and
evidence for $^{13}$C-labeling of ``OTU.2192'' in the $^{13}$C-cellulose
treatment increased over time with the strongest evidence at days~14
and~30 (Figure~\ref{fig:example}). Most \textit{Chloroflexi} cellulose
responders belonged to an unidentified clade within the
\textit{Herpetosiphonales} (Figure~\ref{fig:tiledtree}) and they shared
$<$ 89\% SSU rRNA gene sequence identity to any characterized isolate.
Characteristic of \textit{Chloroflexi} cellulose responders, "OTU.64"
increased in relative abundance over 30 days and evidence for
$^{13}$C-labeling of ``OTU.64'' in the $^{13}$C-cellulose treatment peaked
days 14 and~30 (Figure~\ref{fig:example}). \textit{Bacteroidetes}
cellulose responders fell within
the \textit{Cytophagales} in contrast with \textit{Bacteroidetes} xylose
responders that belonged instead to the \textit{Flavobacteriales} or
\textit{Sphingobacteriales} (Figure~\ref{fig:tiledtree}).
\textit{Bacteroidetes} cellulose responders included one OTU that shared
100\% SSU rRNA gene sequence identity to a \textit{Sporocytophaga}
species, a genus known to include cellulose degraders. The majority (86\%)
of cellulose responders in the \textit{Proteobacteria} were closely
related ($>$ 97\% identity) to bacteria cultured in isolation, including
representatives of the genera: \textit{Cellvibrio}, \textit{Devosia},
\textit{Rhizobium}, and \textit{Sorangium}, which are all known for their
ability to degrade cellulose (Table~\ref{tab:cell}). Proteobacterial
cellulose responders belonged to \textit{Alpha} (13~OTUs), \textit{Beta}
(4~OTUs), \textit{Gamma} (5~OTUs), and \textit{Delta-proteobacteria}
(6~OTUs).

\subsection{Characteristics of cellulose and xylose responders}
% Fakesubsubsection:Cellulose responders tended
Cellulose responders, relative to xylose responders, tended to have lower
relative abundance in non-fractionated DNA, demonstrated signal consistent with
higher atom \% $^{13}$C in labeled DNA, and had lower estimated \textit{rrn}
copy number (Figure~\ref{fig:shift}). OTUs that assimilated C from either
cellulose or xylose were also clustered phylogenetically (see below) indicating
that these traits were not dispersed randomly across bacterial species. 

% Fakesubsubsection: In the non-fractionated DNA
In the non-fractionated DNA, cellulose
responders had lower relative abundance (1.2 x 10$^{-3}$ (s.d. 3.8
x 10$^{-3}$)) than xylose responders (3.5 x 10$^{-3}$ (s.d. 5.2 x 10$^{-3}$))
(Figure~\ref{fig:xyl_count}, P-value~$=$~1.12 x 10$^{-5}$, Wilcoxon Rank Sum
test). Six of the ten most common OTUs observed in the non-fractionated DNA
responded to xylose, and, seven of the ten most abundant responders to xylose
or cellulose in the non-fractionated DNA were xylose responders.

% Fakesubsubsection:DNA buoyant density increases as the amount
DNA buoyant density (BD) increases in proportion to atom \% $^{13}$C.
Hence, the extent of $^{13}$C incorporation into DNA can be evaluated by
the difference in BD between $^{13}$C-labeled and unlabeled DNA. We
calculated for each OTU its mean BD weighted by relative abundance to
determine its ``center of mass'' within a given density gradient. We then
quantified for each OTU the difference in center of mass between control
gradients and gradients from $^{13}$C-xylose or $^{13}$C-cellulose treatments
(see SI for the detailed calculation, Figure~\ref{fig:c1}). We refer to the
change in center of mass position for an OTU in response to $^{13}$C-labeling
as $\Delta\hat{BD}$. This value can be used to compare relative
differences in $^{13}$C-labeling between OTUs. $\Delta\hat{BD}$ values,
however, are not comparable to the BD changes observed for DNA from pure
cultures both because they are  based on relative abundance in density gradient
fractions (and not DNA concentration) and because isolated strains grown in
uniform conditions generate uniformly labeled molecules while OTUs composed of
heterogeneous strains in complex environmental samples do not. Cellulose
responder $\Delta\hat{BD}$ (0.0163 g mL$^{-1}$ (s.d.~0.0094)) was greater than
that of xylose responders (0.0097 g mL$^{-1}$ (s.d.~0.0094))
(Figure~\ref{fig:shift}, P-value~$=$~1.8610 x 10$^{-6}$, Wilcoxon Rank Sum
test). 

