Abstract
We explored the dynamics of microbial contributions to decomposition in soil by coupling DNA Stable Isotope Probing (SIP) and high through-put DNA sequencing. Our experiment evaluated the degradative succession hypothesis, described dynamics of carbon (C) metabolism during organic matter degradation, and characterized bacteria that metabolize labile and structural C in soils. We added a complex amendment representing plant derived organic matter to soil substituting 13C-xylose or 13C-cellulose for unlabeled equivalents in two experimental treatments which were monitored for 30 days. Xylose and cellulose are abundant components in plant biomass and represent labile and structural C pools, respectively. We characterized 5,940 SSU rRNA gene operational taxonomic units (OTUs) finding evidence for 13C-incorporation into DNA from 13C-xylose and 13C-cellulose in 49 and 63 OTUs, respectively. In the 13C-xylose treatment the types of microorganisms that incorporated 13C into DNA changed over time dominated by Firmicutes at day 1 followed by Bacteroidetes at day 3 and then Actinobacteria at day 7. These dynamics of 13C-labeling suggest labile C traveled through different trophic levels within the soil bacterial community. The microorganisms that metabolized cellulose-C increased in relative abundance over the course of the experiment with the highest number of OTUs exhibiting evidence for 13C-assimilation after 14 days. Microbes that metabolized cellulose-C belonged to cosmopolitan soil lineages that remain uncharacterized including Spartobacteria, Chloroflexi and Planctomycetes. Using an approach that reveals the C assimilation dynamics of specific microbial lineages we describe the ecological properties of functionally defined microbial groups that contribute to decomposition in soil.
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.
Introduction
Soils worldwide contain 2,300 Pg of carbon (C) which accounts for nearly 80% of the C present in the terrestrial biosphere [1, 2]. C respiration by soil microorganisms produces annually tenfold more CO2 than fossil fuel emissions [3]. Despite the contribution of microorganisms to global C flux, many global C models ignore the diversity of microbial physiology [4–6] and we still know little about the ecophysiology of soil microorganisms. Such knowledge should assist the development and refinement of global C models [7–10].
Most plant C is comprised of cellulose (30-50%) followed by hemicellulose (20-40%), and lignin (15-25%) [11]. Hemicellulose, being the most soluble, degrades in the early stages of decomposition. Xylans are often an abundant component of hemicellulose, and xylans include differing amounts of xy-lose, glucose, arabinose, galactose, mannose, and rhamnose [12]. Xylose is often the most abundant sugar in hemicellulose, comprising as much as 60-90% of xylan in some plants (e.g hardwoods [13], wheat [14], and switchgrass [15]). Microbes that respire labile C in the form of sugars proliferate during the initial stages of decomposition [16, 17], and metabolize as much as 75% of sugar C during the first 5 days [18]. In contrast, cellulose decomposition proceeds more slowly with rates increasing for approximately 15 days while degradation continues for 30-90 days [18, 19]. It is hypothesized that different microbial guilds mediate the decomposition of different plant biomass components [19–22]. The degradative succession hypothesis posits that fast growing organisms proliferate in response to the labile fraction of plant biomass such as sugars [23, 24] followed by slow growing organisms targeting structural C such as cellulose [23]. Evidence to support the degradative succession hypothesis comes from observing soil respiration dynamics and characterizing microbes cultured at different stages of decomposition. The degree to which the succession hypothesis presents an accurate model of litter decomposition has been questioned [21, 25, 26] and it’s clear that we need new approaches to dissect microbial contributions to C transformations in soils.
