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Keywords:

  • soil alkane degradation potential;
  • cultivation;
  • growth matrix-based enrichment;
  • alkB T-RFLP;
  • seed bank;
  • rare biosphere

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Alkane-degrading bacteria were isolated from uncontaminated soil microcosms, which had been incubated with maize litter as natural alkane source. The isolates served to understand spatio-temporal community changes at the soil–litter interface, which had been detected using alkB as a functional marker gene for bacterial alkane degraders. To obtain a large spectrum of isolates, liquid subcultivation was combined with a matrix-assisted enrichment (Teflon membranes, litter). Elevated cell numbers of alkane degraders were detected by most probable number counting indicating enhanced alkane degradation potential in soil in response to litter treatment. Partial 16S rRNA gene sequencing of 395 isolates revealed forty different phylogenetic groups [operational taxonomic units (OTUs)] and spatio-temporal shifts in community composition. Ten OTUs comprised so far unknown alkane degraders, and five OTUs represented putative new bacterial genera. The combination of enrichment methods yielded a higher diversity of isolates than liquid subcultivation alone. Comparison of 16S rRNA gene T-RFLP profiles indicated that many alkane degraders present in the enrichments were not detectable in the DNA extracts from soil microcosms. These possibly rare specialists might represent a seed bank for the alkane degradation capacity in uncontaminated soil. This relevant ecosystem function can be fostered by the formation of the soil–litter interface.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Studies on alkane-degrading isolates, but also molecular biological screening of environmental DNA, have resulted in a detailed understanding of bacterial alkane degradation. Natural alkane sources like decaying microorganisms, marine algae and terrestrial plants account for a steady low hydrocarbon input into the environment, which explains the ubiquity of the alkane degradation potential (Heredia, 2003; Rojo, 2009). Especially habitats like the soil–litter interface might therefore maintain this function despite only minor selection pressure. However, the role of biogeochemical interfaces (BGIs) in soil regarding specific ecosystem functions (e.g. bacterial alkane degradation) is largely unexplored and poses one of the great challenges in future soil science (Totsche et al., 2010). Recently, Schulz et al. (2012) reported that plant litter-derived alkanes significantly affect bacterial alkane degraders at the soil–litter interface. These authors applied molecular fingerprinting [terminal restriction fragment length polymorphism (T-RFLP) analysis] targeting the gene for a phylogenetically widespread alkane monooxygenase (alkB). The study revealed significant spatio-temporal community changes and by trend a temporal richness increase in the alkB gene pool at the soil–litter interface. However, bacteria possess varying numbers of alkB genes (1–6 genes per cell), and the gene is phylogenetically not well conserved (with similarities among gene paralogs as low as 60% in one cell; Van Beilen et al., 2003; Tourova et al., 2008). Thus, it was impossible to infer further information on the phylogenetic richness and affiliation of bacteria from the environmental alkB profiles (Schulz et al., 2012).

In the present study, we selectively enriched, isolated and identified alkane degraders from the same microcosms (uncontaminated sand soil amended with maize litter) to address the open questions of the molecular-based study (Schulz et al., 2012). Using a combination of cultivation strategies, we sought to enlarge the phylogenetic richness of isolates. The obtained isolates were expected to reveal the phylogenetic distribution of the alkane degradation potential in uncontaminated soil and the influence of soil–litter interfaces on ecosystem functioning.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Sampling

Sandy soil was taken and incubated under constant laboratory conditions with freshly chopped maize litter (Zea mays) as described previously (Schulz et al., 2012). Detailed information on soil characteristics and incubation conditions is given in the Supporting Information, Data S1. Briefly, the soil had no history of anthropogenic alkane contamination. Triplicate cores were sacrificed prior to soil incubation with litter (T0), after two (T2), eight (T8) and thirty (T30) weeks of incubation and subsamples of the litter, the soil 0–0.5 cm beneath the litter (soil–litter interface) and the soil 1–1.5 cm beneath the soil–litter interface (bulk soil) were analysed. Control soil cores were incubated without litter material and sampled and analysed like litter-treated cores. Pooled samples from three replicate cores of each treatment were used for the enumeration and enrichment of bacteria.

Cell enumeration by a most probable number assay

Alkane-degrading bacteria and total heterotrophic bacteria were quantified using a most probable number (MPN) assay in 96-well microtiter plates with eight replicates per dilution as described elsewhere (Wrenn & Venosa, 1996; Kloos et al., 2006). Briefly, bacteria were detached from 1 g soil or 0.25 g litter by 1 h rotary shaking with a 10-fold (w/v) amount of HC-mineral medium (Kloos et al., 2006). After the material settled, 10-fold serial dilutions of the supernatant were incubated with HC-mineral medium and n-hexadecane (Sigma Aldrich) as a straight-chain alkane model substrate for the enumeration of alkane degraders. Heterotrophic bacteria were enumerated after growth on diluted Luria Broth (1 : 5 with HC-mineral medium). To avoid fungal growth 0.2 g L−1, cycloheximide was added to all incubations. Plates were incubated up to 8 weeks to allow colony formation of slow-growing cells. Wells were checked weekly by measuring the turbidity at 600 nm in a microtiter plate reader Spectramax 250 (Molecular Devices, Sunnywale, CA) until no further increase in the number of positive wells was observed. Final cell numbers were calculated using a MPN calculator program (http://www.wiwiss.fu-berlin.de/institute/iso/mitarbeiter/wilrich/MPN_ver2.xls). All values are calculated as log10MPN and respective log10 standard deviations, and log-normal distribution is assumed (Jarvis et al., 2010).