% Fakesubsubsection:We predicted the rrn
We predicted the \textit{rrn} gene copy number for responders as described
\citep{Kembel_2012}. The ability to proliferate after rapid nutrient
influx correlates positively to a microorganism's \textit{rrn} copy number
\citep{Klappenbach_2000}. Cellulose responders possessed fewer estimated
\textit{rrn} copy numbers (2.7~(1.2~s.d.)) than xylose responders
(6.2~(3.4~s.d.)) ( P~=~1.878 x 10$^{-9}$, Wilcoxon Rank Sum test,
Figure~\ref{fig:shift} and Figure~\ref{fig:copy}). Furthermore, the
estimated \textit{rrn} gene copy number for xylose responders was
inversely related to the day of first response (P~=~2.02 x 10$^{-15}$,
Wilcoxon Rank Sum test, Figure~\ref{fig:copy},Figure~\ref{fig:shift}).

% Fakesubsubsection:We assessed phylogenetic
We assessed phylogenetic clustering of $^{13}$C-responsive OTUs with the
Nearest Taxon Index (NTI) and the Net Relatedness Index (NRI)
\citep{Webb2000}. We also quantified the average clade depth of cellulose and
xylose responders with the consenTRAIT metric \citep{Martiny2013}. Briefly, the
NRI and NTI evaluate phylogenetic clustering against a null model for the
distribution of a trait in a phylogeny. The NRI and NTI values are z-scores or
standard deviations from the mean and thus the greater the magnitude of the
NRI/NTI, the stronger the evidence for clustering (positive values) or
overdispersion (negative values). NRI assesses overall clustering whereas the
NTI assesses terminal clustering \citep{Evans2014a}. The consenTRAIT metric is
a measure of the average clade depth for a trait in a phylogenetic tree. NRI
values indicate that cellulose responders clustered overall and at the tips of
the phylogeny (NRI:~4.49, NTI:~1.43) while xylose responders clustered
terminally (NRI:~-1.33, NTI:~2.69). The consenTRAIT clade depth for xylose and
cellulose responders was~0.012 and~0.028 SSU rRNA gene sequence dissimilarity,
respectively. As reference, the average clade depth as inferred from genomic
analyses or growth in culture is approximately~0.017,~0.013 and~0.034 SSU rRNA gene sequence
dissimilarity for arabinase (arabinose like xylose is a five C sugar found in
hemicellulose), glucosidase and cellulase, respectively
\citep{Martiny2013,Berlemont2013}. These results indicate xylose responders
form terminal clusters dispersed throughout the phylogeny while cellulose
responders form deep clades of terminally clustered OTUs.


\section{Discussion} 
% Fakesubsubsection: We highlight to key results
We highlight two key results with implications for understanding structure-function
relationships in soils, and for applying DNA-SIP in future studies of the soil-C
cycle. First, cellulose responders were members of physiologically undescribed
taxonomic groups with few exceptions. This suggests that we have much to learn
about the diversity of structural-C decomposers in soil before we can begin to
assess how they are affected by climate change and land management. Second, the
response to xylose was characterized by a succession in activity from
\textit{Paenibacillus} OTUs (day 1) to \textit{Bacteroidetes} (day 3) and finally
\textit{Micrococcales} (day 7). Notably,  \textit{Paenibacillus} have been
previously shown by DNA-SIP to metabolize glucose \citep{Verastegui_2014}, also a common 
sugar in plant biomass. This activity succession was mirrored
by relative abundance profiles and may mark trophic-C exchange between these
groups. While trophic exchange has been observed previously in DNA-SIP studies
\citep{lueders2004b} most applications of DNA-SIP focus on proximal use of
labeled substrates. However, with increased sensitivity, DNA-SIP is well suited
to tracking C flows throughout microbial communities over time and is not
limited only to observing the entry point for a given substrate into the soil
C-cycle.  Trophic interactions will critically influence how the global soil-C
reservoir will respond to climate change \citep{Crowther2015} but we know
little of biological interactions among soil bacteria. Often bacteria are cast
as a single trophic level \citep{Moore1988} but it may be appropriate to
investigate the soil food web at greater granularity. Additionally, our results
show that DNA-SIP results can change dramatically over time suggesting that
multiple time points are necessary to rigorously and comprehensively describe
which microorganisms consume $^{13}$C-labeled substrates in nucleic acid SIP
incubations.

