Abstract
The application of plant genetic manipulations to agriculture and forestry with the aim of alleviating insect damage through Bacillus thuringiensis transformation could lead to a significant reduction in the release of pesticides into the environment. However, many groups have come forward with very valid and important questions related to potentially adverse effects, and it is crucial to assess and better understand the impact that this technology might have on ecosystems. In this study, we analyzed rhizosphere soil samples collected from the first B. thuringiensis-transformed trees [with insertion of the CryIA(b) toxin-encoding gene] grown in Canada (Val-Cartier, QC, Canada) as part of an ecological impact assessment project. Using a robust amplified rRNA gene restriction analysis approach coupled with 16S rRNA gene sequencing, the rhizosphere-inhabiting microbial communities of white spruce (Picea glauca) genetically modified by biolistic insertion of the cryIA(b), uidA (beta-glucuronidase), and nptII genes were compared with the microbial communities associated with non-genetically modified counterparts and with trees in which only the genetic marker genes uidA and nptII have been inserted. Analysis of 1,728 rhizosphere bacterial clones (576 clones per treatment) using a Cramér-von Mises statistic analysis combined with a Monte Carlo comparison clearly indicated that there was a statistically significant difference (P < 0.05) between the microbial communities inhabiting the rhizospheres of trees carrying the cryIA(b), uidA, and nptII transgenes, trees carrying only the uidA and nptII transgenes, and control trees. Clear rhizosphere microbial community alterations due to B. thuringiensis tree genetic modification have to our knowledge never been described previously and open the door to interesting questions related to B. thuringiensis genetic transformation and also to the impact of commonly used uidA and nptII genetic marker genes.
The application of plant genetic manipulation to agriculture and forestry with the aim of increasing disease resistance or alleviating insect damage could lead to a significant reduction in the release of pesticides into the environment (27). However, considering the relatively recent development of these techniques and the lack of sufficient reliable data derived from environmental impact studies, the possible effects of transgenic DNA insertion into a plant's genome are yet to be fully evaluated. Very valid and important questions related to potentially adverse effects have been raised, and it is crucial to assess and better understand the impact that this technology might have on ecosystems.
Complex microbial communities living in close proximity to plant roots, better known as the rhizosphere, are sustained by the loss of organic matter through root exudation. It has been suggested that rhizospheres could be altered in response to plant genetic transformation (19, 28, 33). Comparative studies assessing whether there are differences between microbial communities living in the rhizospheres of genetically modified and non-genetically modified plants represent an important first step in determining if the presence of transgenic material can catalyze changes in the environment. Although a few studies have reported slight rhizosphere microbial diversity changes associated with transgenic plants compared with nontransgenic plants, thus far the differences have been either very minor, mainly attributed to environmental factors such as seasonal variation or field site (9-12, 14, 20, 21, 24, 32, 34, 42), or simply not statistically significant (13, 15, 23, 24, 29-31, 38, 40).
The forest industry represents an important economic sector in North America and in particular in Canada. Genetic improvement for wood quality and pest resistance has been used for decades, and genetically improved material has been widely deployed (44). More recently, genetic engineering has been applied to tree improvement as a shortcut to traditional breeding. The first documented genetic modification of a conifer was in 1991 (25). One of the particular interests in genetically modifying trees, among many other crops, has been Bacillus thuringiensis transformation to confer insect resistance through the insertion of cry genes. The expressed B. thuringiensis toxins demonstrate a remarkable specificity for their target host spectra, and nonconiferous plant systems expressing these toxins have had no significant effect on nontarget fauna, including plant pathogen populations (11), earthworms and nematodes (38), microbial activity (7), bacterial community structure, or total soil bacterial and fungal counts (7, 11, 38). But B. thuringiensis recombinant DNA and expressed Cry proteins were shown to be released into soils through root exudation and plant tissue decomposition, where they can remain intact and chemically active for extended periods of time (up to 240 days for the proteins) (36, 45, 48). This could be particularly important in conifers expressing a Cry toxin since release could occur over several decades.