Extraction of alkanes

Extraction of alkanes was performed in triplicate as described by Schulz et al. (2012) prior to total alkane analysis by gas chromatography–mass spectrometry (Agilent Technologies, Santa Clara, CA) Odd- and even-numbered alkanes with chain lengths between C10–C40 and two branched alkanes (pristane, phytane) known to originate mainly from plants were quantified. Detailed information on the extraction protocol is given in the Data S2.

Enrichment of alkane-degrading bacteria

Enrichment of alkane-degrading bacteria was performed by conventional liquid subcultivation and a litter- and Teflon membrane-based technique to account for planktonic and sessile bacteria, respectively. The hydrophobic surfaces of alkane-spiked Teflon membranes and plant litter (contact angles 88 ± 5° and 106 ± 2°, respectively) were chosen as substitutes of alkane-containing soil particles or plant litter to which bacteria attach to increase the fluxes of poorly soluble substrates (Bastiaens et al., 2000; Ortega-Calvo et al., 2003). Maize litter and sand soil subsamples from the microcosms were separately incubated with a 10-fold (w/v) amount of mineral medium (per one litre: 0.5 g NaCl, 0.5 g KH2PO4, 0.4 g NH4Cl, 0.2 g  Na2SO4, 0.4 g KCl, 0.1 g CaCl2 × 2H2O, 0.5 g MgCl2 × 2H2O) and 0.2% sterile n-hexadecane (Alfa Aesar, Karlsruhe Germany) as a liquid straight-chain model alkane. Incubation was performed in an Erlenmeyer flask on a rotary shaker at room temperature in the dark for 1 month (after 2 weeks, the enrichment cultures were fed with a further amount of 0.2% n-hexadecane). Teflon membranes (2.25 cm2) were immersed in n-hexadecane for 10 s for spiking after sequential washing in dichloromethane, ethanol and sterile water and added as hydrophobic growth matrices to each subsample. After a first enrichment period supernatant, litter material and Teflon membranes of each sample were transferred separately to fresh mineral medium containing n-hexadecane as sole carbon source for a further enrichment over 1 month (under the same conditions as mentioned above). Thus, bacteria depended on the alkane degradation for sufficient energy yields over 2 months in total.

Single colonies were isolated in a three-step purification procedure. At first, in conventional plating, the supernatants were streaked in serial dilutions (100–10−9) on R2A agar (Difco/Becton Dickinson, Heidelberg, Germany). Alternatively, Teflon membranes were rinsed with 3 mL sterile water for detachment of loosely associated cells prior to the transfer to mineral medium agar [per one litre medium: 18 g agar (Difco/Becton Dickinson)] supplemented with 0.2 g L−1 cycloheximide (to avoid fungal growth) and 0.2% superficially spread n-hexadecane. After growth, cultures from under, around and directly on the Teflon membranes were scraped off, diluted in R2A (1 mL) and plated as described for supernatant. Litter material was treated likewise. Solid media plates were incubated for at least 2 weeks at room temperature to allow slow-growing bacteria to develop colonies. Secondly, cultures of different morphology, that is, morphotypes, (colony size, colour, fringe, surface structure) were individually transferred to R2A agar plates and again incubated for at least 2 weeks. Thirdly, cultures representing single morphotypes were selected for their ability to grow with n-hexadecane as sole carbon source by plating on mineral medium agar supplemented with 0.2 g L−1 cycloheximide and 0.2% n-hexadecane. Only single morphotypes, thus apparently pure cultures, were considered for further DNA extraction (see below).

DNA extraction and PCR amplification

DNA of isolated alkane degraders was extracted by microwave heat treatment as described previously (Orsini & Romano-Spica, 2001), and extracts were used without further purification for 16S rRNA gene amplification. DNA of enrichment cultures obtained with liquid sub-cultivation or growth matrices was extracted as described by Maher et al. (2001) with adjustment to smaller volumes (900 μL total reaction volume) and modified incubation time (1 h). For litter material, only liquid subcultures were analysed as no processable DNA could be obtained directly from litter material. Whole community DNA from soil samples was extracted as described by Griffiths et al. (2000). DNA quality and quantity was determined by agarose gel electrophoresis and spectrophotometrically (Nanodrop® ND-1000; Peqlab, Germany). PCR mixtures comprised 1X Taq PCR Master Mix Kit (Qiagen, Hilden, Germany), 0.01 μM 27f (5′-AGAGTTTGATCMTGGCTCAG-3′; Lane, 1991) and 1525r (5′-AAGGAGGTGWTCCARCC-3′; Weisburg et al., 1991) each and 10 ng DNA in a total reaction volume of 25 μL. After amplification (94 °C/4 min; 30 cycles: 94 °C/45 s, 58 °C/30 s, 72 °C/1 min and finally 72 °C/10 min on a Tetrad 2 thermocycler; BioRad Laboratories, Munich, Germany), PCR products were checked for correct amplicon size (1500 bp) by agarose gel electrophoresis, purified with Wizard® SV Gel and PCR Clean-UP System (Promega, Mannheim, Germany) and subsequently quantified spectrophotometrically (Nanodrop® ND-1000).

Amplified ribosomal DNA restriction analysis

Ten nanogram purified 16S rRNA gene amplicons of alkane-degrading isolates were digested with 2.5 U AluI (New England Biolabs, Ipswich) at 37 °C, overnight. Resulting restriction fragment patterns were analysed by gel electrophoresis (2% agarose, 0.5X TBE, 30 V, overnight, BioRad chamber; BioRad Laboratories) with subsequent SYBR GOLD nucleic acid gel staining (Invitrogen, Paisley, UK). Identification of restriction patterns and grouping into operational taxonomic units (OTU, for further information see section below) was carried out with the software Phoretix™ 1D Advanced version 5.20 (Nonlinear Dynamics, Newcastle, UK) applying the single linkage method for clustering. Dendrograms were calculated without differentiation of the sample origin (time, depth, enrichment method). Generally, one representative of each OTU was subjected to sequence analysis. For larger clusters (> 10 isolates), 2–4 representatives were sequenced to backup the cluster identity.

