unearthing the microbial ecology of soil carbon cycling ...€¦ · 20.07.2015 · ing for...

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Unearthing the microbial ecology of soilcarbon cycling with DNA-SIPCharles Pepe-Ranney and Ashley N Campbell ∗ †, Chantal Koechli † , Sean Berthrong, and Daniel HBuckley † ‡

∗These authors contributed equally to the manuscript and should be considered co-first authors,†School of Integrative5

Plant Sciences, Cornell University, New York, USA, and ‡corresponding author

AbstractWe explored the dynamics of microbial contribu-tions to decomposition in soil by coupling DNAStable Isotope Probing (SIP) and high through-10

put DNA sequencing. Our experiment evaluatedthe degradative succession hypothesis, describeddynamics of carbon (C) metabolism during or-ganic matter degradation, and characterized bac-teria that metabolize labile and structural C in15

soils. We added a complex amendment represent-ing plant derived organic matter to soil substitut-ing 13C-xylose or 13C-cellulose for unlabeled equiv-alents in two experimental treatments which weremonitored for 30 days. Xylose and cellulose are20

abundant components in plant biomass and rep-resent labile and structural C pools, respectively.We characterized 5,940 SSU rRNA gene opera-tional taxonomic units (OTUs) finding evidence for13C-incorporation into DNA from 13C-xylose and2513C-cellulose in 49 and 63 OTUs, respectively. Inthe 13C-xylose treatment the types of microorgan-isms that incorporated 13C into DNA changed overtime dominated by Firmicutes at day 1 followedby Bacteroidetes at day 3 and then Actinobacteria30

at day 7. These dynamics of 13C-labeling suggestlabile C traveled through different trophic levelswithin the soil bacterial community. The microor-ganisms that metabolized cellulose-C increased inrelative abundance over the course of the experi-35

ment with the highest number of OTUs exhibitingevidence for 13C-assimilation after 14 days. Mi-crobes that metabolized cellulose-C belonged tocosmopolitan soil lineages that remain uncharac-terized including Spartobacteria, Chloroflexi and40

Planctomycetes. Using an approach that revealsthe C assimilation dynamics of specific microbiallineages we describe the ecological properties offunctionally defined microbial groups that con-tribute to decomposition in soil.45

stable isotope probing | structure-function relationships | soil mi-

crobial ecology | 16S rRNA gene

Abbreviations: C, Carbon; OTU, Operational Taxonomic Unit; SOM,

Soil Organic Matter; BD, Buoyand Density; SIP, Stable Isotope Prob-

ing50

SignificanceSoil microorganisms drive C flux through the ter-restrial biosphere, and models that predict terres-trial C flux can benefit by accounting for microbialecophysiology in soils. However, characterizing the55

ecophysiology of microbes that mediate C decom-position in soil has proven difficult due to theiroverwhelming diversity. We characterized micro-bial C metabolism in soil and show that differenttypes of C have distinct decomposition dynamics60

governed by different microbial lineages. For ex-ample, we found that uncharacterized microbialtaxa, which are cosmopolitan in soils, assimilatedcellulose-C into DNA. These microbes may drivecellulose decomposition on a global scale. We iden-65

tify microbial lineages engaging in labile and struc-tural C decomposition and explore their ecologicalproperties.

IntroductionSoils worldwide contain 2,300 Pg of carbon (C)70

which accounts for nearly 80% of the C presentin the terrestrial biosphere [1, 2]. C respirationby soil microorganisms produces annually tenfoldmore CO2 than fossil fuel emissions [3]. Despitethe contribution of microorganisms to global C75

flux, many global C models ignore the diversityof microbial physiology [4–6] and we still know lit-tle about the ecophysiology of soil microorganisms.Such knowledge should assist the development andrefinement of global C models [7–10].80

Most plant C is comprised of cellulose (30-50%)followed by hemicellulose (20-40%), and lignin (15-

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25%) [11]. Hemicellulose, being the most soluble,degrades in the early stages of decomposition. Xy-lans are often an abundant component of hemicel-85

lulose, and xylans include differing amounts of xy-lose, glucose, arabinose, galactose, mannose, andrhamnose [12]. Xylose is often the most abundantsugar in hemicellulose, comprising as much as 60-90% of xylan in some plants (e.g hardwoods [13],90

wheat [14], and switchgrass [15]). Microbes thatrespire labile C in the form of sugars proliferateduring the initial stages of decomposition [16, 17],and metabolize as much as 75% of sugar C duringthe first 5 days [18]. In contrast, cellulose decom-95

position proceeds more slowly with rates increas-ing for approximately 15 days while degradationcontinues for 30-90 days [18, 19]. It is hypothe-sized that different microbial guilds mediate thedecomposition of different plant biomass compo-100

nents [19–22]. The degradative succession hypoth-esis posits that fast growing organisms proliferatein response to the labile fraction of plant biomasssuch as sugars [23, 24] followed by slow growingorganisms targeting structural C such as cellulose105

[23]. Evidence to support the degradative succes-sion hypothesis comes from observing soil respi-ration dynamics and characterizing microbes cul-tured at different stages of decomposition. Thedegree to which the succession hypothesis presents110

an accurate model of litter decomposition has beenquestioned [21, 25, 26] and it’s clear that we neednew approaches to dissect microbial contributionsto C transformations in soils.

Though microorganisms mediate 80-90% of the115

soil C-cycle [27, 28], and microbial communitycomposition can account for significant variationin C mineralization [29], terrestrial C-cycle mod-els rarely consider the community composition ofsoils [30, 31]. Rates of soil C transformations are120

measured without knowledge of the organisms thatmediate these reactions [28] leaving the importanceof community membership towards maintainingecosystem function undefined [28, 32, 33]. Vari-ation in microbial community composition can be125

linked effectively to rates of soil processes whendiagnostic genes for specific functions are avail-able (e.g. denitrification [34], nitrification [35–37],methanotrophy [38], and nitrogen fixation [39]).However, the lack of diagnostic genes for describing130

soil-C transformations has limited progress in char-acterizing the contributions of individual microbesto decomposition. Remarkably, we still lack basicinformation on the physiology and ecology of themajority of organisms that live in soils. For exam-135

ple, contributions to soil processes remain unchar-acterized for cosmopolitan bacterial phyla in soilsuch as Acidobacteria, Chloroflexi, Planctomycetes,and Verrucomicrobia. These phyla combined can

comprise 32% of soil microbial communities (based140

on surveys of the SSU rRNA genes in soil) [40, 41].Characterizing the functions of microbial taxa

has relied historically on culturing microorganismsand subsequently characterizing their physiologyin the laboratory, and on environmental surveys of145

genes diagnostic for specific processes. But, mostmicroorganisms are difficult to grow in culture [40]and many biogeochemical processes lack suitablediagnostic genes. Nucleic acid stable-isotope prob-ing (SIP) links genetic identity and activity with-150

out the need to grow microorganisms in cultureand has expanded our knowledge of microbial con-tributions to biogeochemical processes [42]. How-ever, nucleic acid SIP has notable complicationsincluding the need to add large amounts of la-155

beled substrate [43], label dilution resulting in par-tial labeling of nucleic acids [43–45], the potentialfor cross-feeding and secondary label incorporation[45–50], and variation in genome G+C content [51–54]. As a result, most applications of SIP have tar-160

geted specialized microorganisms such as methan-otrophs [43], methanogens [55], syntrophs [56], ormicrobes that target pollutants [57]. Exploringthe soil-C cycle with SIP has proven to be morechallenging because SIP has lacked the resolution165

necessary to characterize the specific contributionsof individual microbial groups to the decomposi-tion of plant biomass. High throughput DNA se-quencing technology, however, improves the resolv-ing power of SIP [58].170

Coupling SIP with high throughput DNA se-quencing now enables exploration of microbial C-cycling in soils. SSU rRNA amplicons are readilysequenced from numerous density gradient frac-tions across multiple samples thereby increasing175

the resolution of a typical nucleic acid SIP experi-ment [59]. It is now possible to use far less isotopi-cally labeled substrate resulting in more environ-mentally realistic experimental conditions [58]. Wehave employed such a high resolution DNA stable180

isotope probing approach to explore the assimila-tion of xylose and/or cellulose into bacterial DNAin an agricultural soil.