The present study reports a survey of microbial biodiversity in an experimental plot where genetically modified white spruce (Picea glauca) and control white spruce were planted in 2000. Trees were genetically modified by biolistic insertion of a B. thuringiensis transgene [cryIA(b)] and the genetic marker genes uidA (encoding beta-glucuronidase [GUS]) and nptII [cryIA(b)/uidA/nptII trees] or only the marker genes uidA and nptII (uidA/nptII trees) (37). The purpose of insertion of the cryIA(b) transgene is expression of a delta-endotoxin which targets Lepidoptera insects, including the spruce budworm (Choristoneura fumiferana). Following ingestion and protease activation, the CryIA(b) toxin induces lesions in the epithelial walls of the insect's digestive tract, causing starvation and death (27). The trees are part of an environmental impact study to assess the impact of genetically modified trees. The objective of this study was to investigate whether changes in the microbial communities inhabiting the rhizosphere of white spruce are associated with the presence of transgenic constructs expressing the Cry protein or only markers. A robust culture-independent amplified rRNA gene restriction analysis (ARDRA) and a 16S rRNA gene sequencing-based approach were used to assess microbial composition and diversity.
MATERIALS AND METHODS
Genetic transformation of white spruce.
Transgenic white spruce trees containing either cryIA(b), uidA, and nptII or only uidA and nptII were generated at Natural Resources Canada's Laurentian Forestry Centre in Quebec, QC, Canada (A. Seguin, unpublished data). Two embryogenic lines of white spruce were initiated from mature zygotic embryos using a procedure described by Cheliak and Klimaszewska (3). Maintenance and maturation of the tissues were performed as described previously (41). The synthetic construct that was described by Cheng et al. (4) was used in the present study. In the vectors the cry1A(b) gene is under the control of the CaMV 35S promoter (pKSB) or the maize ubiquitin promoter (pKUB) and is completed by the termination signal of the nopaline synthase gene. For selection and screening of the transgenic lines, the pBI426 vector, which contains a bifunctional fusion between the uidA and nptII genes, was used (6). All plasmid DNA were isolated and purified using CsCl gradient centrifugation as previously described (2). Microprojectile bombardment was carried out using a PDS1000 helium-driven system apparatus (Bio-Rad, Mississauga, ON, Canada) as described by Charest et al. (2), except that 0.6-μm gold particles were used. For cotransformation, plasmids were mixed together before adsorption onto the gold particles, resulting in 350 ng of DNA for each bombardment. In brief, 4- to 5-week-old cotyledonary stage somatic embryos (approximately 30 to 40) were picked and placed in the center of a petri dish containing induction/maintenance medium (41). The following day the embryos were bombarded using the following settings: 900-lb/in2 rupture disk and 0.25-in gap. After bombardment, the somatic embryos were maintained on the induction medium for 10 days before they were transferred to selection medium (15 mg/liter kanamycin in maintenance medium). Kanamycin selection was maintained for up to 22 weeks with subculturing every 2 to 3 weeks. Reinduced embryonal masses (referred to as secondary somatic embryogenesis) were screened for GUS activity by histochemical staining. Emerging embryonal masses with GUS-positive sectors were cultivated independently (41). Regeneration of plants from stably transformed cultures was initiated approximately 4 to 6 months after DNA transformation.
Sampling.
Rhizosphere soil samples were collected in November 2003 from an experimental plot in Val Cartier (QC, Canada) where genetically modified and control white spruce trees were planted in 2000. The experimental design comprised three treatments: (i) trees transformed with cryIA(b), uidA, and nptII; (ii) trees transformed with uidA and nptII; and (iii) nontransformed controls. For the cryIA(b)/uidA/nptII and uidA/nptII treatments, replicate trees used in this study were derived from the same transformation event. The treatments were arranged in a completely randomized design. Soil samples (approximately 15 to 20 g [wet weight] each) were collected from the rhizospheres of six different trees (n = 6) to obtain a total of 18 soil samples. The samples were transported on ice to the laboratory and then lyophilized and cryopreserved at −80°C until total genomic soil DNA isolation.
Soil DNA isolation.
