13C pulse-labeling assessment of the community structure of active fungi in the rhizosphere of a genetically starch-modified potato (Solanum tuberosum) cultivar and its parental isoline


Author for correspondence:
Emilia Hannula
Tel: +31 317 47 35 07
Email: e.hannula@nioo.knaw.nl


  • The aim of this study was to gain understanding of the carbon flow from the roots of a genetically modified (GM) amylopectin-accumulating potato (Solanum tuberosum) cultivar and its parental isoline to the soil fungal community using stable isotope probing (SIP).
  • The microbes receiving 13C from the plant were assessed through RNA/phospholipid fatty acid analysis with stable isotope probing (PLFA-SIP) at three time-points (1, 5 and 12 d after the start of labeling). The communities of Ascomycota, Basidiomycota and Glomeromycota were analysed separately with RT-qPCR and terminal restriction fragment length polymorphism (T-RFLP).
  • Ascomycetes and glomeromycetes received carbon from the plant as early as 1 and 5 d after labeling, while basidiomycetes were slower in accumulating the labeled carbon. The rate of carbon allocation in the GM variety differed from that in its parental variety, thereby affecting soil fungal communities.
  • We conclude that both saprotrophic and mycorrhizal fungi rapidly metabolize organic substrates flowing from the root into the rhizosphere, that there are large differences in utilization of root-derived compounds at a lower phylogenetic level within investigated fungal phyla, and that active communities in the rhizosphere differ between the GM plant and its parental cultivar through effects of differential carbon flow from the plant.


It has been estimated that 20–50% of the carbon obtained by plants via photosynthetic assimilation is transferred to the roots and about half of this is further released into the soil (Kuzyakov & Domanski, 2000). This release of exudates strongly affects the soil microbial composition and activity close to the roots, giving rise to the so-called rhizosphere effect (Lynch & Whipps, 1990; Jones et al., 2009). Although the rhizosphere effect has mostly been studied in bacteria, an increasing number of studies indicate the importance of fungi in metabolizing root-derived organic compounds (Buée et al., 2009). In a previous study, we described the community dynamics of saprotrophic fungi in the rhizosphere of potato (Solanum tuberosum) cultivars in intensively managed agricultural soils (Hannula et al., 2010). Contrary to the expected low abundance and activity of saprotrophic fungi in intensively managed soils (Van der Wal et al., 2006), we found that fungi made up a significant part of the rhizosphere microbial biomass, especially during the flowering and senescent stages.

Many approaches have been used to monitor the response of rhizosphere microbial communities to root exudates (Kuzyakov & Domanski, 2000). One method that has proved to be very useful is the application of different carbon isotopes in tracking 13C in cellular components (e.g. lipids and nucleic acids) to determine which functional groups actively assimilate 13C-labeled substrates (Boschker et al., 1998; Radajewski et al., 2000; Manefield et al., 2002).

Use of phospholipid fatty acid analysis in combination with stable isotope probing (PLFA-SIP) has indicated that fungi are a very important group of organotrophic organisms in the rhizosphere and even inside roots receiving considerable amounts of plant-derived carbon (Butler et al., 2003; Lu et al., 2004; Wu et al., 2009; Gschwendtner et al., 2011). In addition, fungi are known to respond rapidly to the addition of easily degradable substrates such as root exudates (Broeckling et al., 2008; De Graaff et al., 2010). Unfortunately, the use of PLFA-SIP does not provide information on the identity of the active fungi. It is known that the diversity of fungi in soils is enormous and their functions range from obligate mutualists (Glomeromycota) to saprobes and pathogens (Ascomycota and Basidiomycota), all of which are very important in the rhizosphere (Carlile et al., 2001; Buée et al., 2009). All three fungal phyla are influenced by the plant in one way or another, but the relationships between plants and individual fungal taxa or even species are not known (Christensen, 1989; Broeckling et al., 2008; Buée et al., 2009).

The high variation in rhizodeposition patterns among plant species suggests that genetic modification in plants, especially if the modification targets carbon-related compounds, could result in a change in carbon allocation patterns and thus may give rise to shifts in the abundance of fungal species. It has been reported that carbon allocation within plants is strongly regulated by genotype and stage of development. Several studies (Milling et al., 2004; Götz et al., 2006; Griffiths et al., 2007; O’Callaghan et al., 2008; Weinert et al., 2009; Hannula et al., 2010) have provided information on the effects of transgenic crops on soil bacterial and fungal communities, but only a few have addressed the question from the carbon-partitioning perspective (Wu et al., 2009; Gschwendtner et al., 2011).

The aim of this study was to identify and compare fungal communities actively assimilating root exudates of the genetically modified (GM) potato (Solanum tuberosum) cultivar ‘Modena’, which has modified starch metabolism, and its parental variety ‘Karnico’, cultivated in the same soil, by applying both RNA-SIP and PLFA-SIP to the 13C-labeled plants. As this particular modification targets a biosynthetic pathway, it was hypothesized that this could also result in changes in the composition of rhizodeposition and of rhizosphere microbial communities. The main focus of the study was to improve our understanding of the relationship between plants and different fungal phyla, namely Ascomycota, Basidiomycota and Glomeromycota, in the rhizosphere and to assess how the GM trait would affect these relationships.

Materials and Methods

Glasshouse experiment and 13C labeling

A glasshouse experiment was performed to compare a GM potato (Solanum tuberosum L.) line (‘Modena’) with altered starch quality used for industrial purposes with its parental isoline (‘Karnico’). The altered starch composition was created by complete inhibition of the production of amylose via introduction of an RNAi construct of the granule-bound starch synthase gene inhibiting amylose formation, which yields pure amylopectin (de Vetten et al., 2003). The soil used for the experiments was collected from a Dutch agricultural field (field VMD in Hannula et al., 2010) after the growing season of 2009. The soil was a sandy peat soil with the following characteristics: silt fraction 2.8%, sand fraction 94.3%, organic matter content 25 g 100 g−1 dry soil, and pH 5.0. The soil was homogenized and sieved (< 2 mm) and transferred to pots (volume 10 l). One tuber of either cultivar was planted per pot and the plants were grown in the glasshouse until they reached the phenological stage of senescence (EC90) (Hack et al., 2001). This stage was selected because in an earlier field experiment it was shown that at this stage the highest abundance of fungal biomass in the rhizosphere occurred and the differences between the modified cultivar and its parental variety were most pronounced (Hannula et al., 2010). The day:night period was set at 16 h : 8 h and the maximum daily temperature was c. 22°C. Triplicate pots with soil but without plants (bulk soil) were incubated under the same conditions and used as controls to assess the possible accumulation of labeled carbon by fungi without the presence of a plant.