Specifically, we added to soil a complex amend-ment representative of organic matter derived from185

fresh plant biomass. All treatments received thesame amendment but the identity of isotopicallylabeled substrates was varied between treatments.Specifically, we set up a control treatment where allcomponents were unlabeled, a treatment with 13C-190

xylose instead of unlabeled xylose, and a treatmentwith 13C-cellulose instead of unlabeled cellulose.Soil was sampled at days 1, 3, 7, 14, and 30 andwe identified microorganisms that assimilated 13Cinto DNA at each point in time. The experiment195

was designed to provide a test of the degradativesuccession hypothesis as it applies to soil bacteria,



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to identify soil bacteria that metabolize xylose andcellulose, and to characterize temporal dynamics ofxylose and cellulose metabolism in soil.200

ResultsAfter adding the organic matter amendment tosoil, we tracked the flow of 13C from 13C-xylose or13C-cellulose into microbial DNA over time usingDNA-SIP (Figure S1). The amendment consisted205

of compounds representative of plant biomass in-cluding cellulose, lignin, sugars found in hemicellu-lose, amino acids, and inorganic nutrients (see Sup-plemental Information (SI)). The amendment wasadded at 2.9 mg C g−1 soil dry weight (d.w.), and210

this comprised 19% of the total C in the soil. Thecellulose-C (0.88 mg C g−1 soil d.w.) and xylose-C (0.42 mg C g−1 soil d.w.) in the amendmentcomprised 6% and 3% of the total C in the soil, re-spectively. The soil microbial community respired215

65% of the xylose within one day and 29% of theadded xylose remained in the soil at day 30 (Fig-ure S2). In contrast, cellulose-C declined at a rateof approximately 18 µg C d −1 g−1 soil d.w. and40% of added cellulose-C remained in the soil at220

day 30 (Figure S2).

Types of 13C-labeled OTUs changed with timeand substrate. We assessed assimilation of 13C intomicrobial DNA by comparing the SSU rRNA genesequence composition of SIP density gradient frac-225

tions between 13C treatments and the unlabeledcontrol (see Methods and SI). In the gradient den-sity fractions for the control treatment, fractiondensity represented the majority of the variance inSSU rRNA gene composition (Figure 1). Genome230

G+C content correlates positively with DNA buoy-ant density and influences SSU rRNA gene com-position in gradient fractions [51]. For the 13C-cellulose treatment, the SSU rRNA gene compo-sition in gradient fractions deviated from control235

in high density fractions (> 1.72 g mL−1) ondays 14 and 30 (Figure 1). For the 13C-xylosetreatment, SSU rRNA gene composition in gra-dient fractions also deviated from control in highdensity fractions, but it deviated from control on240

days 1, 3, and 7 (Figure 1). The SSU rRNA genecomposition from the 13C-cellulose treatment and13C-xylose treatment high density gradient frac-tions differed indicating different microorganismsassimilated C from xylose than cellulose (Figure 1).245

Further, in the 13C-cellulose treatment, the SSUrRNA gene sequence composition in high densityfractions was similar on days 14 and 30 indicatingsimilar microorganisms had 13C-labeled DNA in13C-cellulose treatments at days 14 and 30. In con-250

trast, in the 13C-xylose treatment, the SSU rRNAgene composition of high density fractions varied

between days 1, 3, and 7 indicating that differentmicrobes had 13C-labeled DNA on each of thesedays. In the 13C-xylose treatment, the SSU gene255

composition of high density fractions was similarto control on days 14 and 30 (Figure 1) indicat-ing that 13C was no longer detectable in bacterialDNA on these days for this treatment.

Temporal dynamics of OTU relative abundance260

in experimental soil. We monitored the experimen-tal soil microbial community over the course ofthe experiment by surveying SSU rRNA genesin non-fractionated DNA from the experimentalsoil. The SSU rRNA gene composition of the265

non-fractionated DNA changed with time (Fig-ure S3, P-value = 0.023, R2 = 0.63, Adonis test[60]). In contrast, the non-fractionated DNASSU rRNA gene composition showed no statis-tical evidence for changing with treatment (P-270

value 0.23, Adonis test) (Figure S3). The lat-ter result demonstrates the substitution of 13C-labeled substrates for unlabeled equivalents couldnot be shown to alter the soil microbial communitycomposition. Twenty-nine OTUs exhibited suffi-275

cient statistical evidence (adjusted P-value <0.10,Wald test) to conclude they changed in relativeabundance in the non-fractionated DNA over thecourse of the experiment (Figure S4). When SSUrRNA gene abundances were combined at the tax-280

onomic rank of ”class”, the classes that changed inabundance (adjusted P-value < 0.10, Wald test)were the Bacilli (decreased), Flavobacteria (de-creased), Gammaproteobacteria (decreased), andHerpetosiphonales (increased) (Figure S5). Of285

the 29 OTUs that changed in relative abundanceover time, 14 putatively incorporated 13C intoDNA (Figure S4). OTUs that likely assimilated13C from 13C-cellulose into DNA tended to in-crease in relative abundance with time whereas290

OTUs that assimilated 13C from 13C-xylose tendedto decrease (Figure S6). OTUs that responded toboth substrates did not exhibit a consistent rela-tive abundance response over time as a group (Fig-ure S4 and S6).295

Changes in the phylogenetic composition of 13C-labeled OTUs with time.If an OTU exhibitedstrong evidence for assimilating 13C into DNA, werefer to that OTU as a ”responder” (see Meth-ods and SI for our operational definition of ”re-300

sponder”). The SSU rRNA gene sequences pro-duced in this study were binned into 5,940 OTUsand we assessed evidence of 13C-labeling from both13C-cellulose and 13C-xylose for each OTU. Forty-one OTUs responded to 13C-xylose, 55 OTUs re-305

sponded to 13C-cellulose, and 8 OTUs respondedto both xylose and cellulose (Figure 2, Figure 3,Figure S7, Table S1, and Table S2). The number



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of xylose responders peaked at days 1 and 3 anddeclined with time. In contrast, the number of cel-310

lulose responders increased with time peaking atdays 14 and 30 (Figure S8).

The phylogenetic composition of xylose respon-ders changed with time (Figure 2 and Figure 4) and86% of xylose responders shared > 97% SSU rRNA315

gene sequence identity with bacteria cultured inisolation (Table S1). On day 1, Bacilli OTUs rep-resented 84% of xylose responders (Figure 4) andthe majority of these OTUs were closely related tocultured representatives of the genus Paenibacillus320

(Table S1, Figure 3). For example, ”OTU.57” (Ta-ble S1), annotated as Paenibacillus, had a strongsignal of 13C-labeling at day 1 coinciding with itsmaximum relative abundance in non-fractionatedDNA. The relative abundance of “OTU.57” de-325

clined until day 14 and “OTU.57” did not appearto be 13C-labeled after day 1 (Figure S9). Onday 3, Bacteroidetes OTUs comprised 63% of xy-lose responders (Figure 4) and these OTUs wereclosely related to cultured representatives of the330

Flavobacteriales and Sphingobacteriales (Table S1,Figure 3). For example, “OTU.14”, annotatedas a flavobacterium, had a strong signal for 13C-labeling in the 13C-xylose treatment at days 1 and3 coinciding with its maximum relative abundance335

in non-fractionated DNA. The relative abundanceof “OTU.14” then declined until day 14 and didnot show evidence of 13C-labeling beyond day 3(Figure S9). Finally, on day 7, ActinobacteriaOTUs represented 53% of the xylose responders340

(Figure 4) and these OTUs were closely relatedto cultured representatives of Micrococcales (Ta-ble S1, Figure 3). For example, “OTU.4”, anno-tated as Agromyces, had signal for 13C-labeling inthe 13C-xylose treatment on days 1, 3 and 7 with345

the strongest evidence of 13C-labeling at day 7 anddid not appear 13C-labeled at days 14 and 30. Therelative abundance of “OTU.4” in non-fractionatedDNA increased until day 3 and then declined untilday 30 (Figure S9). Proteobacteria were also com-350

mon among xylose responders at day 7 where theycomprised 40% of xylose responder OTUs. No-tably, Proteobacteria represented the majority (6of 8) of OTUs that responded to both celluloseand xylose (Figure S7).355

The phylogenetic composition of cellulose re-sponders did not change with time to the sameextent as the xylose responders. Also, in con-trast to xylose responders, cellulose responders of-ten were not closely related (< 97% SSU rRNA360

gene sequence identity) to cultured isolates. Boththe relative abundance and the number of cel-lulose responders increased over time peaking atdays 14 and 30 (Figure 2, Figure S8, and Fig-ure S6). Cellulose responders belonged to the Pro-365

teobacteria (46%), Verrucomicrobia (16%), Planc-

tomycetes (16%), Chloroflexi (8%), Bacteroidetes(8%), Actinobacteria (3%), and Melainabacteria (1OTU) (Table S2).