For each sample, DNA was extracted from 250-mg (dry weight) soil subsamples using a MoBio UltraClean soil DNA isolation kit by following the manufacturer's protocol (MoBio Laboratories, Solana Beach, CA). Isolated DNA was resuspended in 50 μl of S5 solution (MoBio Laboratories) and maintained at −20°C until 16S rRNA gene PCR amplification.
16S rRNA gene PCR amplification.
Soil DNA was PCR amplified using primers 968f and 1401r targeting a 433-bp portion of the 16S rRNA gene (35). Five microliters of 10× PCR buffer (QIAGEN, Mississauga, ON, Canada), 5 μl of a 5 μM solution of each primer, 1 μl of a 10 mM solution of each deoxyribonucleoside triphosphate, 2 μl of a 1/20 dilution of total soil DNA, 1.25 U of Taq DNA polymerase (QIAGEN), and 31.75 μl nanopure H2O were combined to obtain a 50-μl (total volume) PCR mixture for each sample. PCR amplifications were performed using an MJ Research PTC-200 DNA Engine (MJ Research, Waltham, MA) with the following parameters: (i) initial denaturation for 3 min at 94°C; (ii) 35 cycles of denaturation for 30 s at 94°C, hybridization for 30 s at 60°C, and elongation for 45 s at 72°C; and (iii) final elongation for 5 min at 72°C. PCR products were then visualized using 1.5% agarose-1× Tris-acetate-EDTA gel electrophoresis, followed by ethidium bromide staining and UV transillumination using a Gel Logic 200 transilluminator (Eastman Kodak Company, Rochester, NY). Once amplicon sizes were confirmed, DNA cleanup was performed using a PCR cleanup kit (QIAGEN), and DNA was conserved at −20°C prior to cloning.
Shotgun cloning.
Cleaned amplicons were ligated into pCR4-TOPO vectors and inserted into chemically competent Escherichia coli cells as described in the One Shot TOPO-TA cloning kit protocol (Invitrogen, Carlsbad, CA). For each sample, 96 clones were randomly selected and cultured for 24 h at 37°C in 1.5 ml of Luria-Bertani broth supplemented with 100 μg/ml ampicillin.
Plasmid DNA extraction and ARDRA.
Plasmid DNA extraction was performed using a DirectPrep 96 miniprep kit (QIAGEN), and partial 16S rRNA gene inserts were PCR amplified using the PCR and protocols described above but with a 1/20 plasmid DNA dilution and PCR primers T3 and T7. After confirmation of amplicon sizes using gel electrophoresis, 13 μl of amplified DNA from each clone was separately digested for 2 h at 37°C using either 5 U of MspI or 5 U of HaeIII (New England Biolabs, Mississauga, ON, Canada) in 20-μl reaction mixtures using the appropriate restriction buffer and concentration, as suggested by the manufacturer (New England Biolabs). Restriction patterns were visualized by 3% agarose-1× Tris-boric acid-EDTA electrophoresis, followed by ethidium bromide staining and UV transillumination. Each different restriction pattern among the 1,728 patterns obtained was identified using the 1D Image Analysis software (v. 3.6; Kodak) and was defined as an operational taxonomical unit (OTU).
Sequencing.
Aliquots (1.5 μl) of extracted plasmidic DNA from at least one clone of each different OTU were sequenced with a CEQ 8000 genetic analysis system (Beckman Coulter, Fullerton, CA) using primer T7 according to the manufacturer's instructions.
Sequence analysis.
DNA sequences were edited, and consensus sequences were obtained using BIOEDIT v.7.0.4.1 (22). The presence of a chimera was verified using Bellerophon (http://foo.maths.uq.edu.au/∼huber/bellerophon.pl) (26) and CHIMERA CHECK (http://rdp.cme.msu.edu) (5), and suspected chimeric sequences were not included in further analysis. All the remaining sequences were screened against the sequences in GenBank (National Center for Biotechnology Information) using BLASTn v.2.2.12 (http://www.ncbi.nlm.nih.gov/BLAST/) (1) and RDP-II (5). Homologous sequences found in GenBank and RDP-II were used to construct a multiple-sequence alignment with ClustalX v.1.83 (46). A neighbor-joining analysis was performed using PAUP v.4.0b10 (43) with 1,000 bootstrap replicates. Phylogenetic trees were visualized using Treeview and were edited in Microsoft Word.