Twelve plants of each cultivar and two bulk soil pots were labeled with 99.99 atom-%13CO2 (Cambridge Isotope Laboratories, Andover, MA, USA) in an artificially lit airtight growth chamber for a total of 30 h. The same number of plants were placed in a similar chamber and kept under identical conditions but with a 12CO2 atmosphere, representing the control treatment. The CO2 concentrations in the chambers were monitored throughout the experiment. Before the start of the labeling, the plants were allowed to assimilate carbon until the CO2 concentration fell to 200 μl l−1. During this period, the photosynthetic rate was determined. When the CO2 concentration of 200 μl l−1 was reached, 13CO2 was injected into the chamber using a gas-tight pumping system until the CO2 concentration reached 380 ppm. During the labeling period, additional 13CO2 was injected when the concentration fell below 350 ppm. The plants were labeled during two intervals of 12 h in the light, interrupted by 6 h of nonlabeling in the darkness, during which no 13CO2 was added and excess CO2 was removed. Thus, in total, the plants were labeled for 24 h in the light. The total amount of 13CO2 added to the chamber was 25 l.


After the labeling period, all the pots were removed from the chambers and the rhizosphere soil of three replicate plants per cultivar was immediately harvested from both the 13CO2 and 12CO2 treatments. The rhizosphere soil was collected by brushing the roots and immediately frozen in liquid nitrogen and kept at −80°C until nucleic acid extractions. Bulk soil samples (soil not adhering to roots) were also taken and treated similarly. Part of the soil samples (both rhizosphere and bulk soil) was kept separate, frozen and freeze-dried for use in the lipid fatty acid analyses.

Shoot, leaves, roots and tubers were collected and weighed, and tuber production was estimated. Representative samples of plant parts were frozen, freeze-dried and kept at −80°C until further analyses were performed.

The same harvesting procedure was repeated 5 and 12 d after the end of the labeling period to monitor the carbon flow. These sampling dates were selected based on the findings of previous studies (Drigo et al., 2010).

13C content in different parts of the plant

Freeze-dried plant parts were ground to a mesh size of 0.1 μm. The δ13C value of these samples was analyzed using an elemental analyzer coupled to an isotope ratio mass spectrometer (Thermo Finnigan, Bremen, Germany) to determine the amount of photosynthates allocated to above- and below-ground parts.

The incorporation of 13C into plants was expressed as the increase in the δ13C value relative to the δ13C values of unlabeled control plants (Δδ13C values). Isotope ratios and atom% of 13C were calculated using the equations described previously (Werner & Brand, 2001). Vienna PeeDee Belamnite (V-PDB) was used as reference material.

PLFAs of the soil

PLFAs were extracted, and concentrations and δ13C values were measured on a Finnigan Delta-S gas chromatograph–isotope ratio monitoring mass spectrometer (GC-IRMS) as described in Boschker (2004). The internal standard methyl nonadecanoate fatty acid (19:0) was used for calculating concentrations. The following fatty acids were used as biomarkers for bacterial biomass: i14:0, i15:0, a15:0, i16:0, 16:1ω7t, 17:1ω7, a17:1ω7, i17:0, cy17:0, 18:1ω7c and cy19:0 (Mauclaire et al., 2003). PLFA 10Me16:0 was used as a specific indicator for actinomycetes (Frostegård et al., 1993). PLFA 18:2ω6.9 was considered an indicator for fungal biomass (Bååth, 2003; Bååth & Anderson, 2003). Unfortunately, the neutral lipid (NLFA) extractions were not successful and we could not relate the NLFA marker to the PLFA marker. Thus, the PLFA 16:1ω5, which is found mainly in arbuscular mycorrhizal fungi (AMF) and which often correlates to the corresponding NLFA, was used as an indicator of AMF (Olsson et al., 1995; Drigo et al., 2010). PLFA 20:4ω6 was used to assess the amount of 13C incorporated into protozoan biomass (Mauclaire et al., 2003). The percentage of 13C allocated to a certain PLFA was calculated from the amount of 13C in each PLFA and total 13C accumulation (excess 13C pmol g−1) in all PLFAs used as biomarkers for different microbial groups, and these values were used in data analyses.

RNA extraction and gradient fractionation

Total nucleic acids were co-extracted from 400 mg of frozen rhizosphere and bulk soils following the protocol given by Griffiths et al. (2000). RNA was retrieved by treating the total nucleic acids with DNAse (Turbo DNAse; Ambion Life Technologies, Carlsbad, CA, USA), inspected for its integrity using the Experion RNA StdSens Analysis System (Experion; Bio-Rad Laboratories Inc., Hercules, CA, USA) and stored at − 80°C. Total RNA was quantified using a NanoDrop ND-1000 Spectrophotometer (Bio-Rad Laboratories Inc.). 13C-enriched RNA was separated from nonlabeled RNA by density-gradient centrifugation and analyzed as described in Manefield et al. (2002). 500 ng of RNA was used per sample and 20 fractions (of 100 μl) of the developed density gradient were collected after centrifugation. The fractionated RNA was combined into samples called ‘heavy’ (densities ≥ 1.82 g ml−1) and ‘light’ (densities ≤ 1.78 g ml−1) based on the presence of nucleic acids (measured with NanoDrop) in desired densities, the first containing fractions with 13C-enriched RNA and later fractions containing unlabeled 12C RNA. The 12C-labeled plants were used as controls and analyzed in the same way as the 13C-labeled plants.

RT-qPCR and terminal restriction fragment length polymorphism (T-RFLP)

The ‘light’ and ‘heavy’ fractions were separately reverse-transcribed using random hexamers (0.2 μg μl−1) according to the manufacturer’s protocol (RevertAid First Strand cDNA Synthesis Kit; Fermentas, Burlington, Ontario, Canada). The cDNA produced was further used to quantify the internal transcribed spacer (ITS) region of basidiomycetes and ascomycetes by real-time PCR using ABsolute QPCR SYBR green mix (AbGene, Epsom, UK) on a Rotor-Gene 3000 (Corbett Research, Sydney, Australia) with primers presented in Table 1. All samples were analyzed in at least two different runs to confirm the reproducibility of the quantification. Standard curves were prepared from ITS DNA isolated from purified plasmids and exhibited a linear relationship between the log of the ITS copy number and the calculated threshold (Ct) value (R2 > 0.98). The plasmid DNAs were run as triplicates per dilution in each run and further used to calculate the number of ITS copies in the samples.

Table 1.   Primers, PCR conditions and enzymes used for restriction analyses
TargetPrimersPCR conditionsRestriction enzymes used for T-RFLPReference
AscomycotaITS1F: CTT GGT CAT TTA GAG GAA GTA A95°C 5 min, 35 cycles of (95°C 15 s, 62°C 30 s, 72°C 90 s),HaeIII, HinfI Gardes & Bruns (1993)
ITS4a: TCC TCC GCT TAT TGA TAT GC72°C for 10 min Larena et al. (1999)
BasidiomycotaITS1F: CTT GGT CAT TTA GAG GAA GTA A95°C 5 min, 35 cycles of (95°C 15 s, 55°C 30 s, 72°C 90 s),HaeIII, HinfI Gardes & Bruns (1993)
Glomeromycota1st LR1: GCATATCAATAAGCGGAGGA95°C 5 min, 35 cycles of (95°C 30 s, 58°C 30 s, 72°C 70 s),AluI, MboI Gollotte et al. (2004)
2nd FLR3: GTT GAA AGG GAA ACG RTT RAA G95°C 5 min, 27 cycles of (95°C 30 s, 56°C 40 s, 72°C 60 s),

T-RFLP was used as a fingerprinting method to assess the diversity and community composition of Ascomycota, Basidiomycota and Glomeromycota (AMF) also from the same cDNA. T-RFLP was performed using primers and conditions presented in Table 1 and restriction was carried out according to Hannula et al. (2010).