% Fakesubsubsection: Microorganisms that consumed
Bacteria that consumed $^{13}$C-cellulose were seldom related closely to any
physiologically characterized cultured isolates but were members of
cosmopolitan phylogenetic groups in soil including \textit{Spartobacteria},
\textit{Planctomycetes}, and \textit{Chloroflexi}. Often cellulose responders
were less than 90\% related to their closest cultured relatives showing that we
can infer little, if anything at all, of their physiology from culture-based
studies. Notably, many \textit{Spartobacteria} were among the cellulose responder OTUs.
This is particularly interesting as \textit{Spartobacteria} are globally
distributed and found in a variety of soil types \citep{Bergmann_2011}. These lineages may play
important roles in global cellulose turnover (please see SI note 1 for further
discussion of the phylogenetic affiliation of cellulose responders). 

The turnover of cellulose and plant-derived sugars in soil has been studied
previously using DNA-SIP (e.g. \citep{Verastegui_2014}). Similar to our study,
phylotypes among the \textit{Chloroflexi}, \textit{Bacteroidetes} and
\textit{Planctomycetes} have all been previously implicated in soil cellulose
degradation \citep{Schellenberger_2010}. Additionally, functional metagenomics
enabled by DNA-SIP has identified glycoside hydrolases putatively belonging
to \textit{Cellvibrio} and \textit{Spartobacteria} further suggesting a role
for these organisms in cellulose breakdown in soils \citep{Verastegui_2014}.
Fungi undoubtedly also contribute to the decomposition of cellulose in soils
\citep{boer_2005}, but they are not a focus of this experiment. It should be
noted that longstanding hypotheses that delineate the life history strategies
of fungi and bacteria on the basis of substrate preference have been recently
questioned \citep{rousk_2015}. The approach we describe is also suitable for
observing the activity of fungi (by targeting genetic markers in fungi with
fungal specific PCR primers) and should prove useful in testing hypotheses
that explain the functional traits of both bacteria and fungi as they occur in
soils.

% Fakesubsubsection: In addition to taxonomic identity, we quantified
In addition to taxonomic identity, we quantified four ecological properties of
microorganisms that were actively engaged in labile and structural C
decomposition in our experiment: (1) time of activity, (2) estimated
\textit{rrn} gene copy number, (3) phylogenetic clustering, and (4) density
shift in response to $^{13}$C-labeling. Labile C was consumed before structural C and 
these substrates were consumed by different microorganisms
(Figure~\ref{fig:ord}). This was expected and is consistent with the
degradative succession hypothesis. Consumers of
labile C had higher estimated \textit{rrn}
gene copy number than structural C consumers (Figure~\ref{fig:shift}).  \textit{rrn} copy number is
positively correlated with the ability to resuscitate quickly in response to
nutrient influx \citep{Klappenbach_2000} which may be the advantage that
enabled xylose responders to rapidly consume xylose. Both xylose and cellulose
responders were terminally clustered phylogenetically suggesting that the ability
to use these substrates was phylogenetically constrained. Although labile C
consumption is generally considered to be mediated by a diverse set of 
microorganisms, we found that xylose responders at day 1 were mainly members of
one genus, \textit{Paenibacillus}. Our results suggests that life-history
traits such as the ability to resuscitate
quickly and/or grow rapidly may be more important in determining the
diversity of microorganisms that actually mediate a given process than the genomic
potential for substrate utilization (see SI note 2 for further discussion with respect to
soil-C modelling). And last, labile C consumers, in contrast to structural C consumers, 
had lower $\Delta\hat{BD}$ in response to $^{13}$C-labeling.
This result suggests that labile C consumers were generalists, assimilating C
from a variety of sources both labeled and unlabeled, while structural C
consumers were more likely to be specialists and more closely associated
with C from a single source.