The majority (85%) of cellulose responders out-370

side of the Proteobacteria shared < 97% SSUrRNA gene sequence identity to bacteria culturedin isolation. For example, 70% of the Verrucomi-crobia cellulose responders fell within unidentifiedSpartobacteria clades (Figure 3), and these shared375

< 85% SSU rRNA gene sequence identity to anycharacterized isolate. The Spartobacteria OTU“OTU.2192” exemplified many cellulose respon-ders (Table S2, Figure S9). “OTU.2192” increasedin non-fractionated DNA relative abundance with380

time and evidence for 13C-labeling of “OTU.2192”in the 13C-cellulose treatment increased over timewith the strongest evidence at days 14 and 30 (Fig-ure S9). Most Chloroflexi cellulose responders be-longed to an unidentified clade within the Her-385

petosiphonales (Figure 3) and they shared < 89%SSU rRNA gene sequence identity to any charac-terized isolate. Characteristic of Chloroflexi cel-lulose responders, ”OTU.64” increased in relativeabundance over 30 days and evidence for 13C-390

labeling of “OTU.64” in the 13C-cellulose treat-ment peaked days 14 and 30 (Figure S9). Bac-teroidetes cellulose responders fell within the Cy-tophagales in contrast with Bacteroidetes xyloseresponders that belonged instead to the Flavobac-395

teriales or Sphingobacteriales (Figure 3). Bac-teroidetes cellulose responders included one OTUthat shared 100% SSU rRNA gene sequence iden-tity to a Sporocytophaga species, a genus known toinclude cellulose degraders. The majority (86%)400

of cellulose responders in the Proteobacteria wereclosely related (> 97% identity) to bacteria cul-tured in isolation, including representatives of thegenera: Cellvibrio, Devosia, Rhizobium, and So-rangium, which are all known for their ability to405

degrade cellulose (Table S2). Proteobacterial cel-lulose responders belonged to Alpha (13 OTUs),Beta (4 OTUs), Gamma (5 OTUs), and Delta-proteobacteria (6 OTUs).

Characteristics of cellulose and xylose responders.410

Cellulose responders, relative to xylose responders,tended to have lower relative abundance in non-fractionated DNA, demonstrated signal consistentwith higher atom % 13C in labeled DNA, andhad lower estimated rrn copy number (Figure 5).415

In the non-fractionated DNA, cellulose respondershad lower relative abundance (1.2 x 10−3 (s.d. 3.8x 10−3)) than xylose responders (3.5 x 10−3 (s.d.5.2 x 10−3)) (Figure 4, P-value = 1.12 x 10−5,Wilcoxon Rank Sum test). Six of the ten most420

common OTUs observed in the non-fractionatedDNA responded to xylose, and, seven of the tenmost abundant responders to xylose or cellulose in



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the non-fractionated DNA were xylose respondersalthough “OTU.6” annotated as Cellvibrio a cellu-425

lose responder at day 14 was the responder foundat highest relative abundance (approximately 3%or SSU rRNA genes at day 14, Figure S9).

DNA buoyant density (BD) increases in propor-tion to atom % 13C. Hence, the extent of 13C in-430

corporation into DNA can be evaluated by the dif-ference in BD between 13C-labeled and unlabeledDNA. We calculated for each OTU its mean BDweighted by relative abundance to determine its“center of mass” within a given density gradient.435

We then quantified for each OTU the difference incenter of mass between control gradients and gra-dients from 13C-xylose or 13C-cellulose treatments(see SI for the detailed calculation, Figure S11).We refer to the change in center of mass position440

for an OTU in response to 13C-labeling as ∆BD.

∆BD can be used to compare relative differences

in 13C-labeling between OTUs. ∆BD values, how-ever, are not comparable to the BD changes ob-served for DNA from pure cultures both because445

they are based on relative abundance in densitygradient fractions (and not DNA concentration)and because isolated strains grown in uniform con-ditions generate uniformly labeled molecules whileOTUs composed of heterogeneous strains in com-450

plex environmental samples do not. Cellulose re-

sponder ∆BD (0.0163 g mL−1 (s.d. 0.0094)) wasgreater than that of xylose responders (0.0097 gmL−1 (s.d. 0.0094)) (Figure 5, P-value = 1.8610 x10−6, Wilcoxon Rank Sum test).455

We predicted the rrn gene copy number for re-sponders as described [61]. The ability to prolifer-ate after rapid nutrient influx correlates positivelyto a microorganism’s rrn copy number [62]. Cellu-lose responders possessed fewer estimated rrn copy460

numbers (2.7 (1.2 s.d.)) than xylose responders(6.2 (3.4 s.d.)) ( P = 1.878 x 10−9, Wilcoxon RankSum test, Figure 5 and Figure S10). Furthermore,the estimated rrn gene copy number for xylose re-sponders was inversely related to the day of first465

response (P = 2.02 x 10−15, Wilcoxon Rank Sumtest, Figure S10,Figure 5).

We assessed phylogenetic clustering of 13C-responsive OTUs with the Nearest Taxon Index(NTI) and the Net Relatedness Index (NRI) [63].470

We also quantified the average clade depth of cellu-lose and xylose responders with the consenTRAITmetric [64]. Briefly, the NRI and NTI evaluatephylogenetic clustering against a null model forthe distribution of a trait in a phylogeny. The475

NRI and NTI values are z-scores or standard de-viations from the mean and thus the greater themagnitude of the NRI/NTI, the stronger the evi-dence for clustering (positive values) or overdisper-sion (negative values). NRI assesses overall clus-480

tering whereas the NTI assesses terminal clustering

[65]. The consenTRAIT metric is a measure of theaverage clade depth for a trait in a phylogenetictree. NRI values indicate that cellulose respondersclustered overall and at the tips of the phylogeny485

(NRI: 4.49, NTI: 1.43) while xylose respondersclustered terminally (NRI: -1.33, NTI: 2.69). TheconsenTRAIT clade depth for xylose and celluloseresponders was 0.012 and 0.028 SSU rRNA genesequence dissimilarity, respectively. As reference,490

the average clade depth is approximately 0.017SSU rRNA gene sequence dissimilarity for arabi-nase (another five C sugar found in hemicellulose)utilization as inferred from genomic analyses, andwas 0.013 and 0.034 SSU rRNA gene sequence dis-495

similarity for glucosidase and cellulase genomic po-tential, respectively [64, 66]. These results indi-cate xylose responders form terminal clusters dis-persed throughout the phylogeny while celluloseresponders form deep clades of terminally clustered500

OTUs.

DiscussionWe identified microorganisms participating in soilC cycling using a nucleic acid SIP approach.Specifically, we observed assimilation of 13C from505

either 13C-xylose or 13C-cellulose into DNA for 104OTUs in an agricultural soil. We found 13C from13C-xylose appeared to move into and then out ofgroups of related OTUs over time. By couplingnucleic acid SIP to high throughput sequencing510

we could diagnose OTU activity even when OTUswere at low relative abundance in non-fractionatedDNA (e.g. on three occasions we did not detect13C-responders in the non-fractionated DNA). Ourresults support the degradative succession hypoth-515

esis, elucidate ecophysiological properties of soilmicroorganisms, reveal activity of widespread un-cultured soil bacteria, and begin to piece togetherthe microbial food web in soils.

The degradative succession hypothesis predicts520

an ecological transition in activity during the de-composition of labile and structural plant organicmatter. Our results concur with the degradativesuccession hypothesis. Microorganisms consumedxylose-C before cellulose-c and assimilated xylose-525

C into DNA faster than to cellulose-C. Xylose is amajor constituent of hemicellulose and is a labilecomponent of fresh plant biomass. The phyloge-netic composition of xylose responders changed be-tween days 1, 3 and 7 and few OTUs appeared 13C-530

labeled in the 13C-xylose treatment after day 7.In the 13C-cellulose treatment, 13C-labeled OTUswere few in the in the beginning of the experimentbut most abundant day 14 and 30. Finally, few(8 of 104) OTUs appeared to metabolize both xy-535

lose and cellulose meaning over 30 days celluloseresponders grew in succession to xylose responders.



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Correlations between community compositionand environmental characteristics often indirectlyreveal microorganisms belonging to ecologically540

defined groups [67]. In this experiment, we di-rectly identified ecological groups as a functionof in situ metabolism and inferred the ecologicalproperties of these groups through temporal dy-namics of 13C-assimilation, the extent of OTU 13C-545

labeling, and phylogenetic affiliation. Xylose re-sponders grew faster than cellulose responders andappeared to assimilate C from multiple sources.Xylose responders assimilated xylose-C into DNA

within 24 hours and had low ∆BD relative to cellu-550

lose responders suggesting xylose was not the soleC source used for growth. Xylose represented 15%of the amendment and 3.5% of total soil C. Xy-lose responders often included the most abundantOTUs within the non-fractionated DNA and had555

high estimated rrn copy number relative to cellu-lose responders. However, to some degree, highrrn gene copy number may inflate observed xyloseresponder relative abundance. Notably, the ma-jority of xylose responder SSU rRNA genes (86%)560

matched SSU rRNA genes from cultured isolatesat high sequence identity (> 97%).

Cellulose responders, on the other hand, incor-porated 13C into DNA after xylose responders andappeared to specialize in using cellulose as a C565

source. Cellulose responders grew over a span of

weeks and had high ∆BD indicating cellulose re-mained their dominant C source even though mul-tiple C sources were present (cellulose represented6% of total C present in soil at the start of the570

experiment). Cellulose responders were also lowerin relative abundance on average within the non-fractionated DNA and had lower estimated rrncopy number than xylose responders. The major-ity of cellulose responders were not close relatives575

of cultured isolates although a number of celluloseresponders shared high SSU rRNA gene sequenceidentity with cultured Proteobacteria (e.g. Cellvib-rio), . We identified cellulose responders amongphyla such as Verrucomicrobia, Chloroflexi, and580

Planctomycetes – common soil phyla whose func-tions within soil communities remain unknown.