In order to identify specific operational taxonomic units (OTUs) which cause the differences between the samples, clone libraries were created for all three fungal groups. PCR products of ‘heavy’ and ‘light’ fragments were purified with the Qiaqen PCR purification kit (Qiagen, Valencia, CA, USA) and pooled per treatment after purification. The pooled fragments were cloned into Escherichia coli JM109 using the pGem-T Easy System II cloning kit (Promega, Southampton, UK) with a vector:insert ratio of 3 : 1. Approximately 50 successful transformants per time and fragment, that is, ‘heavy’ and ‘light’, were selected for amplification, restriction digestion and identification with labeled primers, as described in Table 1. The clones producing unique fragments with both restriction enzymes were amplified using vector-based M13 primers and sequenced. Selected plasmids were isolated using a plasmid mini kit (Qiaqen) according to manufacturer’s instructions and further used for qPCR analyses.

Data analyses

Data on 13C enrichment in plant parts, PLFA data, diversity and richness of fungi and copy numbers of ascomycetes and basidiomycetes were analyzed using univariate regression within the general linear mode (GLM) procedure in statistical program past (Hammer et al., 2001). The assumption of normality was tested with Shapiro–Wilk statistics and homogeneity of variances was assessed with Levene’s test. Differences between time-points and cultivars were tested for significance with Tukey’s HSD test, or, when variances were unequal, with Tamhane’s T2 test. All the statistical analyses were performed on the original nontransformed values.

The quality of T-RFLP data was first visually inspected in GeneMapper software v4.1 (Applied Biosystems, Carlsbad, CA, USA) and then transferred to T-Rex (Culman et al., 2009). True peaks were identified as those in which the height exceeded the standard deviation (assuming zero mean) computed over all peaks and multiplied by two (Abdo et al., 2006).

Although the number of Terminal restriction fragments (TRFs) obtained with different restriction enzymes and labels were correlated (Spearman two-tailed < 0.01), the lowest value of the four restriction enzyme–primer combinations was used for further analyses to exclude false positives, and diversity was calculated from that. Moreover, any peak occurring only once (not found in replicates or a different fraction) was deleted from further analyses. Nonmetric multidimensional scaling (NMDS) with Jaccard as a distance measure was used to assess the similarity of the fungal communities in the different fractions and between the cultivars. The effect of the treatments was tested using one- or two-way Analyses of similarity (ANOSIM) with Jaccard as a distance measure. Only presence–absence data were used.

The assignment of peaks (TRFs) to OTUs was performed in the statistical computing environment R using the T-RFLP Analyses Matching Program (tramp-r) (Fitzjohn & Dickie, 2007). Three out of four of the enzyme–primer combinations within a 1.5-bp margin had to be found in a sample for it to be assigned to an OTU. The diversities of OTUs, assigned to classes and orders, and the TRF data were compared with the Shannon–Weaver H’ diversity index and a diversity t-test was used to compare diversities. All statistical analyses were performed in the statistical program past (Hammer et al., 2001).

The PLFA 13C-labeling data were evaluated with principal component analyses (PCAs) and multivariate analysis of variance (MANOVA) was used to determine the overall effects of time and cultivar on mole percentages and δ13C values of PLFAs compared with the controls.


13C enrichment in potato plants and rhizosphere microbes

During the incubation in a 13CO2 atmosphere, a steady consumption of CO2 was measured by the automatic monitoring system which coincided with a detectable amount of 13C in the plant parts and in the rhizosphere microbes (Fig. 1). The 13C values in the control plants were in the normal range (on average δ13C –28%). The amount of labeled carbon in the roots was highest at the first sampling (Fig. 1). This indicates a rapid flux of labeled carbon into the rhizosphere in the very early stages of the experiment. After the first sampling time, the amount of labeled carbon became diluted by ongoing photosynthesis and 12 d after labeling only 35% (significantly less after 12 d than immediately after labeling; = 4.24, < 0.05) of carbon (16% in leaves and 37% in roots) was left in the plant tissues. At the last sampling time-point (12 d after labeling) most of the carbon allocated below-ground was detected in the potatoes and this amount was significantly (= 7.37, < 0.05) higher after 12 d than immediately after labeling. After 5 d of labeling, there was a difference between cultivars, but the 13C data for ‘Karnico’ did not fit into the pattern of other harvests and might thus not be reliable (data not shown).

Figure 1.

Distribution of 13C in potato (Solanum tuberosum) plants and rhizosphere microbes. The 13C content in different parts of labeled potato plants is expressed as excess compared with nonlabeled control plants harvested at the same time and separated into above-ground parts (leaves and stem combined) and below-ground parts (roots and potatoes). The first columns represent the genetically modified (GM) variety ‘Modena’ (M) and the second columns its parental isoline ‘Karnico’ (K). The natural isotopic signatures of the control plants were the same for both cultivars (average δ13C −28%).

Directly after labeling, the 13C content of the GM cultivar and its parental cultivar did not differ significantly, either in their total plant biomass or for any of the plant parts. Analysis of 13C enrichment in PLFAs in the rhizosphere showed that most of the label accumulated in 18:2ω6.9, which is commonly used as a fungal biomarker (Fig. 2). Total 13C in below-ground parts of the plant was positively correlated with the amount of label in the AMF marker 16:1ω5 (= 0.64, < 0.001) and the amount of label in the fungal marker 18:2ω6.9 was positively correlated with the amount of label in root samples (= 0.68, < 0.001) and in the 16:1ω5 marker (= 0.70, < 0.001). Further, the amounts of labeled carbon in PLFA markers 18:2ω6.9 and 18:1ω9 positively correlated (= 0.98, < 0.005) with each other but not with those in any other markers (Fig. 3b). No excess 13C was detected in the PLFAs from plants treated with 12C or in the pots with only bulk soil subjected to 13C labeling.

Figure 2.

The amount of excess 13C in different microbial groups as measured by phospholipid fatty acid (PLFA) analyses. The incorporation of 13C into the markers was calculated for (a) fungi, (b) bacteria, (c) arbuscular mycorrhizal fungi (AMF) and (d) protozoa based on markers specific to these groups mentioned in text at three time-points. Closed circles, genetically modified (GM) potato (Solanum tuberosum) variety ‘Modena’; open circles, its parental cultivar ‘Karnico’. PLFAs used as indicators for the different microbial groups are given in the Materials and Methods section. Note that all axes are different and ordered from highest to lowest.