Statistical analysis.
Distance matrices were prepared using DNADIST and the Jukes-Cantor algorithm (PHYLIP v.3.64) (16). In order to test the null hypothesis that there are no difference in the three treatments, the Cramér-von Mises statistic was calculated, and a Monte Carlo test procedure was executed using the S-Libshuff program (v.1.0; http://www.plantpath.wisc.edu/fac/joh/S-libshuff.html) (39). The tests were conducted first for the three different treatments and then for all six individual rRNA gene libraries within each treatment. OTU frequency was determined by grouping all clones assigned to the same OTU. Richness, evenness, and dominance indices were calculated either manually or using EstimateS (v.7.5.05; http://purl.oclc.org/estimates). The indices retrieved included richness indicators such as the Shannon diversity index, the Chao-1 rarefaction estimator, the Margalef index, and the Alpha diversity and Shannon and Simpson evenness/dominance indices.
Nucleotide sequence accession numbers.
The nucleotide sequences determined in this study have been deposited in the NCBI database under accession no. DQ683995 to DQ684671.
RESULTS
High-molecular-weight DNA was recovered from every soil sample, and the 16S ribosomal cluster was successfully amplified by PCR. Restriction enzymes MspI and HaeIII were used to screen the 1,728 cloned amplicons for ARDRA. OTU grouping and sequencing based on restriction pattern were previously shown to be reproducible (17).
ARDRA was carried out on 1,728 shotgun-cloned partial 16S rRNA gene fragments (96 fragments per replicate). Restriction pattern data sets revealed 686 OTUs which were successfully sequenced, most of which had only few representative clones (Fig. 1). Of these OTUs, 314 were associated with the rhizospheres of cryIA(b)/uidA/nptII trees, 294 were associated with the rhizospheres of uidA/nptII trees, and 270 were associated with the rhizospheres of control trees. A total of 1,593 environmental clones were used for BLAST searches in the GenBank and RDP-II databases. This allowed us to classify unknowns into major bacterial groups. A high level of diversity was observed, and 16 major groups were represented (Table 1). The most frequently represented groups detected in all treatments were the Gammaproteobacteria (30.73 to 34.36% for the three treatments), Thermomicrobia (4.30 to 7.90%), Betaproteobacteria (4.67 to 6.13%), Chlamydiae (5.80 to 7.60%), and Alphaproteobacteria (5.00 to 7.20%). Within groups specifically associated with certain treatments, Bacteroidetes were found in only one replicate of the cryIA(b)/uidA/nptII treatment, whereas cyanobacteria were found in all but the uidA/nptII treatment. Genus incertae sedis WS3 and Nitrospira were also present in all treatments except the cryIA(b)/uidA/nptII treatment (Table 1). Certain OTUs were associated exclusively with certain treatments (Table 2). Twenty clones distributed among three OTUs were associated exclusively with the cryIA(b)/uidA/nptII treatment, 19 clones in three OTUs were associated exclusively with the uidA/nptII treatment, and 51 clones in seven OTUs were associated exclusively with the control treatment. A total of 27 clones in four OTUs shared association with both the cryIA(b)/uidA/nptII and uidA/nptII treatments, and 91 clones in 14 OTUs shared association with the uidA/nptII and control treatments.
FIG. 1.
Observed OTU distribution for two transgenic P. glauca treatments and one nontransgenic P. glauca treatment.
TABLE 1.