Sequencing and phylogenetic analysis

16S rRNA gene PCR products of representatives from each OTU were partially sequenced with the primer 519_r (5′-GTATTACCGCGGCTGCTG-3′; Lane, 1991). The primer directly flanks the highly variable V3 region of the 16S rRNA gene thus permitting the detection of high sequence diversity despite the generation of a relatively short sequence and a certain phylogenetic affiliation at least on the family or on the genus level. BigDye® Terminator chemistry was applied (Applied Biosystems) using 10% sterile trehalose instead of water, and sequence analysis was performed on an abi prism 3100 genetic analyser system (Applied Biosystems) with the Sequence Analysis 5.3.1 software (Applied Biosystems). Nucleotide sequences were corrected manually with the Sequencher 4.8 software (Gene Codes Corporation, Ann Arbor, MI), aligned and counterchecked with BioEdit software (Hall, 1999) before uploading to the blast Basic Local Alignment Tool (Altschul et al., 1990) and the RDP Ribosomal Database Project II (Wang et al., 2007).

Richness estimation

Rarefaction curves were calculated based on the OTU-abundances detected with each enrichment type (liquid or matrix-based enrichment) at each time point with past software version 2.0.6 (http://folk.uio.no/ohammer/past, Hammer et al., 2001). In cases where representative sequences of different OTU were identical on the genus level (or family level, whenever a more detailed affiliation was impossible), the clusters were combined and regarded as one OTU. Only sequences were considered where an unambiguous affiliation at least on the family level was possible (for 100% of the database hits). The effectiveness of the enrichments was compared by applying a Michaelis–Menten fitting to the equation y = ax/(b+x) for individual rarefaction curves: x represents the number of morphologically distinct colonies sampled (n), y represents the number of observed OTU (Sobs), a describes the estimated maximum of species (Smax) in the sample and b the number of morphologically distinct colonies needed to be sampled to obtain exactly 50% Smax (B). The nonparametric Chao1 richness estimator was calculated using the EstimateS which takes into account environmentally relevant species distribution for bacteria and also unseen species (http://viceroy.eeb.uconn.edu/estimates; Colwell, 1997). More detailed information is given in the Supporting Information (Data S3).

We are aware of the suboptimal resolution and roughness of richness estimates using amplified ribosomal DNA restriction analysis (ARDRA) and Michaelis–Menten fitting, and we know that the sampling effort is far from being sufficient to describe the complete phylogenetic richness in the sample. However, the richness estimates were mainly applied to compare the effectiveness of the enrichments and thus were appropriate for our needs.

T-RFLP analysis

T-RFLP analysis was performed with 16S rRNA gene amplicons of DNA extracted directly from litter or soil [defined as total community (TC)] and from enrichment cultures [liquid, L; Teflon membranes or litter, M; total enriched community (TEC, equals L + M)]. The PCR prior to T-RFLP analysis was carried out as described above using a 6-carboxyfluorescein (6-FAM)-labelled forward primer. One hundred nanogram purified PCR product from each sample was digested overnight at 37 °C with 2 U MspI (NEB). After ethanol precipitation, T-RFLP analysis was run on an abi prism 3100 genetic analyser system using the GeneScan™ 500 ROX™ size standard (both Applied Biosystems). Data were analysed with GeneMapper V3.7 (Applied Biosystems). Distinct T-RFs were defined as OTUs. T-RFs were binned and aligned using the method of Abdo et al. (2006) in statistical software r version 2.10.0 (R Development Core Team, 2009; http://www.r-project.org/index.html) as described in more detail in the Supporting Information (Data S4). Output tables were translated into presence/absence matrices, and OTUs detected in the different layers at the same time point were summarised to one signal. The number of OTUs unique to a respective sample (i.e. TC, L, and TEC) was calculated as well as the number of OTUs shared between samples. Rank abundance curves were calculated ranking the OTUs (i.e. T-RFs) according to their per cent fraction of the total fluorescence (i.e. relative abundance) in the T-RFLP profile in descending order (the T-RF with highest relative abundance was given the rank one). High abundant ranks were here defined as T-RFs with a relative abundance > 3% which is threefold mean relative abundance of all T-RFs. Low abundant ranks were defined as T-RFs contributing < 0.1% to the total fluorescence. The log abundances were used for better illustration of low abundant ranks. The evenness (inline image) was calculated using the diversity index tool in the past software (Hammer et al., 2001). An evenness value of 1 suggests that all OTUs have the same abundance in the sample. To compare if the low abundant T-RFs were identical in different samples, Jaccard similarity indices were calculated based on the presence/absence of only low abundant T-RFs (relative abundance < 0.1%).

Statistics

Statistically significant differences for cell number were evaluated with Student's t-test with GraphPad software online tool© (GraphPad Software Inc., La Jolla, CA; http://graphpad.com/) using the t-test calculator (format SD). To test for significant correlations between changes in cell numbers, alkane concentration or isolation results (number of morphologically distinct isolates, phylogenetic richness) and the litter treatment over time and depth one- or two-way anova (for single or combined effects, respectively) was run in Origin 8.5 data analysis and graphing software (OriginLab Co., Northhampton, MA). Statistical analysis of T-RFLP data is described in Supporting Information (Data S4). Briefly, significance of single environmental and experimental parameters was tested with Monte Carlo permutation test with 1000 permutations and final anova.