Figure 3.

Principal component analyses (PCAs) of the labeled phospholipid fatty acid (PLFA) (excess 13C pmol g−1) patterns of the rhizospheres of both cultivars at all time-points (a) and PLFAs explaining this pattern (b). Closed symbols and solid variances, the genetically modified (GM) potato (Solanum tuberosum) variety ‘Modena’; open symbols and dotted line, parental cultivar ‘Karnico’. The variance is based on triplicates of each treatment. Black, the bacterial PLFAs explaining the patterns; red, fungal marker; green, arbuscular mycorrhizal fungi (AMF); blue, actinomycetes; gray, nonidentified. For grouping of the PLFAs, see text.

Five days after labeling, total bacterial PLFAs contained more or less the same amount of 13C as fungal PLFAs. At the last sampling point (12 d), fungal PLFAs again contained more 13C than bacterial PLFAs in the rhizosphere of ‘Modena’ but not in that of ‘Karnico’ (Fig. 2). The total enrichment of 13C at the first sampling was higher in rhizosphere PLFAs of cultivar ‘Karnico’ than in those of ‘Modena’ (Fig. 2). However, this difference appeared to be caused by a higher accumulation of 13C in fungal PLFAs (= 7.098, = 0.04) but not for any other group. In the rhizospheres of both cultivars, the amounts of 13C in bacterial PLFAs were similar for the first two sampling periods but increased 12 d after labeling (Fig. 2). The heaviest labeling of bacterial PLFAs was observed for two Gram-negative markers (16:1ω7t and 18:1ω7c) (data not shown). Protozoan and actinomycetal PLFAs had the highest labeling at later stages (data not shown). There were no differences in the 13C in protozoan or actinomycete PLFA markers in the rhizosphere soil of ‘Karnico’ compared with ‘Modena’.

Similarly, the PCA of labeled PLFAs of rhizosphere microbes revealed a difference between growth stages and at the first time-point also between the GM and parental varieties (Fig. 3). Based on MANOVA of the eigenvalues, there were no significant temporal effects on the overall PLFA labeling profiles for both cultivars (Wilks’ lambda = 0.629, > 0.05), and there were no overall differences between the cultivars (Wilks’ lambda = 0.93, > 0.05). The only significant effect of cultivar on PLFAs was directly after labeling (Wilks’ lambda = 0.053, < 0.05) which could be explained by different labeling of the fungal-specific marker (18:2ω6,9) and AMF marker (16:1ω5) (Fig. 3b).

Ascomycota and Basidiomycota receiving carbon from the plant

The total number of ITS copies in the 13C-enriched RNA fractions was positively correlated with the labeling of PLFA 18:2ω6.9 (= 0.82, < 0.05). The number of ITS copy numbers in 13C-enriched RNA fractions extracted from the rhizosphere 1 d after labeling was ten times higher for Ascomycota than for Basidiomycota. Furthermore, for ‘Modena’, copy numbers of Ascomycota and Basidiomycota showed opposite temporal patterns (Fig. 4). There were no significant differences in total (‘heavy’ and ‘light’ fractions combined) numbers of fungal ITS copies between measuring times or cultivars (data not shown). Furthermore, there were no differences in the total ITS copy numbers between the 13C-labeled and control plants and no ITS copies were detected in the ‘heavy’ fraction of control plants, thus confirming that the 13C enrichment of fungi was real.

Figure 4.

Internal transcribed spacer (ITS) copy numbers of (a) Ascomycota and (b) Basidiomycota in the heavy fraction at different time-points after labeling. Dark gray bars, genetically modified (GM) potato (Solanum tuberosum) cv ‘Modena’; light gray bars, its parental cultivar ‘Karnico’. Letters above bars indicate significant differences at the level of < 0.05. Note that the axes of (a) and (b) are not the same.

There were no significant differences between cultivars in 13C-enriched ascomycetal ITS copy numbers at any time-point (Fig. 4a). The decrease in the labeled ITS copy numbers of ascomycetes with a prolonged sampling time correlated with the amount of labeled carbon in the roots (= 0.77, < 0.05). The percentage of total ascomycete copies in the ‘heavy’ fraction was 70% and 81% immediately after labeling, 56% and 49% after 5 d and 28% and 27% after 12 d for ‘Modena’ and ‘Karnico, respectively. The 13C-enriched ITS copy numbers of basidiomycetes also did not reveal significant differences between cultivars for the first two sampling time-points or if all time-points were combined (Fig. 4b). There was, however, a difference at the last time-point (12 d) when the GM cultivar ‘Modena’ had more labeled basidiomycetal ITS copy numbers in its rhizosphere than ‘Karnico’ (= 18.7, < 0.05). The percentage of 13C-enriched copies of basidiomycetes compared with the total number of copies ranged from 35% to 51%.

Diversity and community structure of Ascomycota, Basidiomycota and Glomeromycota active in the rhizosphere

The number of fungal OTUs in the heavy RNA fraction ranged from 49 to 74 and Shannon H’ diversity ranged from 3.75 to 4.12 (Fig. 5a, Table 2). There were no significant differences between the cultivars, although the diversity in the 13C fraction was lower in the rhizosphere of ‘Modena’ 12 d after labeling compared with ‘Karnico’ (= 1.68, = 0.09). This was mainly a result of a decrease in the diversity of Basidiomycota and Glomeromycota. The diversity of identified OTUs at all levels corresponded well to the diversity of TRFs. Significant differences in fungal community structure between the cultivars were also detected after 12 d but not at the earlier sampling dates (Fig. 5b). Sampling time was a strong factor affecting the fungal community structure in the heavy fraction (ANOSIM: = 0.977, < 0.001) (Fig. 5b). All time points were significantly different from each other (> 0.92 and < 0.005).

Figure 5.

Diversity (a) and community structure (b) of all active (labeled RNA pool) fungal groups combined. (a) Black bars represent average diversity (= 3) (± SD) of fungi in the rhizosphere of potato (Solanum tuberosum) ‘Modena’, and gray bars average diversity (± SD) in the rhizosphere of ‘Karnico’ at three different time-points after 13CO2 pulse-labeling. Letters above bars indicate significant differences in diversity (diversity t-test) at the level of < 0.05. (b) In the nonmetric multidimensional scaling (NMDS) plot: open symbols, the parental variety; closed symbols, the genetically modified (GM) variety. Circles around samples are distinct cultivar and time combinations.

Table 2.   Diversity of terminal restriction fragments (TRFs) and identified fungal operational taxonomic units (OTUs) (at different taxonomic levels) in the heavy RNA fraction immediately after labeling and 1, 5 and 12 d after labeling and in the light fraction (combined)
 Right after labeling5 d after labeling12 d after labelingLight Fractions
  1. The closest species match (% identity) was obtained by comparison to known species in GenBank using BlastN. The assignment into orders is based on this similarity.