% Fakesubsubsection: We propose that the temporal fluctuations
We propose that the temporal fluctuations in $^{13}$C-labeling in the
$^{13}$C-xylose treatment are due to trophic exchange of $^{13}$C.
Alternatively, the temporal dynamics could be caused by microorganisms tuned to
different substrate concentrations and/or cross-feeding. However, trophic exchange
would explain well the precipitous drop in abundance of
\textit{Paenibacillus} after day 1 with subsequent $^{13}$C-labeling of
\textit{Bacteroidetes} at day 3 as well as the precipitous drop in abundance of
\textit{Bacteroidetes} at day 3 followed by $^{13}$C-labeling of Micrococcales at day 7.
Trophic exchange could be enabled by mother cell lysis (in the case of spore formers
such as \textit{Paenibacillus}), viral lysis, and/or the direct indirect effects of
predation. \textit{Bacteroidetes} types have been shown to become
$^{13}$C-labeled after the addition of live $^{13}$C-labeled
\textit{Escherichia coli} to soil \citep{Lueders2006} indicating their ability to
assimilate C from microbial biomass. In addition, the dominant OTU labeled in the $^{13}$C-xylose
treatment from the \textit{Micrococcales} shares 100\% SSU rRNA gene sequence identity
to \textit{Agromyces ramosus} a known predator that
feeds upon on many microorganisms including yeast and \textit{Micrococcus luteus}
\citep{16346402}. \textit{Agromyces} are abundant microorganisms in many soils
and \textit{Agromyces ramosus} was the most abundant xylose responder in our
experiment -- the fourth most abundant OTU in our dataset. It is notable however,
that if \textit{Agromyces ramosus} is acting as a predator in our experiment, the
organism remains unlabeled in response to $^{13}$C-cellulose which suggests that its
activity may be specific for certain prey or for certain environmental
conditions  (see SI note 3 for further discussion of trophic C exchange).
Climate change is expected to diminish bottom-up controls on microbial growth
increasing the importance on top-down biological interactions for mitigating
positive climate change feedbacks \citep{Crowther2015}. Currently the extent
of bacterial predatory activity in soil, and its consequences for the soil C-cycle
and carbon use efficiency is largely unknown. Elucidating the identities of
bacterial predators in soil will assist in assessing the implications of
climate change on global soil-C storage.

\subsection{Conclusion} 
% Fakesubsubsection: Microorganisms sequester atmospheric C
Microorganisms govern C-transformations in soil and thereby influence global
climate but still we do not know the specific identities of microorganisms that carry out 
critical C transformations. In this experiment microorganisms from physiologically
uncharacterized but cosmopolitan soil lineages participated in cellulose
decomposition. Cellulose responders included members of the
\textit{Verrucomicrobia} (\textit{Spartobacteria}), \textit{Chloroflexi},
\textit{Bacteroidetes} and \textit{Planctomycetes}. \textit{Spartobacteria} in
particular are globally cosmopolitan soil microorganisms and are often the most
abundant \textit{Verrucomicrobia} order in soil \citep{Bergmann_2011}.
Fast-growing aerobic spore formers from \textit{Firmicutes} assimilated labile
C in the form of xylose. Xylose responders within the \textit{Bacteroidetes}
and \textit{Actinobacteria} likely became labeled by consuming $^{13}$C-labeled
constituents of microbial biomass either by saprotrophy or predation. Our
results suggest that cosmopolitan \textit{Spartobacteria} may degrade cellulose
on a global scale, decomposition of labile plant C may initiate trophic transfer 
within the bacterial food web, and life history traits may act
as a filter constraining the diversity of active microorganisms relative to
those with the genomic potential for a given metabolism.


\section{Methods}
%
All code to take raw SSU rRNA gene sequencing reads to final publication
figures and through all presented analyses is located at the following URL:\\
\url{https://github.com/chuckpr/CSIP\_succession\_data\_analysis}.\\ DNA sequences
are deposited on MG-RAST (Accession XXXXXXX).