GenBank accession numbers

GenBank accession numbers for sequenced representatives, which identified restriction patterns, are JN616345JN616371 and JX444130JX444158.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Cell enumeration and alkane analysis

Generally, cell numbers of alkane degraders, enumerated with n-hexadecane as model substrate, were much lower than those of total heterotrophic bacteria (by factors of 10−3–10−5). Cell numbers on the litter material exceeded those observed in soil by factors of 102 to 103. However, the litter and the interface layer showed a similarly strong and immediate response to the availability of litter-released substances (T2; Table 1). During the first incubation period (T2 to T8), cell numbers in the interface were significantly higher than in the respective controls but also to the underlying bulk soil layer (P < 0.001; t-test). With elongated incubation time (T30), the cell numbers in the bulk soil layer converged with the respective numbers in the interface and were significantly elevated compared with controls (P < 0.001). For both soil layers, a significant influence of the litter material on the number of alkane degraders over time was observed (P < 0.05; two-way anova).

Table 1. Total alkane concentration, MPN-based numbers of alkane-degrading bacteria (evaluated with n-hexadecane as model substrate) and the number of total cultivable bacteria (evaluated with diluted rich medium) in the three subsamples (litter, interface, bulk soil) during the incubation of sandy soil microcosms with maize litter. For control microcosms incubated without litter, cell numbers are given for the two soil subsamples. Sampling time points at the beginning (T0) and after two (T2), eight (T8) and 30 (T30) weeks of incubation are indicated. Note that cell numbers are given as log10-values. Standard deviations (log10SD for log10MPN) are indicated in brackets (n = 3 for alkanes and n = 8 for MPN)
LitterLitter incubated samplesControls without litter
Total alkanea(SD)Alkane degraders (SD)Total cultivable bacteria (SD)Alkane degraders (SD)Total cultivable bacteria (SD)
μg alkane g−1 litterLog10cell number g−1 litterLog10cell number g−1 litter
  1. n.a., not applicable.

  2. a

    Published in Schulz et al. (2012).

  3. b

    Due to low amounts of single samples, litter material from triplicates had to be pooled for alkane measurements.

  4. c

    Only a mixture of interface and bulk soil was sampled at T0.

  5. d

    Significant elevation compared with the respective control at the same time point and depth (P < at least 0.05; t-test).

  6. e

    Significant elevation (for alkanes: difference) in the interface compared with the respective bulk soil samples at the same time point and treatment (P < at least 0.05; t-test).

  7. f

    For alkanes, significant difference to the preceding time point (P < at least 0.05; t-test).

T0183 (22)6.2 (0.2)9.5 (0.2)n.a.n.a.
T230b7.2 (0.2)11.9 (0.2)n.a.n.a.
T826b6.8 (0.3)12.2 (0.2)n.a.n.a.
T3025 (6)6.6 (0.4)11.2 (0.2)n.a.n.a.
Interfaceng alkane g−1 soilLog10cell number g−1 soilLog10cell number g−1 soilLog10cell number g−1 soilLog10cell number g−1 soil
T0c170 (50)3.2 (0.4)7.1 (0.2)3.4 (1.2)7.9 (2.1)
T250 (10)f4.1 (0.2)d,e9.2 (0.2)d,e2.7 (0.3)7.0 (0.2)e
T840 (10)e4.1 (0.2)d,e9.2 (0.2)d,e3.5 (0.3)7.3 (0.2)e
T3050 (10)e4.5 (0.2)d7.6 (0.2)d2.4 (0.2)7.0 (0.2)
Bulk soilng alkane g−1 soilLog10cell number g−1 soilLog10cell number g−1 soilLog10cell number g−1 soilLog10cell number g−1 soil
T0c170 (50)3.2 (0.4)7.1 (0.2)3.4 (1.2)7.9 (2.1)
T270 (30)f2.7 (0.3)6.9 (0.2)3.7 (0.3)6.7 (0.3)
T8130 (10)f3.2 (0.4)6.9 (0.2)3.2 (0.4)7.0 (0.2)
T30120 (40)4.4 (0.2)d7.5 (0.3)d3.5 (0.3)7.0 (0.2)

Changes in the total alkane concentration were significantly correlated with depth and time (P < 0.001; two-way anova) as already described earlier (Schulz et al., 2012). Briefly, litter samples showed the highest alkane concentrations, while overall alkane losses followed similar dynamics in litter and the soil–litter interface. Contrary, an initial alkane loss of 60% in the bulk soil layer was followed by a significant (P < 0.05, t-test) increase at T8 (Table 1).

Isolation

After 2 months enrichment with n-hexadecane, a total of 3350 isolates were obtained from litter and soil subsamples with the combination of liquid- and matrix-based subcultivation. After two preceding separation steps, apparently pure isolates were screened for their ability to grow with n-hexadecane as sole carbon source. A total of 395 isolates forming distinct colonies with n-hexadecane were analysed for their phylogenetic affiliation. The number of morphologically distinct n-hexadecane-utilising isolates increased over time and so did the phylogenetic richness (P < 0.05) as indicated by rarefaction curves (Fig. 1a–d, Table S1). Generally, more morphologically distinct isolates were obtained from matrices (Teflon membranes or litter) compared with colonies from liquid subcultivation. However, only at T2 and T8 a higher bacterial richness was detected with growth matrices (indicated by Smax in Fig. 1b and c). With the exception of T30, a higher overall richness of cultivable alkane degraders was obtained with the combination of enrichment methods than by either individual method. Accordingly, the nonparametric Chao1 index, which is calculated on the basis of an environmentally relevant species distribution for bacteria and also accounts for unseen species, indicated consistently higher richness estimates for the combination of methods with the exception of T2. Here, the estimates for the liquid subcultivation varied enormously (Table S1). At T30, the rarefaction curve for liquid enrichment displayed a rather linear progression with a steeper slope than observed for the combination of both enrichment methods (Fig. 1d).

image

Figure 1. (a–d) Rarefaction curves of OTU richness detected with liquid- (open), matrix-based enrichment (grey) or the combination of techniques (TEC, black) at T0 (a), after two (T2; b), eight (T8; c) and 30 weeks (T30; d). OTU richness (y-axis) is plotted against the number of analysed colonies (x-axis) which was determined by the alkane degradation capacity and a distinct morphology (summed for all subsamples). Michealis–Menten fits for individual curves are given by y = ax / (b+x) with x the number of morphologically distinct colonies sampled (n), y the number of observed OTUs (Sobs), a estimated maximum of species (Smax) and b the number of morphologically distinct colonies needed to be sampled to obtain 50% Smax (B).