Though microorganisms mediate 80-90% of the soil C-cycle [27, 28], and microbial community composition can account for significant variation in C mineralization [29], terrestrial C-cycle models rarely consider the community composition of soils [30, 31]. Rates of soil C transformations are measured without knowledge of the organisms that mediate these reactions [28] leaving the importance of community membership towards maintaining ecosystem function undefined [28, 32, 33]. Variation in microbial community composition can be linked effectively to rates of soil processes when diagnostic genes for specific functions are available (e.g. denitrification [34], nitrification [35–37], methanotrophy [38], and nitrogen fixation [39]). However, the lack of diagnostic genes for describing soil-C transformations has limited progress in characterizing the contributions of individual microbes 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 Acidobacteria, Chloroflexi, Planctomycetes, and Verrucomicrobia. These phyla combined can comprise 32% of soil microbial communities (based on surveys of the SSU rRNA genes in soil) [40, 41].
Characterizing the functions of microbial taxa has relied historically on culturing microorganisms and subsequently characterizing their physiology in the laboratory, and on environmental surveys of genes diagnostic for specific processes. But, most microorganisms are difficult to grow in culture [40] and many biogeochemical processes lack suitable diagnostic genes. Nucleic acid stable-isotope probing (SIP) links genetic identity and activity without the need to grow microorganisms in culture and has expanded our knowledge of microbial contributions to biogeochemical processes [42]. However, nucleic acid SIP has notable complications including the need to add large amounts of labeled substrate [43], label dilution resulting in partial labeling of nucleic acids [43–45], the potential for 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 geted specialized microorganisms such as methanotrophs [43], methanogens [55], syntrophs [56], or microbes that target pollutants [57]. 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 [58].
Coupling SIP with high throughput DNA sequencing now enables exploration of microbial C-cycling in soils. SSU rRNA amplicons are readily sequenced from numerous density gradient fractions across multiple samples thereby increasing the resolution of a typical nucleic acid SIP experiment [59]. It is now possible to use far less isotopically labeled substrate resulting in more environmentally realistic experimental conditions [58]. We have employed such a high resolution DNA stable isotope probing approach to explore the assimilation of xylose and/or cellulose into bacterial DNA in an agricultural soil.
Specifically, 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 13C-xylose instead of unlabeled xylose, and a treatment with 13C-cellulose instead of unlabeled cellulose. Soil was sampled at days 1, 3, 7, 14, and 30 and we identified microorganisms that assimilated 13C into DNA at each point in time. The experiment was designed to provide a 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.
Results
After adding the organic matter amendment to soil, we tracked the flow of 13C from 13C-xylose or 13C-cellulose into microbial DNA over time using DNA-SIP (Figure S1). The amendment consisted of compounds representative of plant biomass including cellulose, lignin, sugars found in hemicellu-lose, 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 xyloseC (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 S2). In contrast, cellulose-C declined at a rate of approximately 18 µg C d−1 g−1 soil d.w. and 40% of added cellulose-C remained in the soil at day 30 (Figure S2).
Types of 13C-labeled OTUs changed with time and substrate
We assessed assimilation of 13C into microbial DNA by comparing the SSU rRNA gene sequence composition of SIP density gradient frations between 13C treatments and the unlabeled control (see Methods and SI). In the gradient density fractions for the control treatment, fraction density represented the majority of the variance in SSU rRNA gene composition (Figure 1). Genome G+C content correlates positively with DNA buoyant density and influences SSU rRNA gene composition in gradient fractions [51]. For the 13C-cellulose treatment, the SSU rRNA gene composition in gradient fractions deviated from control in high density fractions (> 1.72 g mL−1) on days 14 and 30 (Figure 1). For the 13C-xylose treatment, SSU rRNA gene composition in gradient fractions also deviated from control in high density fractions, but it deviated from control on days 1, 3, and 7 (Figure 1). The SSU rRNA gene composition from the 13C-cellulose treatment and 13C-xylose treatment high density gradient fractions differed indicating different microorganisms assimilated C from xylose than cellulose (Figure 1). Further, in the 13C-cellulose treatment, the SSU rRNA gene sequence composition in high density fractions was similar on days 14 and 30 indicating similar microorganisms had 13C-labeled DNA in 13C-cellulose treatments at days 14 and 30. In contrast, in the 13C-xylose treatment, the SSU rRNA gene composition of high density fractions varied between days 1, 3, and 7 indicating that different microbes had 13C-labeled DNA on each of these days. In the 13C-xylose treatment, the SSU gene composition of high density fractions was similar to control on days 14 and 30 (Figure 1) indicating that 13C was no longer detectable in bacterial DNA on these days for this treatment.