Relative clone abundance for the 18 different 16S rRNA gene libraries obtained from the rhizospheres of transgenic [cryIA(b)/uidA/nptII and uidA/nptII] and nontransgenic (control) trees with respect to different bacterial taxa
| Phylogenetic group | Relative bacterial clone abundance (%)
|
||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
cryIA(b)/uidA/nptII
|
uidA/nptII
|
Control
|
|||||||||||||||||||
| Replicate 1 | Replicate 2 | Replicate 3 | Replicate 4 | Replicate 5 | Replicate 6 | All | Replicate 1 | Replicate 2 | Replicate 3 | Replicate 4 | Replicate 5 | Replicate 6 | All | Replicate 1 | Replicate 2 | Replicate 3 | Replicate 4 | Replicate 5 | Replicate 6 | All | |
| Acidobacteria | 0.00 | 1.10 | 0.00 | 5.60 | 6.30 | 0.00 | 2.10 | 2.20 | 3.60 | 3.30 | 3.30 | 6.80 | 1.10 | 3.40 | 1.20 | 4.70 | 1.10 | 5.80 | 1.20 | 0.00 | 2.10 |
| Actinobacteria | 1.30 | 1.10 | 1.10 | 2.20 | 0.00 | 3.40 | 1.50 | 2.20 | 0.00 | 1.10 | 1.10 | 1.10 | 2.30 | 1.30 | 3.60 | 0.00 | 3.30 | 2.30 | 1.20 | 2.20 | 2.10 |
| Bacteroidetes | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.10 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Chlamydiae | 7.90 | 9.20 | 6.40 | 6.70 | 6.30 | 9.20 | 7.60 | 11.20 | 3.60 | 6.50 | 7.60 | 6.80 | 5.70 | 7.50 | 4.80 | 11.60 | 6.60 | 3.50 | 2.40 | 5.40 | 5.80 |
| Cyanobacteria | 0.00 | 0.00 | 1.10 | 0.00 | 0.00 | 0.00 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.20 |
| Firmicutes | 1.30 | 0.00 | 0.00 | 1.10 | 1.10 | 0.00 | 0.60 | 1.10 | 1.20 | 0.00 | 3.30 | 0.00 | 0.00 | 0.80 | 0.00 | 0.00 | 1.10 | 0.00 | 0.00 | 0.00 | 0.20 |
| Gemmatimonadetes | 0.00 | 0.00 | 0.00 | 1.10 | 0.00 | 0.00 | 0.20 | 0.00 | 0.00 | 0.00 | 1.10 | 0.00 | 0.00 | 0.20 | 2.40 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.40 |
| Genus incertae sedis TM7 | 0.00 | 1.10 | 1.10 | 0.00 | 0.00 | 0.00 | 0.40 | 1.10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.20 | 1.20 | 0.00 | 1.10 | 0.00 | 0.00 | 0.00 | 0.40 |
| Genus incertae sedis WS3 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.10 | 0.20 | 0.00 | 0.00 | 0.00 | 1.20 | 0.00 | 0.00 | 0.20 |
| Nitrospira | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.10 | 0.00 | 0.20 | 0.00 | 1.20 | 0.00 | 0.00 | 1.20 | 0.00 | 0.40 |
| Thermomicrobia | 0.00 | 6.90 | 8.50 | 4.40 | 3.20 | 0.00 | 4.30 | 2.20 | 6.00 | 8.70 | 8.70 | 5.70 | 8.00 | 6.40 | 6.00 | 7.00 | 6.60 | 10.50 | 4.90 | 14.10 | 7.90 |
| Verrucomicrobia | 2.60 | 1.10 | 1.10 | 0.00 | 0.00 | 1.10 | 0.90 | 1.10 | 1.20 | 2.20 | 0.00 | 0.00 | 1.10 | 0.90 | 4.80 | 2.30 | 2.20 | 0.00 | 1.20 | 0.00 | 1.70 |
| Proteobacteria | 42.10 | 51.70 | 56.40 | 48.90 | 52.60 | 62.10 | 52.20 | 55.10 | 52.40 | 46.70 | 51.10 | 54.50 | 46.60 | 50.80 | 58.30 | 52.30 | 60.40 | 46.50 | 65.90 | 56.50 | 56.80 |
| Alphaproteobacteria | 2.65 | 5.74 | 8.52 | 8.90 | 7.36 | 9.19 | 7.20 | 4.52 | 7.13 | 2.19 | 2.20 | 6.81 | 6.80 | 5.08 | 7.11 | 1.15 | 4.41 | 9.30 | 6.13 | 3.28 | 5.00 |