% Fakesubsubsection: Twelve soil cores
Twelve soil cores (5 cm diameter x 10 cm depth) were collected from six 
sampling locations within an organically managed agricultural field in Penn
Yan, New York. Soils were sieved (2 mm), homogenized, distributed into flasks
(10 g in each 250 ml flask, n = 36) and equilibrated for 2 weeks. The soil type
was Honeoye previously measured to be approximately neutral pH (for a thorough
description of the site see \citet{Berthrong_2013}). We amended
soils with a mixture containing 2.9 mg C g$^{-1}$ soil dry weight (d.w.) and
brought soil to 50\% water holding capacity. By mass the amendment
contained 38\% cellulose, 23\% lignin, 20\% xylose, 3\% arabinose, 1\%
galactose, 1\% glucose, and 0.5\% mannose. 10.6\% amino acids (made in house
based on Teknova C9795 formulation) and 2.9\% Murashige Skoog basal salt
mixture which contains macro and micro-nutrients that are associated with plant
biomass (Sigma Aldrich M5524). This mixture approximates the molecular
composition of switchgrass biomass with hemicellulose replaced by its
constituent monomers \citep{Schneckenberger_2008}. We set up three parallel
treatments varying the isotopically labeled component in each treatment. The
treatments were (1) a control treatment with all unlabeled components, (2) a
treatment with $^{13}$C-cellulose instead of unlabeled cellulose (synthesized
as described in SI), and (3) a treatment with $^{13}$C-xylose (98 atom\%
$^{13}$C, Sigma Aldrich) instead of unlabeled xylose. Other details relating to
substrate addition can be found in SI. Microcosms were sampled destructively at
days~1 (control and xylose only),~3,~7,~14, and~30 and soils were stored at
-80 $^{\circ}$C until nucleic acid extraction. The abbreviation "13CXPS" refers
to the $^{13}$C-xylose treatment ($^{13}$C Xylose Plant Simulant), "13CCPS"
refers to the $^{13}$C-cellulose treatment, and "12CCPS" refers to the control
treatment.

We used DESeq2 (R package), an RNA-Seq differential expression statistical
framework \citep{love2014}, to identify OTUs that were enriched in high density
gradient fractions from $^{13}$C-treatments relative to corresponding gradient
fractions from control treatments (for review of RNA-Seq differential
expression statistics applied to microbiome OTU count data see
\citep{McMurdie2014}). We define "high density gradient fractions" as gradient
fractions whose density falls between 1.7125 and 1.755 g ml$^{-1}$. For each OTU,
we calculates logarithmic fold change (LFC) and corresponding standard error for
enrichment in high density fractions of $^{13}$C treatments relative to control.
Subsequently, a one-sided Wald test was used to assess the statistical significance
of LFC values with the null hypothesis that LFC was less than one standard deviation
above the mean of all LFC values. We independently filtered OTUs prior to multiple
comparison corrections on the basis of sparsity eliminating OTUs that failed to 
appear in at least 45\% of high density fractions for a given comparison. P-values
were adjusted for multiple comparisons using the Benjamini and Hochberg method
\citep{benjamini1995}. We selected a false discovery rate of 10\% to denote
statistical significance.

See SI for additional information on experimental and analytical methods.