Verrucomicrobia represented 16% of the cellu-lose responders. Verrucomicrobia are cosmopoli-tan soil microbes [68] that can make up to 23%585

of SSU rRNA gene sequences in soils [68] and9.8% of soil SSU rRNA [69]. Genomic analysesand laboratory experiments show that various iso-lates within the Verrucomicrobia are capable ofmethanotrophy, diazotrophy, and cellulose degra-590

dation [70, 71]. Moreover, Verrucomicrobia havebeen hypothesized to degrade polysaccharides inmany environments [72–74]. However, only oneof the 15 most abundant verrucomicrobial phylo-types in globally distributed soil samples shared595

> 93% SSU rRNA gene sequence identity witha cultured isolate [68] and hence the role of soilVerrucomicrobia in global C-cycling remains un-known. The majority of verrucomicrobial celluloseresponders belonged to two clades that fall within600

the Spartobacteria (Figure 3). Spartobacteria out-numbered all other Verrucomicrobia phylotypes inSSU rRNA gene surveys of 181 globally distributedsoil samples [68]. Given their ubiquity and abun-dance in soil as well as their demonstrated incorpo-605

ration of 13C from 13C-cellulose, Verrucomicrobialineages, particularly Spartobacteria, may be im-portant contributors to cellulose decomposition ona global scale.

Other notable cellulose responders include610

OTUs in the Planctomycetes and Chloroflexi bothof which have previously been shown to assimilate13C from 13C-cellulose added to soil [75]. Plancto-mycetes are common in soil [40], comprising 4 to7% of bacterial cells in many soils [76, 77] and 7%615

± 5% of SSU rRNA [78]. Although soil Plancto-mycetes are widespread, their activities in soil re-main uncharacterized. Plantomycetes represented16% of cellulose responders and shared < 92% SSUrRNA gene sequence identity to their most closely620

related cultured isolates. Chloroflexi are knownfor metabolically dynamic lifestyles ranging fromanoxygenic phototrophy to organohalide respira-tion [79] and are among the six most abundantbacterial phyla in soil [40]. Recent studies have fo-625

cused on Chloroflexi roles in C cycling [79–81] andseveral Chloroflexi isolates use cellulose [79–81].Four of the five Chloroflexi cellulose responders be-long to a single clade within the Herpetosiphonales(Figure 3).630

Finally, a single cellulose responder belongedto the Melainabacteria phylum (95% shared SSUrRNA gene sequence identity with Vampirovib-rio chlorellavorus). The phylogenetic position ofMelainabacteria is debated but Melainabacteria635

have been proposed to be a non-phototrophic sisterphylum to Cyanobacteria. An analysis of a Melain-abacteria genome [82] suggests the genomic capac-ity to degrade polysaccharides though Vampirovib-rio chlorellavorus is an obligate predator of green640

alga [83].Responders did not necessarily assimilate 13C

directly from 13C-xylose or 13C-cellulose. Inmany ways, knowledge of secondary C degradationand/or microbial biomass turnover may be more645

interesting with respect to the soil C-cycle thanknowledge of primary degradation. The responseto xylose suggests xylose-C moved through differ-ent trophic levels within the soil bacterial foodweb. The Bacilli degraded xylose first (65% of the650

xylose-C had been respired by day 1) represent-ing 84% of day 1 xylose responders. Bacilli alsocomprised about 6% of SSU rRNA genes present



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in non-fractionated DNA on day 1. However,few Bacilli remained 13C-labeled by day 3 and655

their abundance declined reaching about 2% ofsoil SSU rRNA genes by day 30. Members of theBacillus [84] and Paenibacillus in particular [59]have been previously implicated as labile C decom-posers. The decline in relative abundance of Bacilli660

could be attributed to mortality and/or sporula-tion coupled to mother cell lysis. BacteroidetesOTUs appeared 13C-labeled at day 3 concomitantwith the decline in relative abundance and loss of13C-label for Bacilli. Finally, Actinobacteria ap-665

peared 13C-labeled at day 7 as Bacteroidetes xyloseresponders declined in relative abundance and be-came unlabeled. Hence, it seems reasonable to pro-pose that Bacteroidetes and Actinobacteria xyloseresponders became labeled via the consumption of67013C derived from 13C-labeled microbial biomass asopposed to primary degradation of 13C-xylose.

The inferred physiology of Actinobacteria andBacteroidetes xylose responders provides furtherevidence for C transfer by saprotrophy and/or pre-675

dation. Most of the Actinobacteria xylose respon-ders that appeared 13C-labeled at day 7 were mem-bers of the Micrococcales (Figure 3) and the mostabundant 13C-labeled Micrococcales OTU at day 7(OTU.4, Table S1) is annotated as belonging in680

the Agromyces. Agromyces are facultative preda-tors that feed on the gram-positive Luteobacter inculture [85]. Additionally, certain types of Bac-teroidetes can assimilate 13C from 13C-labeled Es-cherichia coli added to soil [86]. Alternatively, it is685

possible that Bacilli, Bacteroidetes, and Actinobac-teria are adapted to use xylose at different concen-trations and that the observed activity dynamicsresulted from changes in xylose concentration overtime and/or that Actinobacteria and Bacteroidetes690

xylose responders consumed waste products gener-ated by primary xylose metabolism (e.g. organicacids produced during xylose metabolism). Theselatter two hypotheses cannot explain the sequen-tial loss of 13C-label, however. If trophic transfer695

caused the activity dynamics, at least three differ-ent ecological groups exchanged C in 7 days. Mod-els of the soil C cycle often exclude trophic interac-tions between soil bacteria (e.g. [87]), yet when soilC models do account for predators and/or sapro-700

phytes, trophic interactions are predicted to havesignificant effects on the fate of soil C [88].

Implications for soil C cycling models. Functionalniche characterization for soil microorganisms isnecessary to predict whether and how biogeochem-705

ical processes vary with microbial community com-position. Functional niches are defined by soilmicrobiologists and have been successfully incor-porated into biogeochemical process models (E.g.[88, 89]). In some C models ecological strate-710

gies such as growth rate and substrate specificityare parameters for functional niche behavior [88].The phylogenetic breadth of a functionally definedgroup is often inferred from the distribution ofdiagnostic genes across genomes [66] or from the715

physiology of isolates cultured on laboratory media[64]. For instance, the wide distribution of the gly-colysis operon in microbial genomes is interpretedas evidence that many soil microorganisms partic-ipate in glucose turnover [9]. However, the func-720

tional niche may depend less on the distributionof diagnostic genes across genomes and more onlife history traits that allow organisms to competefor a given substrate as it occurs in the soil. Forinstance, fast growth and rapid resuscitation allow725

microorganisms to compete for labile C which mayoften be transient in soil. Hence, life history traitsmay constrain the diversity of microbes that me-tabolize a given C source in the soil under a givenset of conditions.730

Biogeochemical processes mediated by a broadarray of taxa are assumed to be insensitive to com-munity change whereas community change is as-sumed to affect processes mediated by a narrowsuite of microorganisms [9, 90]. In addition, the735

diversity of a functionally defined group engagedin a specific C transformation is expected to cor-relate positively with C lability [9]. However, thediversity of labile C and structural C decomposersin soil has not been quantified directly. We found740

comparable numbers of OTUs responded to 13C-cellulose and 13C-xylose (63 and 49, respectively).Cellulose responders were phylogenetically clus-tered suggesting that the ability to degrade cellu-lose is phylogenetically conserved. The clade depth745

of cellulose responders, 0.028 SSU rRNA gene se-quence dissimilarity, is on the same order as thatobserved for glycoside hydrolases which are diag-nostic enzymes for cellulose degradation [66]. Xy-lose responders clustered in terminal branches in-750

dicating groups of closely related taxa metabolizedxylose but xylose responders also clustered phylo-genetically with respect to time of response (Fig-ure 3, Figure 4). For example, xylose responderson day 1 are dominated by members of Paenibacil-755

lus. Thus, microorganisms that degraded labile Cand structural C were both limited in diversity. Al-though the genes for xylose metabolism are likelywidespread in the soil community, it’s possible onlya limited diversity of organisms had the ecologi-760

cal characteristics required to degrade xylose un-der experimental conditions. Therefore it’s pos-sible that only a limited number of taxa actuallyparticipate in the metabolism of labile C-sourcesunder a given set of conditions, and hence changes765

in community composition may alter the dynamicsof structural and labile C-transformations in soil.