Temporal dynamics of OTU relative abundance in experimental soil
We monitored the experimental soil microbial community over the course of the experiment by surveying SSU rRNA genes in non-fractionated DNA from the experimental soil. The SSU rRNA gene composition of the non-fractionated DNA changed with time (Figure S3, P-value = 0.023, R2 = 0.63, Adonis test [60]). In contrast, the non-fractionated DNA SSU rRNA gene composition showed no statistical evidence for changing with treatment (P-value 0.23, Adonis test) (Figure S3). The latter result demonstrates the substitution of 13C-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 S4). 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 Bacilli (decreased), Flavobacteria (de-creased), Gammaproteobacteria (decreased), and Herpetosiphonales (increased) (Figure S5). Of the 29 OTUs that changed in relative abundance over time, 14 putatively incorporated 13C into DNA (Figure S4). OTUs that likely assimilated 13C from 13C-cellulose into DNA tended to increase in relative abundance with time whereas OTUs that assimilated 13C from 13C-xylose tended to decrease (Figure S6). OTUs that responded to both substrates did not exhibit a consistent relative abundance response over time as a group (Figure S4 and S6).
Changes in the phylogenetic composition of 13C-labeled OTUs with time
If an OTU exhibited strong evidence for assimilating 13C 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 13C-labeling from both 13C-cellulose and 13C-xylose for each OTU. Forty-one OTUs responded to 13C-xylose, 55 OTUs responded to 13C-cellulose, and 8 OTUs responded to both xylose and cellulose (Figure 2, Figure 3, Figure S7, Table S1, and Table S2). 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 S8).
The phylogenetic composition of xylose responders changed with time (Figure 2 and Figure 4) and 86% of xylose responders shared > 97% SSU rRNA gene sequence identity with bacteria cultured in isolation (Table S1). On day 1, Bacilli OTUs represented 84% of xylose responders (Figure 4) and the majority of these OTUs were closely related to cultured representatives of the genus Paenibacillus (Table S1, Figure 3). For example, “OTU.57” (Table S1), annotated as Paenibacillus, had a strong signal of 13C-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 13C-labeled after day 1 (Figure S9). On day 3, Bacteroidetes OTUs comprised 63% of xy-lose responders (Figure 4) and these OTUs were closely related to cultured representatives of the Flavobacteriales and Sphingobacteriales (Table S1, Figure 3). For example, “OTU.14”, annotated as a flavobacterium, had a strong signal for 13C-labeling in the 13C-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 13C-labeling beyond day 3 (Figure S9). Finally, on day 7, Actinobacteria OTUs represented 53% of the xylose responders (Figure 4) and these OTUs were closely related to cultured representatives of Micrococcales (Table S1, Figure 3). For example, “OTU.4”, annotated as Agromyces, had signal for 13C-labeling in the 13C-xylose treatment on days 1, 3 and 7 with the strongest evidence of 13C-labeling at day 7 and did not appear 13C-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 S9). Proteobacteria were also common among xylose responders at day 7 where they comprised 40% of xylose responder OTUs. Notably, Proteobacteria represented the majority (6 of 8) of OTUs that responded to both cellulose and xylose (Figure S7).
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 2, Figure S8, and Figure S6). Cellulose responders belonged to the Proteobacteria (46%), Verrucomicrobia (16%), Planctomycetes (16%), Chloroflexi (8%), Bacteroidetes (8%), Actinobacteria (3%), and Melainabacteria (1 OTU) (Table S2).