\subsection{Acknowledgements} The authors would like to acknowledge the
assistance of John Christian Gaby and Mallory Choudoir in developing the method
used to produce $^{13}$C-labeled cellulose. We would also like to thank
Ruth Ley, Steve Zinder, Nelson Hairston, and Nick Youngblut for providing comments that
were helpful in the development of this manuscript. Sander Hunter assisted with
the microcosm set up. This material is based upon
work supported by the Department of Energy Office of Science, Office of
Biological \& Environmental Research Genomic Science Program under Award Numbers
DE-SC0004486 and DE-SC0010558. Manuscript preparation by Ashley N. Campbell was
performed under the auspices of the U.S. Department of Energy by LLNL under
Contract DE-AC52-07NA27344.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/l2fc-time/l2fc-time}
\caption{{Change in non-fractionated DNA relative abundance versus time (expressed
as LFC) for OTUs that changed significantly over time (P-value $<$ 0.10, Wald test).
Each panel shows one phylum (labeled on the right). The taxonomic class is
indicated on the left. OTUs that responded to just xylose are shown in 
green, just cellulose in blue, and both xylose and cellulose in red.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.77\columnwidth]{figures/abndVtime-class/abndVtime-class}
\caption{{Relative abundance in non-fractionated DNA versus time for classes that changed significantly.
Samples from different treatments are labeled with different colors as
indicated in the scale. Statistical analysis is presented in main text.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/ordination-all1/ordination-all}
\caption{{NMDS ordination of SSU rRNA gene sequence composition in
gradient fractions shows that variation between fractions is
correlated with fraction density, isotopic labeling, and
time. Dissimilarity in SSU rRNA gene sequence composition was quantified using the 
weighted UniFrac metric. SSU rRNA gene sequences were surveyed in twenty
gradient fractions at each sampling point for each treatment (Figure~S1).
$^{13}$C-labeling of DNA is apparent because the SSU rRNA gene sequence composition of
gradient fractions from $^{13}$C and control treatments differ at high density.
Each point on the NMDS plot represents one gradient fraction.  SSU rRNA gene sequence
composition differences between gradient fractions were quantified by the
weighted Unifrac metric. The size of each point is positively correlated with
density and colors indicate the treatment (A) or day (B).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/l2fc-fig1/l2fc-fig}
\caption{{Enrichment of OTUs in either $^{13}$C-cellulose (13CCPS, upper panels) or
$^{13}$C-xylose (13CXPS, bottom panels) treatments relative to control,
expressed as LFC (see Methods). Each point indicates the LFC for a single OTU.
High enrichment values indicate an OTU is likely $^{13}$C-labeled. Different
colors represent different phyla and different panels represent different days.
The final column shows the frequency distribution of LFC values in each row.
Within each panel, shaded areas are used to indicate one standard deviation
(dark shading) or two standard deviations (light shading) about the mean of all
LFC values.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/13C-chart/13C-chart}
\caption{{The metabolization of $^{13}$C-xylose and $^{13}$C-cellulose is indicated by
the percentage of the added $^{13}$C that remains in soil over time. Soils
were pooled (three samples per time point per treatment) prior to measuring
$^{13}$C-content.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.56\columnwidth]{figures/xylose-rspndr-bar/xylose-rspndr-bar}
\caption{{Xylose reponders in the \textit{Actinobacteria}, \textit{Bacteroidetes},
\textit{Firmicutes} exhibit distinct temporal dynamics of $^{13}$C-labeling.
The left column shows counts of $^{13}$C-xylose responders in the
\textit{Actinobacteria, Bacteroidetes, Firmicutes} and \textit{Proteobacteria}
at days 1, 3, 7 and 30. The right panel shows OTU enrichment in high density
gradient fractions (gray points, expressed as fold change) for responders
as well as a boxplot for the distribution of fold change values
(The box extends one interquartile range, whiskers extend 1.5 times
the IR, and small dots are outliers (i.e. beyond 1.5 times the IR)).
Each day in the right column shows all responders (i.e.
OTUs that responded to xylose at any point in time). High enrichment values 
indicates OTU DNA is likely $^{13}$C-labeled.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/shift-and-rabund3/shift-and-rabund}
\caption{{Characteristics of xylose responders (green) and cellulose responders (blue)
based on estimated \textit{rrn} copy number (A), $\Delta\hat{BD}$ (B), and
relative abundance in non-fractionated DNA (C). The estimated \textit{rrn} copy
number of all responders is shown versus time (A). Kernel density histogram of
$\Delta\hat{BD}$ values shows cellulose responders had higher average
$\Delta\hat{BD}$ than xylose responders indicating higher average atom \%
$^{13}$C in OTU DNA (B). The final panel indicates the rank relative abundance
of all OTUs observed in the non-fractionated DNA (C) where rank was determined
at day 1 (bold line) and relative abundance for each OTU is indicated for all
days by colored lines (see legend). Xylose responders (green ticks) have higher
relative abundance in non-fractionated DNA than cellulose responders (green ticks). 
All ticks are based on day 1 relative abundance.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/copy-number/copy-number}
\caption{{Estimated \textit{rrn} copy number for xylose and cellulose responders. Theleftmost panel contrasts estimated \textit{rrn} copy number for cellulose(13CCPS) and xylose (13CXPS) responders. The right panel shows estimated\textit{rrn} copy number versus time of first response for xylose responders.Colors denote the phylum of the OTUs (see legend).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/all-rspndr-bar/all-rspndr-bar}