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Broadly, we observed labile C use by fast growinggeneralists and structural C use by slow growingspecialists. These results agree with the MIMICS770

model which simulates leaf litter decomposition bymodeling microbial decomposers as two function-ally defined groups, copiotrophs or oligotrophs [89].Including these functional types improved predic-tions of C storage in response to environmental775

change relative to models that did not considerany microbial physiological diversity. We identi-fied microbial lineages engaged in labile and struc-tural C decomposition that can be defined as co-piotrophs or oligotrophs, respectively. We also ob-780

served rate differences in turnover of xylose respon-der biomass relative to cellulose responders whichmay be important to consider when modeling mi-crobial turnover input to SOM. It’s also clear thatthe characterization of microbes as copiotrophs785

and oligotrophs may miss other, vital functionaltypes mediating C-cycling in soil. That is, soil-Cmay travel through multiple bacterial trophic lev-els where each C transfer represents an opportunityfor C stabilization in association with soil miner-790

als or C loss by respiration. Our understanding ofsoil C dynamics will likely improve as we developa more granular understanding of the ecologicaldiversity of microorganisms that mediate C trans-formations in soil.795

Conclusion. Microorganisms governC-transformations in soil influencing climatechange on a global scale but we do not know theidentities of microorganisms that carry out specifictransformations. In this experiment microbes from800

physiologically uncharacterized but cosmopolitansoil lineages participated in cellulose decomposi-tion. Cellulose responders included members ofthe Verrucomicrobia (Spartobacteria), Chloroflexi,Bacteroidetes and Planctomycetes. Spartobacte-805

ria in particular are globally cosmopolitan soil mi-croorganisms and are often the most abundantVerrucomicrobia order in soil [68]. Fast-growingaerobic spore formers from Firmicutes assimilatedlabile C in the form of xylose. Xylose respon-810

ders within the Bacteroidetes and Actinobacterialikely became labeled by consuming 13C-labeledconstituents of microbial biomass either by sapro-trophy or predation. Our results suggest that cos-mopolitan Spartobacteria may degrade cellulose on815

a global scale, plant C may travel through a trophiccascade within the bacterial food web after pri-mary decomposition, and life history traits mayact as a filter constraining the diversity of activemicroorganisms relative to those with the genomic820

potential for a given metabolism.

MethodsAll code to take raw SSU rRNA gene sequencingreads to final publication figures and through allpresented analyses is located at the following URL:825

https://github.com/chuckpr/CSIP_succession_data_analysis.DNA sequences are deposited on MG-RAST (Ac-cession XXXXXXX).

Twelve soil cores (5 cm diameter x 10 cm depth)830

were collected from six sampling locations withinan organically managed agricultural field in PennYan, New York. Soils were sieved (2 mm), homog-enized, distributed into flasks (10 g in each 250ml flask, n = 36) and equilibrated for 2 weeks.835

We amended soils with a mixture containing 2.9mg C g−1 soil dry weight (d.w.) and broughtexperimental soil to 50% water holding capac-ity. By mass the amendment contained 38% cel-lulose, 23% lignin, 20% xylose, 3% arabinose, 1%840

galactose, 1% glucose, and 0.5% mannose. 10.6%amino acids (Teknova C9795) and 2.9% MurashigeSkoog basal salt mixture which contains macroand micro-nutrients that are associated with plantbiomass (Sigma Aldrich M5524). This mixture ap-845

proximates the molecular composition of switch-grass biomass with hemicellulose replaced by itsconstituent monomers [91]. We set up three paral-lel treatments varying the isotopically labeled com-ponent in each treatment. The treatments were (1)850

a control treatment with all unlabeled components,(2) a treatment with 13C-cellulose instead of unla-beled cellulose (synthesized as described in SI), and(3) a treatment with 13C-xylose (98 atom% 13C,Sigma Aldrich) instead of unlabeled xylose. Other855

details relating to substrate addition can be foundin SI. Microcosms were sampled destructively atdays 1 (control and xylose only), 3, 7, 14, and 30and soils were stored at -80◦C until nucleic acid ex-traction. The abbreviation 13CXPS refers to the86013C-xylose treatment (13C Xylose Plant Simulant),13CCPS refers to the 13C-cellulose treatment, and12CCPS refers to the control treatment.

We used DESeq2 (R package), an RNA-Seq dif-ferential expression statistical framework [92], to865

identify OTUs that were enriched in high den-sity gradient fractions from 13C-treatments rela-tive to corresponding gradient fractions from con-trol treatments (for review of RNA-Seq differentialexpression statistics applied to microbiome OTU870

count data see (30)). We define ”high density gra-dient fractions” as gradient fractions whose densityfalls between 1.7125 and 1.755 g ml−1. Briefly,DESeq2 includes several features that enable ro-bust estimates of standard error in addition to reli-875

able ranking of logarithmic fold change (LFC) (i.e.gamma-Poisson regression coefficients) in OTU rel-ative abundance even with low count OTUs whereLFC can often be noisy. Further, statistical evalu-



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ation of LFC can be performed with user-selected880

thresholds as opposed to the typical null hypoth-esis that LFC is exactly zero enabling the mostbiologically interesting OTUs to be identified forsubsequent analyses. For each OTU, we calcu-lated LFC and corresponding standard errors for885

enrichment in high density gradient fractions of13C treatments relative to control. Subsequently,a one-sided Wald test was used to statistically as-sess LFC values. The user-defined null hypothesiswas that LFC was less than one standard devia-890

tion above the mean of all LFC values. P-valueswere corrected for multiple comparisons using theBenjamini and Hochberg method [93]. We inde-pendently filtered OTUs on the basis of sparsityprior to correcting P-values for multiple compar-895

isons. The sparsity value that yielded the mostadjusted P-values less than 0.10 was selected forindependent filtering by sparsity. Briefly, OTUswere eliminated if they failed to appear in at least45% of high density gradient fractions for a given90013C/control treatment pair. These sparse OTUsare unlikely to have sufficient data to allow forthe determination of statistical significance. Weselected a false discovery rate of 10% to denotestatistical significance.905

See SI for additional information on experimen-tal and analytical methods.

Acknowledgements. The authors would like to ac-knowledge the assistance of John Christian Gabyand Mallory Choudoir in developing the method910

used to produce 13C-labeled cellulose. We wouldalso like to thank Steve Zinder, Nelson Hairston,and Nick Youngblut for providing comments thatwere helpful in the development of this manuscript.This material is based upon work supported by915

the Department of Energy Office of Science, Of-fice of Biological & Environmental Research Ge-nomic Science Program under Award NumbersDE-SC0004486 and DE-SC0010558.

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83. Gromov B-V, Mamkaeva K-A (1972) [Electron microscopicstudy of parasitism by Bdellovibrio chlorellavorus bacte-ria on cells of the green alga Chlorella vulgaris]. Tsitologiia1210

14(2): 256–260.84. Cleveland C-C, Nemergut D-R, Schmidt S-K, Townsend A-

R (2007) Increases in soil respiration following labile carbonadditions linked to rapid shifts in soil microbial communitycomposition. Biogeochemistry 82(3): 229–240.1215

85. Casida L-E (1983) Interaction of Agromyces ramosus withother bacteria in soil.. Appl Environ Microbiol 46(4): 881–888.

86. Lueders T, Kindler R, Miltner A, Friedrich M-W, KaestnerM (2006) Identification of bacterial micropredators distinc-1220

tively active in a soil microbial food web. Appl Environ Mi-crobiol 72(8): 5342–5348.

87. Moore J-C, Walter D-E, Hunt H-W (1988) Arthropod reg-ulation of micro- and mesobiota in below-ground detritalfood webs. Annu Rev Entomol 33(1): 419–435.1225

88. Kaiser C, Franklin O, Dieckmann U, Richter A (2014) Mi-crobial community dynamics alleviate stoichiometric con-straints during litter decay. Ecol Lett 17(6): 680–690.

89. Wieder W-R, Grandy A-S, Kallenbach C-M, Bonan G-B (2014) Integrating microbial physiology and physio-1230

chemical principles in soils with the MIcrobial-MIneral Car-bon Stabilization (MIMICS) model. Biogeosciences 11(14):3899–3917.

90. Schimel J (1995) Ecosystem consequences of microbial di-versity and community structure. Arctic and alpine bio-1235

diversity: patterns, causes and ecosystem consequences,, eds. Chapin III F-S, Korner C, Ecological Studies(Springer, Berlin Heidelberg), pp 239–254.

91. Schneckenberger K, Demin D, Stahr K, Kuzyakov Y (2008)

Microbial utilization and mineralization of 14C glucose1240

added in six orders of concentration to soil. Soil BiolBiochem 40(8): 1981–1988.

92. Love M-I, Huber W, Anders S (2014) Moderated estima-tion of fold change and dispersion for RNA-seq data withDESeq2. Genome Biol 15(12): 550.1245

93. Benjamini Y, Hochberg Y (1995) Controlling the false dis-covery rate: A practical and powerful approach to multi-ple testing. Journal of the Royal Statistical Society. Series B(Methodological) 57(1): 289–300.



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0.0

0.2

−0.50 −0.25 0.00 0.25NMDS1

NM

DS

2

Density, g/mL●

●

●

●●

1.681.701.721.741.76

Treatment12CCPS13CCPS13CXPS

A

−0.2

0.0

0.2

−0.50 −0.25 0.00 0.25NMDS1

NM

DS

2

Day●

●

●

●

●

1371430

B

Fig. 1. NMDS analysis of SIP gradient fraction SSU rRNA gene sequence composition reveals differences in the sequence composition of gradient fractions is

correlated to fraction density, isotopic labeling, and time. SSU rRNA gene compositon was profiled for fractions for each density gradient. 13C-labeling of DNA

is apparent because the SSU rRNA gene composition of gradient fractions from 13C and control treatments differ at high density. Each point on the NMDS plot

represents one gradient fraction. SSU rRNA gene 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).