| Betaproteobacteria | 2.65 | 5.74 | 4.23 | 6.65 | 6.31 | 10.37 | 6.06 | 4.52 | 5.97 | 1.07 | 3.27 | 10.25 | 4.57 | 4.67 | 3.56 | 6.96 | 7.67 | 4.65 | 3.69 | 9.77 | 6.13 |
| Deltaproteobacteria | 0.00 | 1.14 | 1.07 | 1.12 | 2.10 | 1.18 | 1.72 | 1.10 | 1.21 | 2.19 | 3.27 | 1.14 | 0.00 | 2.08 | 1.17 | 2.30 | 6.58 | 1.16 | 4.88 | 1.07 | 3.86 |
| Gammaproteobacteria | 30.27 | 27.56 | 36.21 | 22.25 | 30.51 | 37.94 | 31.01 | 38.24 | 35.74 | 32.60 | 27.19 | 21.58 | 27.26 | 30.73 | 36.90 | 33.68 | 37.33 | 17.44 | 45.14 | 36.95 | 34.36 |
| Unclassified Proteobacteria | 6.57 | 11.48 | 6.37 | 10.02 | 6.31 | 3.48 | 6.26 | 6.72 | 2.36 | 8.69 | 15.23 | 14.77 | 7.97 | 8.23 | 9.50 | 8.16 | 4.41 | 13.95 | 6.13 | 5.42 | 7.50 |
| Unclassified bacteria | 44.70 | 27.60 | 24.50 | 30.00 | 30.50 | 23.00 | 29.90 | 23.60 | 28.60 | 31.50 | 23.90 | 23.90 | 34.10 | 28.10 | 17.90 | 19.80 | 17.60 | 30.20 | 22.00 | 21.70 | 21.90 |
TABLE 2.
OTUs systematically associated with certain treatments identified by BLAST nucleotide-nucleotide closest-neighbor comparisons in GenBank
| Closest neighbor | % Similarity | No. of homologous base pairs/total no. | Accession no. | No. of clones with:
|
||
|---|---|---|---|---|---|---|
| cryIA(b)/uidA/nptII treatment | uidA/nptII treatment | Control treatment | ||||
| Uncultured beta proteobacterium clone EB1098 | 99 | 409/411 | AY395417 | 4 | ||
| Uncultured Chloroflexi bacterium clone BSR2LA07 | 95 | 336/351 | AY690094 | 8 | ||
| Uncultured soil bacterium clone 1236-2 | 98 | 428/434 | AY326587 | 8 | ||
| Uncultured betaproteobacterium clone BDC2 C07 | 98 | 392/398 | AY690254 | 4 | 1 | |
| Uncultured gammaproteobacterium clone BPC2 E10 | 98 | 393/401 | AY689997 | 5 | 1 | |
| Uncultured soil bacterium clone 1236-2 | 98 | 426/434 | AY326587 | 4 | 8 | |
| Uncultured bacterium clone SM-OTU55 | 96 | 393/407 | AY321212 | 3 | 1 | |
| Uncultured Chloroflexi bacterium clone BSR2LA07 | 92 | 372/401 | AY690094 | 9 | ||
| Bacterium Ellin5264 | 97 | 423/434 | AY234615 | 4 | ||
| Uncultured gammaproteobacterium clone BPC2 E10 | 97 | 391/401 | AY689997 | 6 | ||
| Uncultured Chloroflexi bacterium clone BSR1LC03 | 89 | 297/333 | AY689715 | 1 | 6 | |
| Uncultured Chloroflexi bacterium clone BSR1LG07 | 96 | 374/387 | AY689725 | 5 | 1 | |
| Uncultured Acidobacteria bacterium clone EB1023 | 95 | 424/445 | AY395342 | 1 | 5 | |
| Uncultured Chloroflexi bacterium clone BSR1LC03 | 94 | 387/409 | AY689715 | 6 | 3 | |
| Uncultured Chloroflexi bacterium clone BPM2 E03 | 98 | 367/371 | AY689675 | 4 | 2 | |
| Uncultured bacterium clone 1974a-21 | 89 | 376/422 | AY917851 | 2 | 4 | |
| Uncultured Chloroflexi bacterium clone BDC3SG08 | 97 | 322/330 | AY689624 | 3 | 1 | |
| Uncultured gammaproteobacterium clone AKYH1135 | 94 | 409/435 | AY921812 | 6 | 5 | |
| Uncultured soil bacterium clone 1236-2 | 97 | 424/434 | AY326587 | 4 | 2 | |
| Uncultured bacterium clone 1959a-14 | 98 | 427/434 | AY917650 | 1 | 3 | |