\caption{{Counts of xylose responders and cellulose responders over time.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/bulk-ordination/bulk-ordination}
\caption{{NMDS analysis of SSU rRNA gene composition in non-fractionated DNA (colored points) indicates
that isotopic labelling does not alter overall microbial community composition,
microbial community composition in the soil microcosms changes over time, and
variance in non-fractionated DNA is smaller than variance in fractionated DNA (black points).
SSU rRNA gene sequences were determined for non-fractionated DNA from the
unlabeled control, $^{13}$C-xylose, and $^{13}$C-cellulose treatments over time (colors
indicate time, different symbols used for different treatments). Distance in SSU
rRNA gene composition was quantified with the UniFrac metric. The
leftmost panel indicates NMDS of data from both non-fractionated and
fractionated samples. The rightmost panel indicates NMDS of data only from
non-fractionated DNA. Statistical analysis is presented in main text. Samples not
represented in the ordination did not sequence successfully and constitute
missing data (e.g. ``12CCPS'' day 7).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/bulk-phylum-rspndr-abund/abund-v-time-phyla}
\caption{{Change in relative abundance in non-fractionated DNA over time for xylose
responders (13CXPS) and cellulose responders (13CCPS). Each panel represents
a responders to the indicated substrate (i.e. cellulose (13CCPS) or xylose (13CXPS)) 
within the indicated phylum except for the lower right panel which shows all reponders to both
xylose and celluose. The abbreviations Proteo., Verruco., and Plancto.,
correspond to \textit{Proteobacteria}, \textit{Verrucomicrobia}, and \textit{Planctomycetes},
respectively.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/conceptual1/conceptual1}
\caption{{Density profile for a single cellulose responder in the $^{13}$C-cellulose
treatment (blue) and control (orange). Vertical lines show center of mass for
each density profile and the arrow denotes the magnitude and direction of
$\Delta\hat{BD}$. Right panel shows relative abundance values in the high
density fractions (The boxplot line is the median value. The box spans one
interquartile range (IR) about the median, whiskers extend 1.5 times the IR.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/conceptual3/conceptual3}
\caption{{Density profile for a single non-responder OTU. The $^{13}$C-cellulose
treatment is in blue and the control treatment is in orange. The vertical line
shows where high density fractions begin as defined in our analysis. The right
panel shows relative abundance values in the high density fractions for each
gradient (The boxplot line is the median value. The box spans one
interquartile range (IR) about the median, whiskers extend 1.5 times the IR.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/generalist-specialist/generalist-specialist}
\caption{{Maximum enrichment at any point in time in high density fractions
of $^{13}$C-treatments relative to control (expressed as LFC) shown
for $^{13}$C-cellulose versus $^{13}$C-xylose treatments. Each point
represents an OTU. Blue points are cellulose responders, green xylose
responders, red are responders to both xylose and cellulose, and gray points
are OTUs that did not respond to either substrate. Line indicates a slope of
one.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/tiled-tree/tiled-tree}
\caption{{Phylogenetic position of cellulose responders and xylose responders in the
context of all OTUs that passed sparsity independent filtering criteria (see
Methods). Only those phyla that contain responders are shown.  Colored dots are
used to identify xylose responders (green) and cellulose responders (blue). The
heatmaps indicate enrichment in high density fractions relative to control
(represented as LFC) for each OTU in response to both $^{13}$C-cellulose
(13CCPS, leftmost heatmap) and $^{13}$C-xylose (13CXPS, rightmost heatmap) with
values for different days in each heatmap column. High enrichment values
(represented as LFC) provide evidence of $^{13}$C-labeled DNA.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/20150320methods-conceptual/20150320methods-conceptual}
\caption{{We added a carbon mixture that contained inorganic
salts and amino acids (not shown here) to each soil microcosm where the
only difference between treatments was the $^{13}$C-labeled isotope (in red). At days
1, 3, 7, 14, and 30 replicate microcosms were destructively harvested for
downstream molecular applications. DNA from each treatment and time (n = 14)
was subjected to CsCl density gradient centrifugation and density gradients
were fractionated (orange tubes wherein each arrow represents a fraction from
the density gradient). SSU rRNA genes from each gradient fraction were PCR
amplified and sequenced. In addition, SSU rRNA genes were also PCR amplified
and sequenced from non-fractionated DNA to represent the soil microbial
community.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/example/examples}
\caption{{Raw data from individual responders highlighted in the main text (see
Results). The left column shows OTU relative abundance in density gradient 
fractions for the indicated treatment pair at each sampling point. Time is
indicated by the line color (see legend). Gradient profiles are shaded
to represent the different treatments where orange represents ``control'',
blue ``$^{13}$C-cellulose'', and green ``$^{13}$C-xylose.'' The right column
shows the relative abundance of each OTU in non-fractionated DNA. Enrichment in the
high density fractions of $^{13}$C-treatments indicates an OTU likely
has $^{13}$C-labeled DNA.%
}}
\end{center}
\end{figure}

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