6

4

2

0

2

4

613CXPS, Day 1

6

4

2

0

2

4

613CCPS, Day 3

13CXPS, Day 3

13CCPS, Day 7

13CXPS, Day 7

13CCPS, Day 14

13CXPS, Day 14

13CCPS, Day 30

13CXPS, Day 30

Log 2

Fol

d C

hang

e

Phylum

PhylumActinobacteriaVerrucomicrobiaPlanctomycetesProteobacteriaFirmicutesCyanobacteriaChloroflexiBacteroidetesChlorobiArmatimonadetesChlamydiaeAcidobacteriaCandidate_division_WS3GemmatimonadetesNitrospiraeJLETNPZ39Candidate_division_BRC1unassigned

Fig. 2. OTU enrichment in 13C-treatment heavy density fractions relative to control expressed as LFC (see Methods) for the 13C-cellulose treatment (top)

and 13C-xylose treatment (bottom). High LFC indicate the OTU incorporated 13C into DNA (each point represents an OTU LFC for the given treatment relative

to control at the day indicated). 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 LFC plus or minus one standard deviation (dark shading) or two

standard devations (light shading) about the mean of all LFC values.



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Fig. 3. Phylogenetic relationships of OTUs passing independent filtering when quantifying OTU enrichment in heavy gradient fractions relative to control

(see Methods). Only those phyla that contain responders are shown. Colored dots are used to identify xylose responders (green) and cellulse responders (blue).

The heatmaps indicate enrichment in high denstiy fractions relative to control (represented as LFC) for each OTU in response to both 13C-cellulose (“13CCPS”,

leftmost heatmap) and 13C-xylose (“13CXPS”, rightmost heatmap) with values for different days in each heatmap column. Greater enrichment (represented as

LFC) in heavy density fractions provide evidence of 13C-labeled DNA.



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Actinobacteria

Bacteroidetes

Firmicutes

Proteobacteria

02468

0.02.55.07.5

10.012.5

0

5

10

15

0

2

4

6

1 3 7 14 30Day

Num

ber

of O

TU

s●

●

●●

●

●

●

●●●

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●

Actinobacteria

Bacteroidetes

Firmicutes

Proteobacteria

0369

12

0

5

10

15

0

10

20

30

0

10

20

30

1 3 7 14 30Day

Fol

d ch

ange

Fig. 4. Xylose reponders in the Actinobacteria, Bacteroidetes, Firmicutes exhibit distinct temporal dynamics of 13C-labeling. The left column

shows counts of 13C-xylose responders in the Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria at days 1, 3, 7 and 30. The right panel

shows enrichment in high density gradient fractions (expressed as fold change, not logarithmic) for responders (large points) as well as a boxplot for the distribution

of fold change values (small dots are outliers, i.e. beyond 1.5 times the interquartile range (IR). Whiskers extend to 1.5 times the IR, and the box extends one IR

about the median (solid line)). Each day in the right column shows all responders (i.e. OTUs that responded to xylose at any point in time). Greater enrichment

in high density fractions of the 13C-xylose treatment relative to control indicates DNA is 13C-labeled.

5

10

1 3 7 14 30Day

rrn

gene

cop

y nu

mbe

r

Treatment

13CCPS

13CXPS

0

10

20

30

40

50

−0.02 0.00 0.02

∆BD

Ker

nel D

ensi

ty

0.001

0.100

10 1000Day 1 Rank

Rel

ativ

e A

bund

ance Day

1

3

7

14

30

Fig. 5. Characteristics of xylose responders (green) and cellulose responders (blue) based on estimated rrn copy number (A), ∆BD (B), and relative

abundance in non-fractionated DNA (C). The estimated rrn copy number of all responders is shown versus time (A). Kernel density histogram of ∆BD values

shows cellulose responders had generally higher ∆BD than xylose responders indicating potentially greater 13C incorporation per unit 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) but relative abundance

for each OTU is indicated for all days by colored lines (see legend). Xylose responders (green ticks) have higher relative abundance than xylose responders (ticks

are based on day 1 relative abundance).



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Supplemental Figures and Tables1250



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Fig. S1. An organic matter enrichment including C components and nutrients commonly found in plant biomass was added to soil microcosms. At days 1, 3,

7, 14, and 30 replicate microcosms were destructively harvested. Bulk DNA from each treatment and time point (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 were

PCR amplified and sequenced from gradient fractions and from non-fractionated DNA (representing the bulk soil microbial community).

40

60

80

100

0 10 20 30Day

Car

bon

rem

aini

ng (

%)

Substratexylosecellulose

Fig. S2. Percentage of added 13C remaining in soil over time.



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●●

●

−0.3

−0.2

−0.1

0.0

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0.2

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NM

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2

Day●

●

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1371430

Treatment● 12CCPS

13CCPS13CXPS

●●

●

−0.05

0.00

0.05

−0.1 0.0 0.1NMDS1

NM

DS

2

Fig. S3. NMDS analysis of SSU rRNA gene composition differences between non-fractionated DNA alone (right panel) and in the context of SIP gradient

fractions (left panel). Non-fractionated DNA SSU rRNA gene composition changed with time but not with treatment (right panel) and variance of non-fractionated

DNA SSU rRNA gene composition was less than variance introduced by density fractionation (left panel). Distance in SSU rRNA gene composition was quantified

with the weighted UniFrac metric.

DA023

Micrococcales

Flavobacteria

Sphingobacteriia

Herpetosiphonales

KD4−96

SM1D11

Bacilli

Planctomycetacia

Alphaproteobacteria

Betaproteobacteria

Gammaproteobacteria

OPB35_soil_group

Acidobacteria

Actinobacteria

Bacteroidetes

Chloroflexi

Cyanobacteria

Firmicutes

Planctomycetes

Proteobacteria

Verrucomicrobia

−5.0 −2.5 0.0 2.5Log2 fold change in non−fractionated DNA with time

Response Both Cellulose None Xylose

Fig. S4. Change in non-fractionated DNA relative abundance versus time (expressed as LFC) for OTUs that changed significantly (P-value < 0.10, Wald

test). Each panel shows one phylum (labeled on the right). The taxonomic class is indicated on the left. Colors represent whether OTUs responded to just xylose

(green), just cellulose (blue), or both xylose and cellulose (red).



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Gammaproteobacteria Herpetosiphonales

0.025

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0.125

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1 3 7 14 30 1 3 7 14 30Day

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13CXPS

Fig. S5. Relative abundance in non-fractionated DNA versus time for classes that changed significantly.



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0.00

0.02

0.04

0.06

1 3 7 14 30Day

Rel

ativ

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bund

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OTU.1040OTU.1069OTU.2040OTU.267OTU.3OTU.319OTU.335OTU.3507

OTU.369OTU.394OTU.4743OTU.5603OTU.57OTU.8OTU.843OTU.9

13CXPS, Firmicutes

0.00

0.01

0.02

0.03

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1 3 7 14 30Day

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ativ

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OTUOTU.107OTU.131OTU.14OTU.159OTU.183OTU.2379

OTU.277OTU.3540OTU.360OTU.5906OTU.6203OTU.760

13CXPS, Bacteroidetes

0.000

0.005

0.010

0.015

0.020

1 3 7 14 30Day

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ativ

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OTUOTU.24OTU.244OTU.252OTU.37

OTU.4OTU.4446OTU.5284OTU.62

13CXPS, Actinobacteria

0.000

0.003

0.006

0.009

0.012

1 3 7 14 30Day

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ativ

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OTUOTU.22OTU.290OTU.346

OTU.48OTU.68

13CXPS, Proteobacteria

0.000

0.001

0.002

0.003

0.004

0.005

1 3 7 14 30Day

Rel

ativ

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bund

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OTUOTU.1023OTU.1533OTU.185OTU.2192OTU.266


13CCPS, Verrucomicrobia

0.000

0.002

0.004

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ativ

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OTU.971OTU.98

13CCPS, Chloroflexi

0.000

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0.002

0.003

0.004

1 3 7 14 30Day

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OTUOTU.1065OTU.1204OTU.204OTU.285OTU.351


13CCPS, Planctomycetes

0e+00

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4e−04

6e−04

8e−04

1 3 7 14 30Day

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OTU.669OTU.670

13CCPS, Bacteroidetes

0.0000

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0.0050

0.0075

0.0100

0.0125

1 3 7 14 30Day

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ativ

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bund

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OTUOTU.100OTU.1087OTU.114OTU.119OTU.1312OTU.154OTU.1754OTU.228OTU.28OTU.327OTU.3594OTU.3775

OTU.429OTU.442OTU.518OTU.5539OTU.6062OTU.633OTU.766OTU.899OTU.90OTU.900OTU.982

13CCPS, Proteobacteria

0.000

0.025

0.050

0.075

1 3 7 14 30Day

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ativ

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bund

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OTUOTU.11OTU.150OTU.165OTU.19


Both, Proteo., Verruco., Plancto.