TotalShannon - H’ TRFs3.86ab4.12a4.12a4.03a4.00ab3.75a4.644.65
Number of TRFs556374586049103106
Shannon - H’ OTUs3.33a3.33a3.78b3.56ab3.37a3.18a3.853.95
Number of OTUs2828443529244752
Shannon - H’ orders2.422.382.792.852.522.422.672.78
Number of orders1516222115142022
Shannon - H’ classes1.941.961.991.721.421.291.861.92
Number of classes9101110761111
AscomycotaShannon - H’ TRFs3.05ac3.37ab3.55b3.37ab2.71c2.71c3.533.65
Number of TRFs2129352915153440
Shannon - H’ OTUs2.77ab2.30a3.14a2.94b2.08c2.30c2.832.94
Number of OTUs161023198101719
Shannon - H’ orders1.981.902.192.281.611.811.952.12
Number of orders1091212681011
Shannon - H’ classes1.401.
Number of classes65563455
BasidiomycotaShannon - H’ TRFs2.83a2.83a2.77a3.09ab3.18b3.14b3.563.58
Number of TRFs1717162224233536
Shannon - H’ OTUs1.95a2.08a2.34ab2.64b2.77b2.63b2.943.00
Number of OTUs78111416141920
Shannon - H’ orders1.151.071.671.911.791.651.711.82
Number of orders44787678
Shannon - H’ classes0.670.690.850.650.450.500.710.68
Number of classes22332233
GlomeromycotaShannon - H’ TRFs2.34a2.75a2.55a1.75b2.78a1.77b3.533.40
Number of TRFs171723721113430
Shannon - H’ OTUs1.61ab2.30b2.30b0.69a1.61abn.d.2.342.57
Number of OTUs510102501113
Shannon - H’ orders0.000.640.850.000.50n.d.0.760.72
Number of orders13312033
Shannon - H’ classes0.000.640.850.000.50n.d.0.710.68
Number of classes13312033

There were no significant differences between cultivars or harvest times in the total number of ascomycetal TRFs, that is, when light and heavy fractions were combined. The diversity of ascomycetes ranged from 2.71 in the fraction labeled with 13C sampled after 12 d to 3.55 on day 5 (Fig. 6a). There were no differences in diversity between cultivars at any time-point. Directly after labeling, 20 TRF types under ‘Modena’ and 29 under ‘Karnico’ had already received labeled carbon and incorporated it into their RNA, corresponding to diversity levels of 3.05 and 3.17, respectively (Table 3). Five days later ‘Modena’ and ‘Karnico’ had 29 and 35 TRFs active in their rhizospheres, respectively, of which 11 (for ‘Modena’) and 13 (for ‘Karnico’) were the same as at day 0. The community structure of active ascomycetal OTUs was significantly different between the time-points (ANOSIM: = 0.5, < 0.001) (Fig. 6d). Although the number of active ascomycetal OTUs did not differ significantly between cultivars, the community structure did (ANOSIM, > 0.5, < 0.05) at time-points 0 and 12 d after labeling (Fig. 6d).

Figure 6.

Diversity (a–c) and community structure (d, e) of Ascomycota (a, d), Basidiomycota (b, e) and Glomeromycota (c, f) in the 13C-enriched fraction of RNA extracted from potato (Solanum tuberosum) rhizosphere soil. Bars represent average diversity (= 3) (± SD) of (a) ascomycetes, (b) basidiomycetes and (c) glomeromycetes in the rhizosphere of ‘Modena’ (black bars) and ‘Karnico’ (gray bars) at three measuring points. Letters above bars indicate significant differences at the level of < 0.05. In the nonmetric multidimensional scaling (NMDS) plots of (d) ascomycetes, (e) basidiomycetes and (f) glomeromycetes different symbols represent different time-points and colors different cultivars. Circles around samples represent distinct cultivar and time combinations.

Table 3.   Distribution of identified OTUs in the rhizosphere of the genetically modified (GM) variety and its parental isoline at different time-points (immediately after labeling and 5 and 12 d after labeling) in the heavy fraction and the commonly occurring (all time-points) OTUs in the light fraction, indicated as presence-absence
PhylaNameOrderClosest species (% identity)Right after labeling5 d after labeling12 d after labelingLight Fractions
  1. Total fungi are calculated by combining the three phyla. The letters behind numbers in level of TRFs and OTUs indicate significance at < 0.05. The OTUs are assigned to orders as presented in Table 3.

  2. The classes investigated were (orders included): Deuteromycota (unassigned), Dothideomycetes (Capnodiales and Pleosporales), Eurotiales (Eurotiomycetes), Leotiomycetes (Helotiales and Thelebolales), Sordariomycetes (Chaetothyriales, Hypocreales, Magnaporthales, Microascales, Phyllacorales, Sordariales and Xylariales), Ascomycota incertae sedis, Agaricomycetes (Agaricales, Cantharellales, Corticiales, Hymenomycetales, Polyporales and Trechisporales), Mitosporic Agaricomycotina, Tremellomycetes (Tremellales), Diversiporales (Acaulosporales and Diversiporales), Glomerales (Glomerales) and Paraglomerales (Paraglomerales).