The majority (85%) of cellulose responders out-side of the Proteobacteria shared< 97% SSU rRNA gene sequence identity to bacteria cultured in isolation. For example, 70% of the Verrucomicrobia cellulose responders fell within unidentified Spartobacteria clades (Figure 3), and these shared < 85% SSU rRNA gene sequence identity to any characterized isolate. The Spartobacteria OTU “OTU.2192” exemplified many cellulose responders (Table S2, Figure S9). “OTU.2192” increased in non-fractionated DNA relative abundance with time and evidence for 13C-labeling of “OTU.2192” in the 13C-cellulose treatment increased over time with the strongest evidence at days 14 and 30 (Figure S9). Most Chloroflexi cellulose responders belonged to an unidentified clade within the Herpetosiphonales (Figure 3) and they shared< 89% SSU rRNA gene sequence identity to any characterized isolate. Characteristic of Chloroflexi cellulose responders, “OTU.64” increased in relative abundance over 30 days and evidence for 13C-labeling of “OTU.64” in the 13C-cellulose treatment peaked days 14 and 30 (Figure S9). Bacteroidetes cellulose responders fell within the Cytophagales in contrast with Bacteroidetes xylose responders that belonged instead to the Flavobacteriales or Sphingobacteriales (Figure 3). Bacteroidetes cellulose responders included one OTU that shared 100% SSU rRNA gene sequence identity to a Sporocytophaga species, a genus known to include cellulose degraders. The majority (86%) of cellulose responders in the Proteobacteria were closely related (> 97% identity) to bacteria cultured in isolation, including representatives of the genera: Cellvibrio, Devosia, Rhizobium, and Sorangium, which are all known for their ability to degrade cellulose (Table S2). Proteobacterial cellulose responders belonged to Alpha (13 OTUs), Beta (4 OTUs), Gamma (5 OTUs), and Delta-proteobacteria (6 OTUs).
Characteristics of cellulose and xylose responders
Cellulose responders, relative to xylose responders, tended to have lower relative abundance in nonfractionated DNA, demonstrated signal consistent with higher atom % 13C in labeled DNA, and had lower estimated rrn copy number (Figure 5). 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 4, 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 although “OTU.6” annotated as Cellvibrio a cellulose responder at day 14 was the responder found at highest relative abundance (approximately 3% or SSU rRNA genes at day 14, Figure S9).
DNA buoyant density (BD) increases in proportion to atom % 13C. Hence, the extent of 13C incorporation into DNA can be evaluated by the difference in BD between 13C-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 13C-xylose or 13C-cellulose treatments (see SI for the detailed calculation, Figure S11). We refer to the change in center of mass position for an OTU in response to 13C-labeling as . can be used to compare relative differences in 13C-labeling between OTUs. 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 (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 5, P-value = 1.8610 x 10−6, Wilcoxon Rank Sum test).
We predicted the rrn gene copy number for responders as described [61]. The ability to proliferate after rapid nutrient influx correlates positively to a microorganism’s rrn copy number [62]. Cellulose responders possessed fewer estimated 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 5 and Figure S10). Furthermore, the estimated 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 S10,Figure 5).
We assessed phylogenetic clustering of 13C-responsive OTUs with the Nearest Taxon Index (NTI) and the Net Relatedness Index (NRI) [63]. We also quantified the average clade depth of cellulose and xylose responders with the consenTRAIT metric [64]. 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 [65]. 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 is approximately 0.017 SSU rRNA gene sequence dissimilarity for arabinase (another five C sugar found in hemicellulose) utilization as inferred from genomic analyses, and was 0.013 and 0.034 SSU rRNA gene sequence dissimilarity for glucosidase and cellulase genomic potential, respectively [64, 66]. These results indicate xylose responders form terminal clusters dispersed throughout the phylogeny while cellulose responders form deep clades of terminally clustered OTUs.