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Overall, 40 different OTUs were affiliated to 31 different genera (sequence similarities to published sequences 95–100%) with the RDP classifier tool (Wang et al., 2007; Table 2). Five OTUs were assigned to bacterial groups on higher taxonomic levels (sequence similarities < 95%). Further four OTUs could not be differentiated by ARDRA into single genera and were thus identified only on the family level (Bradyrhizobiaceae, Rhizobiaceae, Comamonadaceae and Sphingomonadaceae). Bacteria belonging to the genera Pseudomonas, Rhodococcus and Flavobacterium were most abundant (36, 44 and 46 individuals of 395 total identified alkane degraders, respectively). These bacteria were isolated throughout the microcosm experiment with liquid and/or matrix-based enrichment. Some OTUs (with five or more representatives) showed temporal shifts in the abundance or a strict association with one subsample. For example, 27 Williamsia-related isolates were obtained until T2, but none at T30. Conversely, Sphingomonadaceae-affiliated isolates (n = 22) were only obtained after the litter amendment to the soil. Gordonia- (n = 13) and Tsukamurellaceae-related bacteria (n = 5) were detected exclusively on litter whereas Mitsuaria-, Microbacterium- and Chitinophaga-related bacteria (n = 5, each) were isolated solely from soil samples and never from litter material.

Table 2. Phylogenetic affiliation of 40 ARDRA-determined phylotypes (OTUs) according to RDP classifier with the closest (cultured) relatives according to RDP seqmatch tool in the adjacent column (accession numbers and sequence similarities are indicated). Certainties for the classification to the family level are indicated in brackets for those OTUs with sequence similarity to reference sequences < 95 %. The overall abundances (summarised for all time points and enrichment methods) of the OTUs are given by the number of representatives (of 395 total analysed colonies) as well as the per cent fraction of representatives detected with either liquid (L) or matrix-based (M) enrichment. Temporal progress of detection with either enrichment method is indicated for the start (T0) and after two (T2), eight (T8) and 30 (T30) weeks of incubation [with (+) one; (+) 2–10; (++) 10–20; (+++) ≥ 20 representatives]. The affiliation on the phyla level is indicated for Alpha-, Beta-, Gammaproteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia
Phyla affiliationOTU affiliation (according to RDP classifier tool)Closest (cultured) relatives (according to RDP seqmatch tool)Accession numberSequence similarityNumber of individualsLiquid transfer (L)Matrix method (M)T0T2T8T30
%of 395%%LMLMLMLM
  1. a

    No alkane degradation reported.

  2. b

    Exclusively detected in soil.

  3. c

    Alkane degradation reported.

  4. d

    OTU classified to family level.

  5. e

    Exclusively detected on plant litter.

Alpha-proteobacteria Acetobacteraceae (100%)a,b

Uncultured bacterium

Roseococcus sp. LW5

888110100       +
Aminobacter a , b Aminobacter aminovorans AJ011759 10011000      + 
Azospirillum c Azospirillum sp. TSH100 AB508901 100101000  + +   
Bosea a Bosea sp. TM19 DQ303323 100102080 +   +++
Bradyrhizobiaceae c , d

Nitrobacter vulgaris

Rhodopseudomonas palustris

Bradyrhizobium sp.

98979636733      ++
Brevundimonas c Brevundimonas sp. FWC43 AJ227798 100115545   + ++ 
Devosia a , b

Devosia sp. T15

Devosia insulae

989810100       +
Phenylobacterium b , c Caulobacter sp. H62 AB076664 9811000      + 
Rhizobiaceae c , d Rhizobium radiobacter CFBP 2712 AJ389886 10072971+  + +  
Sphingomonadaceae c , d

Sphingomonas sp. KT-1

Sphingopyxis sp. IMER-B1-11

Sphingobium sp. 3A16

100

100

100

225941  ++++++
Beta-proteobacteria Achromobacter b , c Achromobacter xylosoxidans AF225979 9531000    + + 
Aquabacterium a , b Aquabacterium parvum B6 AF035052 9611000      + 
Burkholderiales genus incertae sedis (99%)a,d

Uncultured bacterium

Leptothrix mobilis

Ideonella sp. B508-1

88

84

83

60100 + + +  
Comamonadaceae c , d

Acidovorax valerianellae

Comamonadaceae bacterium

Pseudorhodoferax soli

Comamonas terrigena

100

100

89

89

51000+ +   + 
Mitsuaria a , b Mitsuaria sp. H24L1B EU714909 9850100       +
Oxalobacteraceae (99%)c,d