Ascomycota Cap1Capnodiales Davidiella macrospora (EU167591) (99) X X         X X
Cap2Capnodiales Cladosporium cladosporioides (AY251074) (99) X     X       X
Cap3Capnodiales Cladosporium herbarum (AF177734) (80)     X X       X
Cap4Capnodiales Zasmidium nocoxi (CQ852842) (83) X X X X     X  
Cap5Capnodiales Devriesia sp. NG_p52 (HQ115717) (100)       X   X    
Chae1Chaetothyriales Cladophialophora chaetospira (EU035405) (100)     X X X   X X
Chae2Chaetothyriales Exophiala sp. Ppf18 (GQ302685) (97)     X X X X    
Chae3ChaetothyrialesUncultured Herpotrichiellaceae (FJ554453) (98) X X            
Deu1Deuteromycota Tetracladium furcatum strain CCM F-11883 (FJ000375) (98)     X X     X  
Deu2Deuteromycota Stilbella fimetaria strain MH178 (96) X X           X
Deu3Deuteromycota Microsphaeropsis sp. MTFD09 (DQ132840) (99) X   X X   X    
Eur1Eurotiales Capronia sp. 94003b (EU129158) (81) X X X X X X    
Hel1Helotiales Botryotinia fuckeliana (EF207415) (99) X   X X X      
Hel2Helotiales Meliniomyces variabilis (EF093178) (95)     X         X
Hyp1Hypocreales Clonostachys miodochialis (AF210674) (99) X   X       X X
Hyp2Hypocreales Bionectria cf. ochroleuca (EU552110) (98) X X X   X X    
Hyp3Hypocreales Fusarium sp. 5/97–45 (AJ279478) (97)             X X
Hyp4Hypocreales Gibberella fujikuroi strain SH-f13 (HM165488) (100)     X X   X    
Hyp5Hypocreales Gibellulopsis nigrescens (HQ115693) (100)     X       X  
Hyp6Hypocreales Fusarium equiseti (GQ50572) (100)     X          
Hyp7Hypocreales Fusarium merismoides var. merismoides (EU860057) (100) X X X X X X X X
Hyp8Hypocreales Eucasphaeria capensis (EU272516) (89) X X X X X X X X
Hyp9Hypocreales Nectria sp. ASIN2 (DQ779785) (100) X X X X     X X
Hyp10Hypocreales Fusarium sp. HMA-16 (GU480953) (100) X X X X     X X
Hyp11Hypocreales Volutella ciliata (AJ301966) (98) X X     X X X X
Hyp1 2Hypocreales Fusarium sp. (96) X           X X
IS1Incertae sedis Pseudeurotium bakeri (DQ068995) (100)             X X
IS2Incertae sedis Pseudeurotium bakeri strain MCJAxII (DQ529304) (99) X X           X
IS3Incertae sedis Leptodontidium sp. (95)       X        
Ma1Mitosporic ascomycota Zalerion varium (AJ608987) (98) X           X X
Mag1Magnaporthales Phialophora sp. DF36 (EU314710) (99)     X X X X   X
Micr1Microascales Microascaceae sp. LM278 (EF060607) (98) X   X       X  
Phy1Phyllacorales Plectosphaerella sp. (96)   X X X     X X
Pleo1PleosporalesUncultured Ampelomyces clone IIP2–29 (EU516670) (98) X             X
Pleo2PleosporalesAff. Drechslera MT0008 (AB199583) (99) X   X       X X
Pleo3Pleosporales Dendryphion nanum (AY387657) (98)     X X        
Pleo4PleosporalesConiothyrium sp. 229 (FJ228186) (93) X             X
Pleo5Pleosporales Pyrenochaeta sp. ZLY-2010b (HM5955516) (90)   X           X
Sor1Sordariales Podospora miniglutinans (FJ946483) (94)     X X X X X X
Sor2Sordariales Podospora glutinans (AY615208) (96)     X X     X X
Sor3Sordariales Podospora sp. (80)     X X        
Sor4Sordariales Chaetomium sp. 15003 (EU750691) (98) X X         X  
Thel1Thelebolales Thelebolus sp. (FJ613125) (99)     X       X  
Xyl1Xylariales Sarcostroma bisetulatum (EU552155) (80)       X   X   X
Basidiomycota Aga1Agaricales Naucoria bohemica (FJ904179) (97)         X X X X
Aga2Agaricales Campanella subdendrophora (AY445118) (95)         X X X X
Aga3Agaricales Rhodocybe mundula (DQ089017) (98)         X X X X
Aga4Agaricales Camarophyllopsis schulzeri (GU187661) (94)     X X X X X X
Can1Cantharellales Rhizoctonia solani (EU730860) (97) X   X X X X X X
Can2Cantharellales Uthatobasidium fusisporum (AF518593) (96)           X X X
Can3Cantharellales Thanatephorus cucumeris (HM625913) (98)       X X   X X
Cor1Corticiales Hyphodontia hastata (DQ340311) (100)       X X X    
Cor2Corticiales Phlebia tremellosa (DQ384584) (96) X X X X X X X X
Cor3Corticiales Hyphoderma praetermissum (AY707094) (95)       X X   X X
Hym1Hymenomycetales Hymenochaetales sp. (FN907922) (100)       X X   X X
MB3Mitosporic agaricomycotina Mycotribulus mirabilis (EF589734) (97)     X X     X X
Pol1Polyporales Limonomyces roseipellis (EU622845) (96) X X X X X X X X
Trem1Tremellales Cryptococcus festucosus (FR717832) (99) X X X X X X X X
Trem2Tremellales Cryptococcus podzolicus (FN428889) (100) X X X X   X X X
Trem3Tremellales Cryptococcus sp. (92) X X X   X   X X
Trem4Tremellales Cryptococcus terreus (AB032649) (99)   X X X X X X X
Trem5Tremellales Holtermannia corniformis (GU937753) (96)             X X
Trem6Tremellales Trichosporon dulcitum strain HB940 (AJ507663) (98)   X         X X
Trem7Tremellales Cryptococcus podzolicus (FN428938) (97) X   X X X X X X
Trec1Trechisporales Trechispora farinacea (EU909231) (100)   X X X X X   X
Glomeromycota Glo1Glomerales Glomus aurantium (FN547663) (97)   X         X X
Glo2Glomerales Glomus cf. claroideum (AY639343) (96)   X           X
Glo3Glomerales Glomus mosseae (AY639156) (97)   X X   X   X X
Glo4Glomerales Glomus eburneum (AM713413) (96) X X X   X   X X
Glo5Glomerales Glomus sp. (93)   X X       X X
Glo6Glomerales Glomus versiforme (95)             X X
Glo7Glomerales Glomus caledonium (Y17653) (99) X X X       X X
Glo8Glomerales Glomus geosporum (AJ245637) (97) X X X   X   X X
Glo9Glomerales Glomus verruculosum (AJ301858) (97) X X X   X   X X
Para1Paraglomerales Paraglomus sp. (93)     X X     X X
Para2Paraglomerales Paraglomus sp. (89)   X X       X X
Acau1Acaulosporales Acaulospora sp. W4699 (FN825900) (95)     X   X   X X
Div1Diversiporales Diversispora sp. (95)   X            
Mycoromycotina Muc1Mucorales Rhizopus oryzae strain CAF276 (EU399919) (95) X   X X       X

Not only were there fewer copies of Basidiomycota than of Ascomycota, but the diversity of basidiomycetes in the 13C fraction was also lower, with c. 20 TRFs, of which about half could be identified (Table 2). The basidiomycete diversity increased with sampling time (Fig. 6b). The diversity of active basidiomycetes was not significantly different between cultivars overall or at any time-point. Further, the community structure of active basidiomycetal OTUs was significantly affected by the sampling time (ANOSIM: = 0.98, < 0.001) (Fig. 6e) and cultivar. In addition, the cultivar affected the community structure at the last two sampling points (R = 0.97, P < 0.001).

The glomeromycetes showed the clearest differences in diversity between the two cultivars: in the rhizosphere of the cultivar ‘Karnico’, the diversity of labeled AMF was higher at the last two sampling time-points (= 2.99, < 0.01 and = 3.92, < 0.001) than under the genetically modified cultivar ‘Modena’, which had the highest diversity immediately after labeling (Fig. 6c). The AMF community in the 13C fraction was less diverse in the rhizosphere of ‘Modena’ than in that of ‘Karnico’ 5 and 12 d after labeling. The community fingerprints were, however, not significantly different between the cultivars (Fig. 6f), probably because of the low amount of TRFs and thus the lack of statistical power.