Discussion
We identified microorganisms participating in soil C cycling using a nucleic acid SIP approach. Specifically, we observed assimilation of 13C from either 13C-xylose or 13C-cellulose into DNA for 104 OTUs in an agricultural soil. We found 13C from 13C-xylose appeared to move into and then out of groups of related OTUs over time. By coupling nucleic acid SIP to high throughput sequencing we could diagnose OTU activity even when OTUs were at low relative abundance in non-fractionated DNA (e.g. on three occasions we did not detect 13C-responders in the non-fractionated DNA). Our results support the degradative succession hypothesis, elucidate ecophysiological properties of soil microorganisms, reveal activity of widespread un-cultured soil bacteria, and begin to piece together the microbial food web in soils.
The degradative succession hypothesis predicts an ecological transition in activity during the decomposition of labile and structural plant organic matter. Our results concur with the degradative succession hypothesis. Microorganisms consumed xylose-C before cellulose-c and assimilated xylose-C into DNA faster than to cellulose-C. Xylose is a major constituent of hemicellulose and is a labile component of fresh plant biomass. The phylogenetic composition of xylose responders changed between days 1, 3 and 7 and few OTUs appeared 13C-labeled in the 13C-xylose treatment after day 7. In the 13C-cellulose treatment, 13C-labeled OTUs were few in the in the beginning of the experiment but most abundant day 14 and 30. Finally, few (8 of 104) OTUs appeared to metabolize both xylose and cellulose meaning over 30 days cellulose responders grew in succession to xylose responders.
Correlations between community composition and environmental characteristics often indirectly reveal microorganisms belonging to ecologically defined groups [67]. In this experiment, we directly identified ecological groups as a function of in situ metabolism and inferred the ecological properties of these groups through temporal dynamics of 13C-assimilation, the extent of OTU 13C-labeling, and phylogenetic affiliation. Xylose responders grew faster than cellulose responders and appeared to assimilate C from multiple sources. Xylose responders assimilated xylose-C into DNA within 24 hours and had low lose responders suggesting xylose was not the sole C source used for growth. Xylose represented 15% of the amendment and 3.5% of total soil C. Xy-lose responders often included the most abundant OTUs within the non-fractionated DNA and had high estimated rrn copy number relative to cellulose responders. However, to some degree, high rrn gene copy number may inflate observed xylose responder relative abundance. Notably, the majority of xylose responder SSU rRNA genes (86%) matched SSU rRNA genes from cultured isolates at high sequence identity (> 97%).
Cellulose responders, on the other hand, incorporated 13C into DNA after xylose responders and appeared to specialize in using cellulose as a C source. Cellulose responders grew over a span of weeks and had high indicating cellulose remained their dominant C source even though multiple C sources were present (cellulose represented 6% of total C present in soil at the start of the experiment). Cellulose responders were also lower in relative abundance on average within the nonfractionated DNA and had lower estimated rrn copy number than xylose responders. The majority of cellulose responders were not close relatives of cultured isolates although a number of cellulose responders shared high SSU rRNA gene sequence identity with cultured Proteobacteria (e.g. Cellvibrio),. We identified cellulose responders among phyla such as Verrucomicrobia, Chloroflexi, and Planctomycetes – common soil phyla whose functions within soil communities remain unknown.
Verrucomicrobia represented 16% of the cellulose responders. Verrucomicrobia are cosmopolitan soil microbes [68] that can make up to 23% of SSU rRNA gene sequences in soils [68] and 9.8% of soil SSU rRNA [69]. Genomic analyses and laboratory experiments show that various isolates within the Verrucomicrobia are capable of methanotrophy, diazotrophy, and cellulose degradation [70, 71]. Moreover, Verrucomicrobia have been hypothesized to degrade polysaccharides in many environments [72–74]. However, only one of the 15 most abundant verrucomicrobial phylo-types in globally distributed soil samples shared > 93% SSU rRNA gene sequence identity with a cultured isolate [68] and hence the role of soil Verrucomicrobia in global C-cycling remains unknown. The majority of verrucomicrobial cellulose responders belonged to two clades that fall within the Spartobacteria (Figure 3). Spartobacteria outnumbered all other Verrucomicrobia phylotypes in SSU rRNA gene surveys of 181 globally distributed soil samples [68]. Given their ubiquity and abundance in soil as well as their demonstrated incorporation of 13C from 13C-cellulose, Verrucomicrobia lineages, particularly Spartobacteria, may be important contributors to cellulose decomposition on a global scale.