Uncultured bacterium

Janthinobacterium sp. HKAM1

92

91

21000+       
Variovorax c Variovorax sp. TUT1027 AB098595 100141486 + +++++
Gamma-proteobacteria Acinetobacter c Acinetobacter sp. H1 AY663435 10091189++   +  
Lysobacter b , c Lysobacter ximonensis; XM415 EU237492 10011000      + 
Pseudomonas c Pseudomonas sp. AJ002801 100363169 +  ++++++
Pseudoxanthomonas c Pseudoxanthomonas sp. IMER-B211 FJ772009 100135446+++++++ 
Stenotrophomonas c Stenotrophomonas sp. AJ243605 100202080  ++ +++ 
Thermomonas a , e Thermomonas brevis S47 AB355702 9610100     +  
Actinobacteria Gordonia c , e Gordonia sp. 3/4 EU041712 100134654  ++  ++
Microbacterium b , c Microbacterium sp. WPCB025 FJ006871 10052080      ++
Mycobacterium c Mycobacterium sp. K328W DQ372732 10060100       +
Nocardia c Nocardia sp. S24 AF430052 9820595      +++
Nocardoides c Nocardioides hankookensis DS-30 EF555584 95120100       ++
Rhodococcus c Rhodococcus sp. S9 AF260713 100444555+ ++ ++++++
Smaragdicoccus a , b Smaragdicoccus niigatensis AB243007 10011000      + 
Tsukamurellaceaec,d,e (66%)Tsukamurella sp. RP-B6 FM997982 9451000      + 
Williamsia c Williamsia muralis AY986734 100275644+++++ +  
Bacteroidetes Chitinophagab,c (100%) Chitinophaga arvensicola AM237311 8952080   +  ++
Chitinophagaceaec,d (100%)Terrimonas sp. RIB1-6 FJ347757 9145050      ++
Dyadobacter c Dyadobacter fermentans CP001619 99105050 +  ++++
Flavobacterium c Flavobacterium sp. 10B AJ698832 10046496 ++ ++++ +
Pedobacter c Pedobacter sp. GR12-04 AM279216 9894456      ++
Sphingobacterium c Sphingobacterium sp. cxh-8 EF059711 10025050 ++     
Firmicutes Bacillus b , c Bacillus sp. P109 EU195956 10021000  +   + 
Verrucomicrobia Verrucomicrobium a , e Verrucomicrobium sp. GD GQ304751 10011000      + 

Sixteen OTUs represented rare phylotypes (isolates belonging to a specific OTU) with less than five representatives including nine singletons. Six singletons (all at T30) and six highly abundant OTUs were sampled exclusively after liquid enrichment. Matrix-based enrichment selected for seven OTUs that were not obtained by liquid enrichment. Furthermore, representatives of eight OTUs were enriched preferably by growth matrices (80% or more of the representatives) including the highly abundant Flavobacterium species (96%).

Applying the Jaccard similarity index, the isolate communities derived from the three different subsamples (litter, soil–litter interface, bulk soil) displayed strong temporal (P < 0.001; Fig. 2) and weak spatial changes (P < 0.1, Fig. 2) in the composition which were significantly influenced by the substrate availability (P < 0.05). The composition of isolate communities in the three subsamples converged over time with highest similarities between litter and soil samples at T8 (Fig. 4). Likewise, isolate communities of the two different soil layers were most similar at the end of the incubation (T30). Communities from litter and interface were slightly more similar to each other than respective litter/bulk soil samples at the same time point. When the communities of isolates were discriminated by time, subsample type and enrichment method in comparative analysis, they grouped according to the incubation time and with weak significance to the enrichment method (P < 0.1), but not according to sampling depth (Fig. S1). In general, the isolate communities enriched with different techniques from the same layer at the same time displayed only little similarity (Fig. S1). However, communities enriched via transfer of Teflon membranes and litter were more similar to each other relative to enrichments involving liquid subcultivation from the same samples.

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Figure 2. Nonmetric multidimensional scaling (NMDS) plot of isolate communities from the three different subsamples [litter (○), interface (∆), bulk soil (□)] based on the presence/absence of OTUs (Jaccard similarity). Sampling time points are indicated by colours (T0: open; T2: light grey; T8: dark grey; T30: black). Factors significantly determining the community structure of isolates (P < 0.05) are indicated by solid arrows (calculated with Monte Carlo permutation with 1000 permutations and final anova). Factors showing a weak correlation on community structure (P < 0.1) are indicated by dashed arrows.

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Comparative T-RFLP analysis

To tentatively judge the environmental relevance of our enrichment cultures, 16S rRNA gene T-RFLP patterns of community DNA derived from liquid enrichment (L) or the total enriched community (TEC) were compared with those from DNA extracted directly from soil and litter samples (total community, TC). Shared OTUs between samples (L/TC or TEC/TC, respectively) accounted for 15–39% depending on the sampling date, with the highest overlap of OTUs found in TC and TEC after 2 weeks. Though not extensively, higher overlaps of TC and TEC compared with TC and L were observed continuously. TC fingerprints did not reflect the steady increase in richness as observed for the isolates (Fig. 3).

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Figure 3. Number of OTUs (T-RFs) detected in 16S rRNA gene T-RFLP analyses unique to the TC obtained after direct DNA extraction (shaded bars), unique to the enrichment community [of liquid enrichment (L) or the combination of liquid- and matrix-based methods, TEC open bars] or shared OTUs detected in TC and enrichment communities (black bars) over time (as indicated in the graph).

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Semi-log rank abundance curves were calculated, ranking the OTUs by their relative abundance in descending order, to allow comparing the evenness in the samples. The abundance curves revealed only few dominant OTUs (above the 3%-line which is threefold mean relative abundance of all T-RFs, Fig. 4) and a large fraction of rare OTUs in all samples (below the 0.1%-line). Comparable numbers of high abundant OTUs were calculated for TC and the enrichment cultures (4–10%), while a greater fraction of rare OTUs was detected in enrichment samples (21%/35%/46% for TC/L/TEC, respectively). This was also reflected by the evenness index where a value close to 1 indicates an equal distribution of OTUs and which was higher in TC than in enrichment cultures (Fig. 4). The rare OTUs detected in the TC-sample were mainly not identical to those in the enrichment samples as indicated by low Jaccard similarities (L/TC = 0.1, TEC/TC = 0.1, L/TEC = 0.6, respectively).

image

Figure 4. Semi-log rank abundance curves of OTUs (T-RFs) detected in 16S rRNA gene T-RFLP analyses of enrichment communities [liquid enrichment (open); TEC (black)] and TC obtained after direct DNA extraction (grey). The logarithmic relative abundance of an OTU on the total fluorescence intensity of the sample is shown over the fraction of OTUs (in per cent). The solid and dashed lines indicate the fraction of high (fraction on total fluorescence > 3%) and low abundant OTUs (fraction on total fluorescence < 0.1%), respectively. The evenness (inline image) was calculated with the diversity index application in past (Hammer et al., 2001).