Species composition of active community

The observed differences in community fingerprints and diversities can be partly explained by differences in the species identified (Table 3). A total of 72 different OTUs could be identified from the fractions. Of these, the majority (37) were ascomycetes. Differences observed in community fingerprints between ‘Karnico’ and ‘Modena’ could be explained by labeled OTUs (i.e. ‘Cap2’, ‘Hel1’ and ‘Deu3’) receiving heavy carbon already after 24 h from ‘Karnico’ and later also from ‘Modena’, and some OTUs showing the opposite (‘Phy1’ and ‘US3’). Furthermore, some OTUs in the heavy fraction were only detected under one of the cultivars. Nine OTUs were found to receive labeled carbon only in the rhizosphere of ‘Karnico’ and four OTUs were found only in the fraction with 13C in the rhizosphere of ‘Modena’ (Table 3). This might explain the observed differences in the community structure when the diversity was similar. The differences in observed OTU composition had only a minor impact at the level of orders (Table 2).

In total, 29 basidiomycetal sequences were identified from the 13C fractions. In general, directly after labeling there were mostly Cryptococcus yeasts found in the heavy fractions, while at later measuring times Agaricales, Cantharellales and Corticiales dominated the community. Three OTU types (‘Cor2’, ‘Pol2’ and ‘Trem1’) were detected in the heavy fraction at all time-points. Of these, only one OTU, ‘Pol1’, was closely related to a known plant pathogenic species, Limonomyces roseipellis (EU622845), while the others were closer to yeasts (‘Trem1’) and even to jelly rot fungi (‘Cor2’). Differences detected in diversity and community structure in the heavy fraction 5 d after labeling (Fig. 4) can be explained by delayed labeling of a few OTUs (‘Cor1’, ‘Cor3’‘Can1’ and ‘Hym1’) in the rhizosphere of ‘Karnico’. All of these OTUs were detected after 5 d in the rhizosphere of ‘Modena’ but only after 12 d in the rhizosphere of ‘Karnico’.

There were in total 13 OTUs of Glomeromycota identified to be active in the rhizosphere during this experiment (Table 3). The differences seen in diversity between ‘Modena’ and ‘Karnico’ could be explained by some Glomus OTUs (‘Glo4’, ‘Glo7’, ‘Glo8’ and ‘Glo9’, closely related to Glomus eburneum, Glomus caledonium, Glomus geosporum and Glomus verruculosum, respectively) receiving carbon from both cultivars immediately after the labeling but not from cultivar ‘Modena’ at the later stages.


13C distribution in the plants

Immediately after labeling, a substantial amount of 13C was transferred to the roots (Fig. 1). This is in accordance with earlier findings of quick allocation of carbon to the roots by grassland species (Vandenkoornhuyse et al., 2007). No significant differences were detected in the initial amounts of labeled carbon in roots between the GM cultivar and its parental cultivar. Similar observations were reported in a previous study on earlier phenological stages of potato (Gschwendtner et al., 2011).

Active microbial communities in the rhizosphere

There is evidence from stable isotope experiments that fungi are a very important group of organotrophic organisms in the rhizosphere, receiving considerable amounts of plant-derived carbon (Butler et al., 2003; Lu et al., 2004; Wu et al., 2009). Furthermore, it has been shown that fungi can respond rapidly to the addition of easily degradable substrates to soil (Broeckling et al., 2008; De Graaff et al., 2010). It was indeed confirmed by our PLFA analysis that fungi were the dominant organisms incorporating 13C from the plant immediately after labeling (Fig. 2a). There is, however, a possibility that some fast-growing bacteria could have already metabolized the carbon before the first sampling point of this study, and thus no trace of them would be left in the PLFA fingerprints. Vandenkoornhuyse et al. (2007) showed a rapid (within 5 h) incorporation of carbon into the RNA of endophytic bacteria, but studies based on PLFAs have detected slower incorporation of the carbon into lipids (Rinnan & Bååth, 2009). In our study, immediately after labeling, the major part (> 70%) of the 13C found in microbial phospholipids was found in the PLFA marker 18:2ω6.9, which is commonly used as an indicator for fungi. The use of this marker as an indicator of fungal biomass is often debated, but, as we found a highly significant positive correlation between PLFA 18:2ω6.9 and active fungal ITS copy number, we conclude that this marker is a useful indicator of fungal biomass in the rhizosphere, despite the presence of living roots (Frostegård et al., 2011). Earlier studies also showed that fungi quickly incorporate carbon from plants into their phospholipids (Lu et al., 2007; Wu et al., 2009; Drigo et al., 2010; Gschwendtner et al., 2011). Another large part (c. 9% of the total in the first sampling) of the total 13C was detected in PLFA 16:1ω5, mainly representing AMF (Olsson & Johnson, 2005; Denef et al., 2007). This is interesting, as it has been thought that, despite the importance of mycorrhiza in nutrient uptake, their importance would be minor in a high-nutrient environment such as intensively managed agricultural soils (Cesaro et al., 2008; Cheeke et al., 2011). Yet, results obtained for earlier developmental stages of potato were similar, with 6.3% of the 13C allocated to the AMF-specific PLFA marker (Gschwendtner et al., 2011).

Further, by using RNA-based techniques, we could confirm these findings, as we detected 13C incorporation in several fungal species immediately after the period of pulse-labeling (Fig. 4, table 3). We conclude that these rapidly responding fungal species in the rhizosphere are truly plant-dependent organisms. It should be noted that we did not differentiate between rhizosphere fungal species with and without access to 13C inside roots. Penetration of living roots by saprotrophic rhizosphere fungi has been reported (Harman et al., 2004). Hence, part of the allocation of 13C to saprotrophic rhizosphere fungi may be independent of rhizodepositions. In addition to the fast accumulators, we detected another group of fungi benefiting from plant-derived carbon at later time-points after labeling and probably able to use more recalcitrant compounds. Some (mostly Gram-negative) bacteria were also labeled immediately after the end of the aboveground labeling procedure, which is in accordance with the findings of earlier studies (Wu et al., 2009; Gschwendtner et al., 2011). In this study, however, the majority of bacteria received the labeled carbon later than fungi, possibly through fungal-related exudation processes (Vandenkoornhuyse et al., 2007; Drigo et al., 2010) or as a consequence of their inability to gain access to the interior of the root. The PLFA marker for protozoa (20:4ω6), which is not known to be able to use plant-derived carbon readily, revealed a delayed response to the 13C addition, possibly because they were feeding on labeled bacteria or fungi.

Active fungal communities in the rhizosphere

When root-derived products enter the soil, they are rapidly metabolized and the microbial community is likely to shift in favor of those species that are able to compete for these resources (Dennis et al., 2010). The copy number calculations revealed that mostly ascomycetes, glomeromycetes and some basidiomycetal yeasts received carbon immediately released by the plant, while later the fungal community changed in favor of (basidiomycetal and ascomycetal) species probably better adapted to a different carbon source or secondary carbon from dead plant parts or from other organisms (Lu et al., 2004; Rangel-Castro et al., 2005; Lu et al., 2007; Vandenkoornhuyse et al., 2007; Dennis et al., 2010). The carbon sources at these later stages may consist of more complex substrates, for example, sloughed-off root cells.