Other notable cellulose responders include OTUs in the Planctomycetes and Chloroflexi both of which have previously been shown to assimilate 13C from 13C-cellulose added to soil [75]. Planctomycetes are common in soil [40], comprising 4 to 7% of bacterial cells in many soils [76, 77] and 7% ± 5% of SSU rRNA [78]. Although soil Planctomycetes are widespread, their activities in soil remain uncharacterized. Plantomycetes represented 16% of cellulose responders and shared < 92% SSU rRNA gene sequence identity to their most closely related cultured isolates. Chloroflexi are known for metabolically dynamic lifestyles ranging from anoxygenic phototrophy to organohalide respiration [79] and are among the six most abundant bacterial phyla in soil [40]. Recent studies have focused on Chloroflexi roles in C cycling [79–81] and several Chloroflexi isolates use cellulose [79–81]. Four of the five Chloroflexi cellulose responders belong to a single clade within the Herpetosiphonales (Figure 3).
Finally, a single cellulose responder belonged to the Melainabacteria phylum (95% shared SSU rRNA gene sequence identity with Vampirovibrio chlorellavorus). The phylogenetic position of Melainabacteria is debated but Melainabacteria have been proposed to be a non-phototrophic sister phylum to Cyanobacteria. An analysis of a Melainabacteria genome [82] suggests the genomic capacity to degrade polysaccharides though Vampirovibrio chlorellavorus is an obligate predator of greenalga [83].
Responders did not necessarily assimilate 13C directly from 13C-xylose or 13C-cellulose. In many ways, knowledge of secondary C degradation and/or microbial biomass turnover may be more interesting with respect to the soil C-cycle than knowledge of primary degradation. The response to xylose suggests xylose-C moved through different trophic levels within the soil bacterial food web. The Bacilli degraded xylose first (65% of the xylose-C had been respired by day 1) representing 84% of day 1 xylose responders. Bacilli also comprised about 6% of SSU rRNA genes present in non-fractionated DNA on day 1. However, few Bacilli remained 13C-labeled by day 3 and their abundance declined reaching about 2% of soil SSU rRNA genes by day 30. Members of the Bacillus [84] and Paenibacillus in particular [59] have been previously implicated as labile C decomposers. The decline in relative abundance of Bacilli could be attributed to mortality and/or sporulation coupled to mother cell lysis. Bacteroidetes OTUs appeared 13C-labeled at day 3 concomitant with the decline in relative abundance and loss of 13C-label for Bacilli. Finally, Actinobacteria appeared 13C-labeled at day 7 as Bacteroidetes xylose responders declined in relative abundance and became unlabeled. Hence, it seems reasonable to propose that Bacteroidetes and Actinobacteria xylose responders became labeled via the consumption of 13C derived from 13C-labeled microbial biomass as opposed to primary degradation of 13C-xylose.
The inferred physiology of Actinobacteria and Bacteroidetes xylose responders provides further evidence for C transfer by saprotrophy and/or predation. Most of the Actinobacteria xylose responders that appeared 13C-labeled at day 7 were members of the Micrococcales (Figure 3) and the most abundant 13C-labeled Micrococcales OTU at day 7 (OTU.4, Table S1) is annotated as belonging in the Agromyces. Agromyces are facultative predators that feed on the gram-positive Luteobacter in culture [85]. Additionally, certain types of Bacteroidetes can assimilate 13C from 13C-labeled Escherichia coli added to soil [86]. Alternatively, it is possible that Bacilli, Bacteroidetes, and Actinobacteria are adapted to use xylose at different concentrations and that the observed activity dynamics resulted from changes in xylose concentration over time and/or that Actinobacteria and Bacteroidetes xylose responders consumed waste products generated by primary xylose metabolism (e.g. organic acids produced during xylose metabolism). These latter two hypotheses cannot explain the sequential loss of 13C-label, however. If trophic transfer caused the activity dynamics, at least three different ecological groups exchanged C in 7 days. Models of the soil C cycle often exclude trophic interactions between soil bacteria (e.g. [87]), yet when soil C models do account for predators and/or saprophytes, trophic interactions are predicted to have significant effects on the fate of soil C [88].