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Response of the cultivable alkane-degrading community to litter treatment

The current cultivation experiments revealed spatio-temporal community changes of cultivable alkane degraders as well as increases in cell number and richness in response to litter amendment to soil microcosms. The results reflect formerly observed dynamics of the alkB gene pool. The gene was targeted as a proxy to evaluate the litter influence on the alkane-degrading community in a molecular-based study of the same samples (Schulz et al., 2012). Here, significantly increased cell numbers of n-hexadecane degraders may indicate a fast response to the substrate availability in close vicinity (i.e. litter and interface soil layer) to the alkane emitter (i.e. litter). With increasing depth (i.e. bulk soil), this response was delayed, which is also generally reflected by the reduction in measurable alkanes. Constantly, low amounts of alkanes in the litter and interface layer throughout the experiment were likely due to the immediate consumption of the alkanes after the release. In the bulk soil, higher amounts of alkanes were detectable, after an initial reduction, also pointing to a retarded response.

To evaluate responses in the community structure, selective enrichment with n-hexadecane as sole carbon source in liquid medium and on Teflon membranes over several months resulted in the isolation of phylogenetically diverse alkane-degrading bacteria. Ubiquitous alkane degraders (i.e. Flavobacteria, Rhodococcus or Pseudomonas species) were isolated independent of sampling depth or time throughout the experiment. In contrast, other phylotypes showed spatio-temporal dynamics and were possibly responsible for the previously observed shifts in alkB fingerprints (Schulz et al., 2012). These organisms were cultivable in different incubation phases (influenced by changing substrate availability) or from different subsamples (litter or soil) and might represent alkane degraders occupying more narrow niches than ubiquitously isolated bacteria. However, the strong spatial differences in the community composition of alkB gene pools (P < 0.001), one major finding of the molecular-based study, were not equally observed on the phylogenetic level of the isolates. This may result from either presence of multiple gene paralogs in one bacterium or alkane degraders not captured by the cultivation, but which were relevant for the differences in the alkB community patterns. The use of n-hexadecane as model substrate may have underestimated the cell- and OTU-number. For instance, alkane degraders growing exclusively on short- (< C10) or long-chain alkanes (> C20, as often predominantly in plants) might have been missed. However, many alkane degraders are capable of degrading various alkanes by possessing many different enzymes which cover a large spectrum of alkanes with different chain length (Van Beilen & Funhoff, 2007; Coleman et al., 2011; Liu et al., 2011). Thus, most degraders make use of a wide substrate spectrum.

Besides the highly abundant ubiquitous alkane degraders, further 27 isolates were affiliated to bacteria known to degrade alkanes. Interestingly, ten OTU (Acetobacteraceae, Aminobacter, Bosea, Devosia, Aquabacterium, Ideonella/Leptothrix-related Burkholderia incertae sedis, Mitsuaria, Thermomonas, Smaragdicoccus and Verrucomicrobium) were related to genera for which alkane degradation was not yet or only putatively reported. This suggests that the respective function is phylogenetically far more widespread than formerly observed. It has to be noted that selective culturing also bears uncertainties as false-positive isolates might belong to versatile oligotrophs, which grow on minimal amounts of alternative substrates present in the cultivation media. Therefore, counter checking by nonsubstrate controls, culturing in liquid minimal medium and subsequent substrate utilisation studies may give ultimate evidence for the respective function. However, the current hypothesis of a phylogenetically widespread alkane degradation capacity is supported by previous studies which reported on the genetic mobility of the alkB gene (Van Beilen et al., 2001; Martins dos Santos et al., 2008). Furthermore, the overall bacterial tool box for alkane degradation comprises many different enzyme systems which were also ubiquitously detected (reviewed in Rojo, 2009). With a broader ecological view, a spread of the alkane degradation function, for example by horizontal gene transfer, might be favoured where phylogenetically diverse bacteria come into contact given that there is a competitive advantage from acquiring the function (i.e. when the substrate is available); prerequisites that are fulfilled at the soil–litter interface, but formerly received little consideration.

Combining enrichment methods yields a more diverse isolate community

The combination of liquid subcultivation and transfer of hydrophobic growth matrices yielded a higher phylogenetic richness of alkane-degrading bacteria as compared with each individual method. The two different modes for substrate supplementation mimicked different micro habitats thus selecting planktonic and sessile alkane degraders (Bastiaens et al., 2000). The growth matrices sampled 28 alkane-degrading phylotypes of which 15 OTUs were preferentially or exclusively enriched on matrices. Conventional liquid-based subcultivation might counterselect against bacteria best growing in association with surfaces and hence possibly miss specialists of high relevance in contaminated environments. As a matter of fact, maize litter communities enriched on plant material and on Teflon membranes were more similarly composed compared with litter communities enriched with liquid subcultivation (indicated by Jaccard indices). Teflon membranes apparently mimicked the adhesion matrix of the hydrophobic plant surfaces with comparable substrate provision properties and hence might better reflect the litter-sessile cultivable community exhibiting a surface-associated life style.