We could detect certain fungal orders and species that were labeled at the first sampling point but not at later stages (Table 3). These OTUs are expected to be good competitors for simple root exudates but not for more complicated carbon sources and thus active only immediately after labeling. Orders typically receiving carbon immediately from the plant were the basidiomycetal Tremellales and the ascomycetal Capnodiales while basidiomycetal orders Agaricales, Cantharellales, Sordariales, Magnaporthales and Chaetothyriales seemed to receive carbon only later. The presence of basidiomycetal yeasts in the rhizosphere that are able to use simple root-exudate compounds has been observed in earlier studies (Botha, 2011; Mestre et al., 2011).

Although we could see this pattern at the level of orders, some of the OTUs within orders had very different functions. For instance, one OTU assigned to Cantharellales (‘Can1’) received heavy carbon immediately labeling, while the other OTUs assigned to the same order received heavy carbon only 5 or 12 d later. These observed differences between individual OTUs within orders indicate differences between closely related species with respect to carbon resource utilization. The high number of OTUs closely related to known decomposer species can partly be attributed to the late phenological stage at which the labeling was performed. Although no senescent leaves were allowed to drop onto the soil, we could detect sequences from orders with many known decomposer species receiving labeled carbon, especially 5 and 12 d after labeling (Table 3), indicating that, in addition to root exudates, there might be another pathway by which the fungi receive carbon, probably via decomposing dead root material. (Dennis et al., 2010).

Effect of GM trait on active soil microbial communities

PLFA analyses showed no overall effect of cultivar (GM vs parental cultivar) on the total amount of carbon allocated to fungi. However, differences between cultivars in 13C allocation to both fungi and AMF were found at different sampling times, and this was related to the amount of carbon allocated to the roots (Fig. 2). Furthermore, differences in basidiomycete diversity and copy numbers and AMF diversity could be detected, which can be explained by the difference in the amount of carbon released from the plant and thus a difference in the speed of succession. A recent study carried out on plants with the same genetic modification (although in a different soil) using PLFA markers revealed no significant differences between the cultivar with the GM trait and its parental isoline in fungal biomass or plant exudation patterns (Gschwendtner et al., 2011). However, that study was carried out in the earlier growth stages EC30 and EC60, while our study focused on the senescent stage EC90. This can explain the differences in the results, as it has been shown that the amount of carbon allocated to the roots increases with increasing age of the plant and initiation of carbon storage structures (i.e. tubers in potato) (Timlin et al., 2006), making any differences more obvious in later growth stages. These age-dependent exudation patterns might explain the differences in the outcomes of earlier studies conducted on GM plants, as they were carried out on plants at different growth stages (Rossi et al., 2007; Wu et al., 2009; Gschwendtner et al., 2011), thus confirming the importance of considering the plant phenological state when designing experiments (van Overbeek & van Elsas, 2008; Weinert et al., 2010). Indeed, it was previously shown that differences between this GM cultivar and its parental variety in carbon allocation below-ground and microbial communities in the field could be seen at the stage of senescence (Hannula et al., 2010).

While some studies reported effects of modified crops on soil bacterial numbers (Siciliano & Germida, 1999; Dunfield & Germida, 2001), others have documented only minor or transient effects (reviewed by Kowalchuk et al., 2003). A few studies have addressed the effects of GM crops on general fungal community structures, but none has detected significant cultivar-dependent differences (Milling et al., 2004; Götz et al., 2006; Hart et al., 2009). The approach of using RNA-SIP on fungal communities as a tool to investigate the side effects of GM plants is very promising, as it allows differences between a GM cultivar and the parental variety to be detected. Previously, Rasche et al. (2009) investigated differences in shoot endophytic bacteria between two cultivars of potato using DNA-SIP and found a cultivar-related shift in bacterial communities after 4 d of labeling, very similar to the differences that we observed here for soil fungal communities. In this study, we showed that a potato cultivar modified for differential tuber starch quality and its parental isoline differed in their carbon allocation patterns, and this in turn coincided with differences in soil fungal communities. In contrast, using PLFA-SIP as an indicator of microbial communities under Bacillus thuringiensis (Bt) rice (Oryza sativa) and its parental isoline, Wu et al. (2009) did not find differences in 13C distribution in roots or the rhizosphere, indicating that observed differences might be modification-dependent.

The largest differences for the three fungal phyla were seen for the diversity of active AMF, especially at later sampling times (Fig. 6). Previously, Vandenkoornhuyse et al. (2007) observed differences in active glomeromycete communities between plant species and explained this as a consequence of competition among colonizers occupying the same ecological niche. We took this one step further and could, indeed, detect differences in active communities between the two cultivars. Previously, some studies carried out on Bt maize (Zea mays) isolines expressing Cry1Ab protein reported reduced AMF colonization (Turrini et al., 2004; Girlanda et al., 2008; Cheeke et al., 2011). In the current study, the observed differences in AMF communities in the rhizospheres of the two cultivars could be explained by the presence and absence of certain OTUs in the heavy fraction in the rhizosphere of only one cultivar, ‘Karnico’. Most of the OTUs were present, though, in the light fraction of both cultivars, indicating differences in carbon uptake abilities of the AM species.

Although we could detect these differences in the speed of carbon flow to fungal communities under glasshouse conditions between the GM crop and its parental isoline, caution in extrapolating these results to the field scale is warranted. Earlier field observations did not reveal significant differences in bacterial or fungal communities between this GM cultivar and its parental cultivar (Hannula et al., 2010; Inceoglu et al., 2010), although differences between the two were greatest at the stage of senescence, probably as a result of differences in rhizodeposition (Weinert et al., 2009). Moreover, comparing the GM cultivar only with its parental variety and neglecting intraspecific variation in carbon distribution can cause false significant results, especially when evaluating potential risks of GM crops (Hannula et al., 2010). Differences between cultivars in their carbon allocation patterns should be investigated to strengthen the results presented here.


We conclude that both saprotrophic and mycorrhizal fungi rapidly metabolize organic substrates flowing from the root into the rhizosphere and that there are large differences in utilization of root-derived compounds. Furthermore, we showed that there are differences in active fungal communities in the rhizosphere between a starch-modified GM plant and its parental isoline which are probably attributable to different compositions of rhizodeposits. The differences in carbon allocation and microbial communities assimilating carbon between the GM cultivar and its parental variety, although plausible, may not reflect long-term effects in natural systems. However, the current study was carried out especially to show that measurements of active fungal communities may enhance the sensitivity of detection of effects exerted by GM crops, which may be helpful for the evaluation of possible risks of GM crops.


The authors would like to thank Nadia Marttin, Agata Pijl and Henk Duyts for technical assistance and Peter Bruinenberg from AVEBE/AVERIS seeds for providing the tubers and soil material. This research was financed by ERGO grant number 838.06.052 of the Netherlands Organization for Scientific Research. This is publication 5211 of the Netherlands Institute of Ecology (NIOO-KNAW).