Implications for soil C cycling models
Functional niche characterization for soil microorganisms is necessary to predict whether and how biogeochemical processes vary with microbial community composition. Functional niches are defined by soil microbiologists and have been successfully incorporated into biogeochemical process models (E.g. [88, 89]). In some C models ecological strategies such as growth rate and substrate specificity are parameters for functional niche behavior [88]. The phylogenetic breadth of a functionally defined group is often inferred from the distribution of diagnostic genes across genomes [66] or from the physiology of isolates cultured on laboratory media [64]. For instance, the wide distribution of the glycolysis operon in microbial genomes is interpreted as evidence that many soil microorganisms participate in glucose turnover [9]. 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 soil. For instance, fast growth and rapid resuscitation allow microorganisms to compete for labile C which may often be transient in soil. Hence, life history traits may constrain the diversity of microbes that metabolize a given C source in the soil under a given set of conditions.
Biogeochemical processes mediated by a broad array of taxa are assumed to be insensitive to community change whereas community change is assumed to affect processes mediated by a narrow suite of microorganisms [9, 90]. In addition, the diversity of a functionally defined group engaged in a specific C transformation is expected to correlate positively with C lability [9]. However, the diversity of labile C and structural C decomposers in soil has not been quantified directly. We found comparable numbers of OTUs responded to 13C-cellulose and 13C-xylose (63 and 49, respectively). Cellulose responders were phylogenetically clustered suggesting that the ability to degrade cellulose is phylogenetically conserved. The clade depth of cellulose responders, 0.028 SSU rRNA gene sequence dissimilarity, is on the same order as that observed for glycoside hydrolases which are diagnostic enzymes for cellulose degradation [66]. Xy-lose responders clustered in terminal branches indicating groups of closely related taxa metabolized xylose but xylose responders also clustered phylo-genetically with respect to time of response (Figure 3, Figure 4). For example, xylose responders on day 1 are dominated by members of Paenibacillus. Thus, microorganisms that degraded labile C and structural C were both limited in diversity. Although the genes for xylose metabolism are likely widespread in the soil community, it’s possible only a limited diversity of organisms had the ecological characteristics required to degrade xylose under experimental conditions. Therefore it’s possible that only a limited number of taxa actually participate in the metabolism of labile C-sources under a given set of conditions, and hence changes in community composition may alter the dynamics of structural and labile C-transformations in soil.
Broadly, we observed labile C use by fast growing generalists and structural C use by slow growing specialists. These results agree with the MIMICS model which simulates leaf litter decomposition by modeling microbial decomposers as two functionally defined groups, copiotrophs or oligotrophs [89]. Including these functional types improved predictions of C storage in response to environmental change relative to models that did not consider any microbial physiological diversity. We identified microbial lineages engaged in labile and structural C decomposition that can be defined as copiotrophs or oligotrophs, respectively. We also observed rate differences in turnover of xylose responder biomass relative to cellulose responders which may be important to consider when modeling microbial turnover input to SOM. It’s also clear that the characterization of microbes as copiotrophs and oligotrophs may miss other, vital functional types mediating C-cycling in soil. That is, soil-C may travel through multiple bacterial trophic levels where each C transfer represents an opportunity for C stabilization in association with soil minerals or C loss by respiration. Our understanding of soil C dynamics will likely improve as we develop a more granular understanding of the ecological diversity of microorganisms that mediate C transformations in soil.