In total, the combination of different enrichment strategies selected for forty different phylotypes including five OTUs that were only distantly related to known cultured relatives (sequence similarity < 95% according to RDP classifier). The detection of novel phylotypes is not surprising when analysing soil bacteria as many studies reported on the enormous bacterial diversity in this habitat (Curtis & Sloan, 2005; Roesch et al., 2007). However, most information on soils resulted from molecular high-throughput screenings, and many bacteria were believed to belong to the ‘uncultivable majority’ (Russel, 1923). Advanced isolation strategies might capture many yet unisolated phylotypes supporting the recommended precautious application of the term ‘uncultivable’ (Eilers et al., 2000). Moreover, our results confirm that the ‘uncultivable majority’ might be more related to an inability to provide proper cultivation conditions rather than a general inability of bacteria to grow in the laboratory as also proposed earlier (Stewart, 2012). Thus, many as yet unisolated bacteria might be cultured in future when we would succeed in a delicate imitation of their natural environment as indicated by our results and former findings (Kaeberlein, 2002; Zengler et al., 2002; Stevenson et al., 2004).

It remains unclear if many of our isolated alkane degraders were key players in the bacterial community of uncontaminated soil and would thus probably be assigned as ‘readily cultivable, but not environmentally relevant’ (Ritz, 2007) in status quo-snapshots of the community composition. Moreover, the proportion of alkane degraders of the number of total cultivable bacteria was generally << 0.1%. The isolation of bacteria under a certain selection pressure (here, the alkane degradation capacity) will not reflect the actual community composition, and bacteria lacking the respective function or requiring specific growth conditions were not cultivated. For instance, some oligotrophic bacteria (i.e. Acidobacteria) were reported to be underrepresented in culture-dependent collections despite their prevalence in communities observed by culture-independent methods. In contrast, many copiotrophs (e.g. Betaproteobacteria or Bacterioidetes) often showed converse phenomena (Fierer & Jackson, 2006; Shade et al., 2012). As a matter of fact, in contrast to Betaproteobacteria and Bacteroidetes, no Acidobacteria-related isolates were obtained from our samples with the applied enrichment methods. In the current study, the presence of a high number of unique T-RFs in the enrichment communities (TEC) not detectable in profiles of litter or soil DNA extracts (TC) and the greater fraction of low abundant T-RFs in semi-log rank abundance curves of TEC indicates the enrichment of phylotypes with low abundances in the original community. The detection of these bacteria was coupled to selective enrichment and their presence might be easily overlooked in a study using exclusively cultivation-independent screening. Former studies also reported on the cultivation of low abundant bacteria whose sequences were not detected with elaborate culture-independent methods. The discovery of these bacteria pointed to the existence of a ‘rare biosphere’ likely escaping molecular screening (Zengler et al., 2002; Shade et al., 2012). The rare phylotypes (i.e. rare biosphere) in the current study might constitute a seed bank for the alkane degradation function in uncontaminated soil (Sogin et al., 2006; Epstein, 2009; Shade et al., 2012). Their life strategy might be similar to that of the hydrocarbonoclastic bacteria (Bogan et al., 2003; Rojo, 2009), which have high environmental relevance in case of contamination. Moreover, the rare phylotypes might constitute precious specialists for alkane degradation in soil to which attention should be given in future studies for instance with regard to their contribution to the respective microbial ecosystem function or the detection of novel metabolic pathways.

The soil–litter interface: maintaining the soil bacterial alkane degradation

The former study of Schulz et al. (2012) and the current study observed significant effects of plant litter-derived alkanes on the bacterial alkane degradation potential in the neighbouring soil layers (i.e. the soil–litter interface). Other studies also observed a promotion of a plant cover on the hydrocarbon removal efficiency and concluded that the enhancement extended beyond the rhizosphere effect (Siciliano et al., 2003). These studies generally focussed on phytoremediation of hydrocarbon-contaminated soils. Here, we could show that plant litter alone indeed promoted the potential for alkane removal at the soil–litter interface also in uncontaminated soil. The steadily increased richness of cultivable alkane degraders at the interface over 30 weeks points to a long-lasting effect due to a low but steady substrate provision. Thus, the formation of a soil–litter interface contributes to the function maintenance in soil.

Interestingly, the total cultivable alkane-degrading communities of litter and soil samples increased in similarity over time (Fig. 2) reflecting either similar growth conditions through plant-derived substrates and nutrients or an inoculation of bacteria from the plant litter to the soil or vice versa. These communities were highly similar shortly after the litter amendment to the soil (2–8 weeks) where concomitantly increasing numbers of alkane degraders and low amounts of measurable alkanes due to extensive consumption were detected. This indicates a fast response to the litter amendment. These results and the detection of rare alkane-degrading specialists that inhabit the soil–litter interface may provide alternative strategies for optimising bioremediation approaches. For instance, by actively creating new soil–litter interfaces (i.e. subverting plant litter in the soil), the litter could serve as indigenous growth matrix and/or introduce specialised alkane degraders.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The study was financed by the German Research Foundation (DFG) within the priority programme 1315 (‘Biogeochemical Interfaces in Soil’). Further acknowledged is the work of Stephan Schulz for microcosm preparation and sampling as well as the technical support of Birgit Würz in alkane analysis and Thomas Mayer and Marcus Böttcher in molecular work.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
fem12097-sup-0001-SuppinfoS1.docxWord document17KData S1. Sampling.
fem12097-sup-0002-SuppinfoS2.docxWord document18KData S2. Extraction and quantification of alkanes.
fem12097-sup-0003-SuppinfoS3.docxWord document18KData S3. OTU richness estimation for ARDRA.
fem12097-sup-0004-SuppinfoS4.docxWord document18KData S4. Statistical analyses of T-RFLP data.
fem12097-sup-0005-SuppinfoS5.docxWord document21KFig. S1. Cluster analysis of enriched communities.
fem12097-sup-0006-SuppinfoS6.docxWord document43KTable S1. Richness estimators and diversity indices for OTU patterns.

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