Fig. S6. Change in relative abundance in non-fractionated DNA over time for xylose responders (13CXPS) and cellulose responders (13CCPS). Each panel

represents a phylum except for the lower right panel which shows all reponders to both xylose and celluose.



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Actinobacteria Bacteroidetes Chloroflexi

Cyanobacteria Firmicutes Planctomycetes

Proteobacteria Verrucomicrobia

−2

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−2 0 2 4 6 −2 0 2 4 6Log2 Fold Change 13CCPS

Log 2

Fol

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hang

e 13

CX

PS

ResponseBothCelluloseXylose

Fig. S7. Maximum enrichment at any point in time in heavy fractions of 13C-treatments relative to control (expressed as LFC) shown for 13C-cellulose

versus 13C-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 repspond to either substrate. Line indicates a slope of one.

0

10

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30

40

1 3 7 14 30Day

Num

ber

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TU

s

Treatment

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13CXPS

Fig. S8. Counts of xylose responders and cellulose responders over time.



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0.00

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1.66 1.68 1.70 1.72 1.74 1.76Density, g/mL

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OTU.57 Paenibacillus

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OTU.573 Cytophagia

Fig. S9. Raw data from example responders highlighted in the main text (see Results). The left column shows DNA-SIP density fraction relative abundances

for 13C-xylose or 13C-cellulose gradients in addition to control gradients for each of the chosen OTUs. Time is indicated by the color of the relative abundance

profile (see legend). Gradient profiles are shaded by treatment where orange represents “control” profles, blue “13C-cellulose”, and green “13C-xylose.” The

right column shows the relative abundance of each OTU in non-fractionated DNA (i.e. the DNA that was subsequently fractionated on the density gradient).

Enrichment in the heavy end of the gradient in 13C-treatments indicates an OTU has 13C-labeled DNA.



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Cellulose v. Xylose Day v. Xylose

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13CCPS 13CXPS Day 1 Day 3 Day 7 Day 14

Est

imat

ed r

RN

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pero

nco

py n

umbe

r pe

r ge

nom

e

PhylumActinobacteriaBacteroidetesChloroflexiCyanobacteriaFirmicutesPlanctomycetesProteobacteriaVerrucomicrobia

Fig. S10. Estimated rrn copy number for xylose and cellulose responders. The leftmost panel contrasts estimated rrn copy number for cellulose (13CCPS)

and xylose (13CXPS) responders. The right panel shows estimated rrn copy number versus time of first response for xylose responders. Colors denote the phylum

of the OTUs (see legend).

●

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Heavyfractions

Fig. S11. Density profile for a single cellulose responder in the 13C-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 ∆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 and the dots indicate outlier

values beyond 1.5 times the IR).



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12CCPS

13CCPS

Treatment

Heavyfractions

Fig. S12. Density profile for a single non-responder OTU. The 13C-cellulose treatment is in blue and the control treatment is in orange. The vertical line

shows where ”heavy” fractions begin as defined in our analysis. The right panel shows relative abundance values in the heavy 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 and the dots indicate outlier

values beyond 1.5 times the IR).



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Table S1: 13C-xylose responders BLAST against Living Tree Project

OTU ID Fold change a Day b All days c Top BLAST hits BLAST %ID Phylum;Class;Order

OTU.1040 4.78 1 1 Paenibacillus daejeonensis 100.0 Firmicutes Bacilli Bacillales

OTU.1069 3.85 1 1 Paenibacillus terrigena 100.0 Firmicutes Bacilli Bacillales

OTU.107 2.25 3 3 Flavobacterium sp. 15C3 ,Flavobacterium banpakuense

99.54 Bacteroidetes FlavobacteriaFlavobacteriales

OTU.11 5.25 7 7 Stenotrophomonas pavanii ,Stenotrophomonas maltophilia,Pseudomonas geniculata

99.54 ProteobacteriaGammaproteobacteriaXanthomonadales

OTU.131 3.07 3 3 Flavobacterium fluvii ,Flavobacteria bacterium HMD1033 ,Flavobacterium sp. HMD1001


OTU.14 3.92 3 1, 3 Flavobacterium oncorhynchi ,Flavobacterium glycines,Flavobacterium succinicans


OTU.150 3.08 14 14 No hits of at least 90%identity

86.76 Planctomycetes PlanctomycetaciaPlanctomycetales

OTU.159 3.16 3 3 Flavobacterium hibernum 98.17 Bacteroidetes FlavobacteriaFlavobacteriales

OTU.165 2.38 3 3 Rhizobium skierniewicense,Rhizobium vignae,Rhizobium larrymoorei ,Rhizobium alkalisoli ,Rhizobium galegae,Rhizobium huautlense

100.0 Proteobacteria AlphaproteobacteriaRhizobiales


89.5 Bacteroidetes SphingobacteriiaSphingobacteriales

OTU.19 2.14 7 7 Rhizobium alamii ,Rhizobium mesosinicum,Rhizobium mongolense,Arthrobacter viscosus,Rhizobium sullae,Rhizobium yanglingense,Rhizobium loessense


OTU.2040 2.91 1 1 Paenibacillus pectinilyticus 100.0 Firmicutes Bacilli Bacillales

OTU.22 2.8 7 7, 14 Paracoccus sp. NB88 99.09 Proteobacteria AlphaproteobacteriaRhodobacterales

OTU.2379 3.1 3 3 Flavobacterium pectinovorum,Flavobacterium sp. CS100


OTU.24 2.81 7 7 Cellulomonas aerilata,Cellulomonas humilata,Cellulomonas terrae,Cellulomonas soli ,Cellulomonas xylanilytica

100.0 Actinobacteria MicrococcalesCellulomonadaceae

OTU.241 3.38 3 3, 14 No hits of at least 90%identity

87.73 Verrucomicrobia SpartobacteriaChthoniobacterales

OTU.244 3.08 7 7 Cellulosimicrobium funkei ,Cellulosimicrobium terreum

100.0 Actinobacteria MicrococcalesPromicromonosporaceae

OTU.252 3.34 7 7 Promicromonospora thailandica 100.0 Actinobacteria MicrococcalesPromicromonosporaceae

OTU.267 4.97 1 1 Paenibacillus pabuli ,Paenibacillus tundrae,Paenibacillus taichungensis,Paenibacillus xylanexedens,Paenibacillus xylanilyticus

100.0 Firmicutes Bacilli Bacillales

OTU.277 3.52 3 3 Solibius ginsengiterrae 95.43 Bacteroidetes SphingobacteriiaSphingobacteriales



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Table S1 – continued from previous page

OTU ID Fold change Day All days Top BLAST hits BLAST %ID Phylum;Class;Order

OTU.290 3.59 1 1 Pantoea spp.,Kluyvera spp.,Klebsiella spp.,Erwinia spp.,Enterobacter spp.,Buttiauxella spp.

100.0 ProteobacteriaGammaproteobacteriaEnterobacteriales

OTU.3 2.61 1 1 [Brevibacterium] frigoritolerans,Bacillus sp. LMG 20238 ,Bacillus coahuilensis m4-4 ,Bacillus simplex


OTU.319 3.98 1 1 Paenibacillus xinjiangensis 97.25 Firmicutes Bacilli Bacillales

OTU.32 3.0 3 3, 7, 14 Sandaracinus amylolyticus 94.98 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.335 2.53 1 1 Paenibacillus thailandensis 98.17 Firmicutes Bacilli Bacillales

OTU.346 3.44 3 3 Pseudoduganella violaceinigra 99.54 Proteobacteria BetaproteobacteriaBurkholderiales

OTU.3507 2.36 1 1 Bacillus spp. 98.63 Firmicutes Bacilli Bacillales

OTU.3540 2.52 3 3 Flavobacterium terrigena 99.54 Bacteroidetes FlavobacteriaFlavobacteriales

OTU.360 2.98 3 3 Flavisolibacter ginsengisoli 95.0 Bacteroidetes SphingobacteriiaSphingobacteriales

OTU.369 5.05 1 1 Paenibacillus sp. D75 ,Paenibacillus glycanilyticus


OTU.37 2.68 7 7 Phycicola gilvus,Microterricola viridarii ,Frigoribacterium faeni ,Frondihabitans sp. RS-15 ,Frondihabitans australicus

100.0 Actinobacteria MicrococcalesMicrobacteriaceae

OTU.394 4.06 1 1 Paenibacillus pocheonensis 100.0 Firmicutes Bacilli Bacillales

OTU.4 2.84 7 7, 14 Agromyces ramosus 100.0 Actinobacteria MicrococcalesMicrobacteriaceae

OTU.4446 3.49 7 7 Catenuloplanes niger ,Catenuloplanes castaneus,Catenuloplanes atrovinosus,Catenuloplanes crispus,Catenuloplanes nepalensis,Catenuloplanes japonicus