Conclusion
Microorganisms govern C-transformations in soil influencing climate change on a global scale but we do not know the identities of microorganisms that carry out specific transformations. In this experiment microbes from physiologically uncharacterized but cosmopolitan soil lineages participated in cellulose decomposition. Cellulose responders included members of the Verrucomicrobia (Spartobacteria), Chloroflexi, Bacteroidetes and Planctomycetes. Spartobacte ria in particular are globally cosmopolitan soil microorganisms and are often the most abundant Verrucomicrobia order in soil [68]. Fast-growing aerobic spore formers from Firmicutes assimilated labile C in the form of xylose. Xylose responders within the Bacteroidetes and Actinobacteria likely became labeled by consuming 13C-labeled constituents of microbial biomass either by sapro-trophy or predation. Our results suggest that cosmopolitan Spartobacteria may degrade cellulose on a global scale, plant C may travel through a trophic cascade within the bacterial food web after primary decomposition, 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.
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: https://github.com/chuckpr/CSIP_succession_data_analysis.
DNA sequences are deposited on MG-RAST (Accession XXXXXXX).
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. We amended soils with a mixture containing 2.9 mg C g−1 soil dry weight (d.w.) and brought experimental 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 (Teknova C9795) 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 switch-grass biomass with hemicellulose replaced by its constituent monomers [91]. 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 13C-cellulose instead of unlabeled cellulose (synthesized as described in SI), and (3) a treatment with 13C-xylose (98 atom% 13C, 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[openbullet]C until nucleic acid extraction. The abbreviation 13CXPS refers to the 13C-xylose treatment (13C Xylose Plant Simulant), 13CCPS refers to the 13C-cellulose treatment, and 12CCPS refers to the control treatment.
We used DESeq2 (R package), an RNA-Seq differential expression statistical framework [92], to identify OTUs that were enriched in high density gradient fractions from 13C-treatments relative to corresponding gradient fractions from control treatments (for review of RNA-Seq differential expression statistics applied to microbiome OUT count data see (30)). We define “high density gradient fractions” as gradient fractions whose density falls between 1.7125 and 1.755 g ml−1. Briefly, DESeq2 includes several features that enable robust estimates of standard error in addition to reliable ranking of logarithmic fold change (LFC) (i.e. gamma-Poisson regression coefficients) in OTU relative abundance even with low count OTUs where LFC can often be noisy. Further, statistical evaluation of LFC can be performed with user-selected thresholds as opposed to the typical null hypothesis that LFC is exactly zero enabling the most biologically interesting OTUs to be identified for subsequent analyses. For each OTU, we calculated LFC and corresponding standard errors for enrichment in high density gradient fractions of 13C treatments relative to control. Subsequently, a one-sided Wald test was used to statistically assess LFC values. The user-defined null hypothesis was that LFC was less than one standard deviation above the mean of all LFC values. P-values were corrected for multiple comparisons using the Benjamini and Hochberg method [93]. We independently filtered OTUs on the basis of sparsity prior to correcting P-values for multiple comparisons. The sparsity value that yielded the most adjusted P-values less than 0.10 was selected for independent filtering by sparsity. Briefly, OTUs were eliminated if they failed to appear in at least 45% of high density gradient fractions for a given 13C/control treatment pair. These sparse OTUs are unlikely to have sufficient data to allow for the determination of statistical significance. We selected a false discovery rate of 10% to denote statistical significance.
See SI for additional information on experimental and analytical methods.
Acknowledgements
The authors would like to acknowledge the assistance of John Christian Gaby and Mallory Choudoir in developing the method used to produce 13C-labeled cellulose. We would also like to thank Steve Zinder, Nelson Hairston, and Nick Youngblut for providing comments that were helpful in the development of this manuscript. 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.
Footnotes
Abbreviations: C, Carbon; OTU, Operational Taxonomic Unit; SOM, Soil Organic Matter; BD, Buoyand Density; SIP, Stable Isotope Probing
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