97.72 Actinobacteria FrankialesNakamurellaceae

OTU.4743 2.24 1 1 Lysinibacillus fusiformis,Lysinibacillus sphaericus


OTU.48 2.99 1 1, 3 Aeromonas spp. 100.0 ProteobacteriaGammaproteobacteria aaa34a10

OTU.5 3.69 7 7 Delftia tsuruhatensis,Delftia lacustris

100.0 Proteobacteria BetaproteobacteriaBurkholderiales

OTU.5284 3.56 7 7 Isoptericola nanjingensis,Isoptericola hypogeus,Isoptericola variabilis

98.63 Actinobacteria MicrococcalesPromicromonosporaceae

OTU.5603 3.96 1 1 Paenibacillus uliginis 100.0 Firmicutes Bacilli Bacillales

OTU.57 4.39 1 1, 3, 7, 14,30

Paenibacillus castaneae 98.62 Firmicutes Bacilli Bacillales

OTU.5906 3.16 3 3 Terrimonas sp. M-8 96.8 Bacteroidetes SphingobacteriiaSphingobacteriales

OTU.6 3.24 3 3 Cellvibrio fulvus 100.0 ProteobacteriaGammaproteobacteriaPseudomonadales



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OTU.62 2.57 7 7 Nakamurella flavida 100.0 Actinobacteria FrankialesNakamurellaceae

OTU.6203 3.32 3 3 Flavobacterium granuli ,Flavobacterium glaciei


OTU.68 3.74 7 7 Shigella flexneri ,Escherichia fergusonii ,Escherichia coli ,Shigella sonnei

100.0 ProteobacteriaGammaproteobacteriaEnterobacteriales

OTU.760 2.89 3 3 Dyadobacter hamtensis 98.63 Bacteroidetes CytophagiaCytophagales

OTU.8 2.26 1 1 Bacillus niacini 100.0 Firmicutes Bacilli Bacillales

OTU.843 3.62 1 1 Paenibacillus agarexedens 100.0 Firmicutes Bacilli Bacillales

OTU.9 2.04 1 1 Bacillus megaterium,Bacillus flexus


a Maximum observed log2 of fold change.b Day of maximum fold change.c All response days.



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Table S2: 13C-cellulose responders BLAST against Living Tree Project

OTU ID Fold change a Day b All days c Top BLAST hits BLAST %ID Phylum;Class;Order

OTU.100 2.66 14 14 Pseudoxanthomonas sacheonensis,Pseudoxanthomonas dokdonensis






OTU.1087 4.32 14 14, 30 Devosia soli ,Devosia crocina,Devosia riboflavina


OTU.1094 3.69 30 30 Sporocytophaga myxococcoides 99.55 Bacteroidetes CytophagiaCytophagales

OTU.11 3.41 14 14 Stenotrophomonas pavanii ,Stenotrophomonas maltophilia,Pseudomonas geniculata


OTU.114 2.78 14 14 Herbaspirillum sp. SUEMI03 ,Herbaspirillum sp. SUEMI10 ,Oxalicibacterium solurbis,Herminiimonas fonticola,Oxalicibacterium horti


OTU.119 3.31 14 14, 30 Brevundimonas alba 100.0 Proteobacteria AlphaproteobacteriaCaulobacterales

OTU.120 4.76 14 14, 30 Vampirovibrio chlorellavorus 94.52 Cyanobacteria SM1D11uncultured-bacterium

OTU.1204 4.32 30 30 Planctomyces limnophilus 91.78 Planctomycetes PlanctomycetaciaPlanctomycetales

OTU.1312 4.07 30 30 Paucimonas lemoignei 99.54 Proteobacteria BetaproteobacteriaBurkholderiales

OTU.132 2.81 14 14 Streptomyces spp. 100.0 Actinobacteria StreptomycetalesStreptomycetaceae





OTU.154 3.24 14 14 Pseudoxanthomonas mexicana,Pseudoxanthomonas japonensis


OTU.165 3.1 14 14 Rhizobium skierniewicense,Rhizobium vignae,Rhizobium larrymoorei ,Rhizobium alkalisoli ,Rhizobium galegae,Rhizobium huautlense


OTU.1754 4.48 14 14 Asticcacaulis biprosthecium,Asticcacaulis benevestitus

96.8 Proteobacteria AlphaproteobacteriaCaulobacterales



OTU.19 2.44 14 14 Rhizobium alamii ,Rhizobium mesosinicum,Rhizobium mongolense,Arthrobacter viscosus,Rhizobium sullae,Rhizobium yanglingense,Rhizobium loessense




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OTU.228 2.54 30 30 Sorangium cellulosum 98.17 Proteobacteria DeltaproteobacteriaMyxococcales



OTU.257 2.94 14 14 Lentzea waywayandensis,Lentzea flaviverrucosa

100.0 Actinobacteria PseudonocardialesPseudonocardiaceae



OTU.28 2.59 14 14 Rhizobium giardinii ,Rhizobium tubonense,Rhizobium tibeticum,Rhizobium mesoamericanum CCGE 501 ,Rhizobium herbae,Rhizobium endophyticum


OTU.285 3.55 30 14, 30 Blastopirellula marina 90.87 Planctomycetes PlanctomycetaciaPlanctomycetales

OTU.32 2.34 3 3 Sandaracinus amylolyticus 94.98 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.327 2.99 14 14 Asticcacaulis biprosthecium,Asticcacaulis benevestitus


OTU.351 3.54 14 14, 30 Pirellula staleyi DSM 6068 91.86 Planctomycetes PlanctomycetaciaPlanctomycetales

OTU.3594 3.83 30 30 Chondromyces robustus 90.41 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.3775 3.88 14 14 Devosia glacialis,Devosia chinhatensis,Devosia geojensis,Devosia yakushimensis


OTU.429 3.7 30 14, 30 Devosia limi ,Devosia psychrophila


OTU.4322 4.19 14 7, 14, 30 No hits of at least 90%identity

89.14 Chloroflexi HerpetosiphonalesHerpetosiphonaceae

OTU.442 3.05 30 30 Chondromyces robustus 92.24 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.465 3.79 30 30 Ohtaekwangia kribbensis 92.73 Bacteroidetes CytophagiaCytophagales

OTU.473 3.58 14 14 Pirellula staleyi DSM 6068 90.91 Planctomycetes PlanctomycetaciaPlanctomycetales



OTU.5 2.69 14 14 Delftia tsuruhatensis,Delftia lacustris


OTU.518 4.8 14 14 Hydrogenophaga intermedia 100.0 Proteobacteria BetaproteobacteriaBurkholderiales





OTU.5539 4.01 14 14 Devosia subaequoris 98.17 Proteobacteria AlphaproteobacteriaRhizobiales

OTU.573 3.03 30 30 Adhaeribacter aerophilus 92.76 Bacteroidetes CytophagiaCytophagales



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OTU.6 3.62 7 3, 7, 14 Cellvibrio fulvus 100.0 ProteobacteriaGammaproteobacteriaPseudomonadales



OTU.6062 4.83 30 30 Dokdonella sp. DC-3 ,Luteibacter rhizovicinus


OTU.627 4.43 14 14 Verrucomicrobiaceae bacterium DC2a-G7100.0 Verrucomicrobia VerrucomicrobiaeVerrucomicrobiales


89.5 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.638 4.0 30 30 Luteolibacter sp. CCTCC AB 2010415 ,Luteolibacter algae

93.61 Verrucomicrobia VerrucomicrobiaeVerrucomicrobiales



OTU.663 3.63 30 30 Pirellula staleyi DSM 6068 90.87 Planctomycetes PlanctomycetaciaPlanctomycetales

OTU.669 3.34 30 30 Ohtaekwangia koreensis 92.69 Bacteroidetes CytophagiaCytophagales

OTU.670 2.87 30 30 Adhaeribacter aerophilus 91.78 Bacteroidetes CytophagiaCytophagales

OTU.766 3.21 14 14, 30 Devosia insulae 99.54 Proteobacteria AlphaproteobacteriaRhizobiales

OTU.83 5.61 14 7, 14, 30 Luteolibacter sp. CCTCC AB 2010415 97.72 Verrucomicrobia VerrucomicrobiaeVerrucomicrobiales

OTU.862 5.87 14 14 Allokutzneria albata 100.0 Actinobacteria PseudonocardialesPseudonocardiaceae

OTU.899 2.28 30 30 Enhygromyxa salina 97.72 Proteobacteria DeltaproteobacteriaMyxococcales

OTU.90 2.94 14 14, 30 Sphingopyxis panaciterrae,Sphingopyxis chilensis,Sphingopyxis sp. BZ30 ,Sphingomonas sp.

100.0 Proteobacteria AlphaproteobacteriaSphingomonadales

OTU.900 4.87 14 14 Brevundimonas vesicularis,Brevundimonas nasdae



78.57 Chloroflexi AnaerolineaeAnaerolineales



OTU.982 4.47 14 14 Devosia neptuniae 100.0 Proteobacteria AlphaproteobacteriaRhizobiales

a Maximum observed log2 of fold change.b Day of maximum fold change.c All response days.



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