| Uncultured Chloroflexi bacterium clone BSR1LC03 | 94 | 383/406 | AY689715 | 8 | 1 | |
| Uncultured forest soil bacterium clone DUNssu045 | 98 | 412/420 | AY913265 | 1 | 4 | |
| Uncultured gammaproteobacterium YNPRH65B | 98 | 429/434 | AF465652 | 4 | 3 | |
| Uncultured bacterium clone SM-OTU07 | 93 | 405/434 | AY321244 | 1 | 4 | |
| Uncultured Chloroflexi bacterium clone BSR2LA07 | 93 | 314/337 | AY690094 | 9 | ||
| Uncultured Acidobacteria bacterium clone EB1023 | 98 | 422/428 | AY395342 | 8 | ||
| Uncultured betaproteobacterium clone BDC2 C07 | 98 | 382/388 | AY690254 | 8 | ||
| Uncultured soil bacterium clone Y11-13 | 97 | 408/420 | AY930389 | 6 | ||
| Bacterium Ellin5280 | 98 | 416/422 | AY234631 | 5 | ||
| Uncultured bacterium clone 1959a-14 | 97 | 413/422 | AY917650 | 5 | ||
| Uncultured soil bacterium clone 1236-2 | 97 | 351/360 | AY326587 | 10 | ||
To test the null hypothesis that the treatments had no effect on the microbial populations, a Cramér-von Mises statistic analysis combined with a Monte Carlo comparison was conducted. Combined sequence libraries for each of the three treatments were significantly different from each other (P < 0.05 adjusted for multiple comparisons at P < 0.0085) (Table 3). The null hypothesis was rejected for all treatment comparisons, including the cryIA(b)/uidA/nptII-control, cryIA(b)/uidA/nptII-uidA/nptII, and uidA/nptII-control comparisons. In each comparison, the test revealed significantly more differences among treatments than within treatments. Further analysis also revealed that most replicate sequence libraries for each treatment were significantly similar to each other (P < 0.05) (Table 4).
TABLE 3.
Comparison of 16S rRNA gene sequence libraries from rhizospheres of transgenic P. glauca and nontransgenic P. glauca combined for treatmentsa
| Treatment |
P value with:
|
||
|---|---|---|---|
| cryIA(b)/uidA/nptII | uidA/nptII | Control | |
| cryIA(b)/uidA/nptII | <0.0001 | 0.0001 | |
| uidA/nptII | <0.0001 | <0.0001 | |
| Control | 0.0001 | 0.0001 | |
For correction for multiple comparisons (six tests), the minimum P value for a 0.0500 family-wise error rate is 0.0085. For the Monte Carlo error (1,000 permutations), the 95% margin of error for the minimum P value is <0.0001. All treatment libraries were compared to each other twice. If at least one of the two comparisons resulted in a value lower than the minimum P value, the libraries were considered heterogeneous.
TABLE 4.
Comparison of 16S rRNA gene sequence libraries from rhizospheres of transgenic P. glauca and nontransgenic P. glauca for replicatesa
| Treatment | Replicate |
P value with:
|
|||||
|---|---|---|---|---|---|---|---|
| Replicate 1 | Replicate 2 | Replicate 3 | Replicate 4 | Replicate 5 | Replicate 6 | ||
| cryIA(b)/uidA/nptII | 1 | 0.5538 | 0.1848 | 0.2430 | 0.3659 | 0.1335 | |
| 2 | 0.5120 | 0.4671 | 0.5769 | 0.4338 | 0.0045 | ||
| 3 | 0.3560 | 0.8218 | 0.3084 | 0.1492 | 0.4509 | ||
| 4 | 0.0019 | 0.0032 | 0.0985 | 0.1034 | 0.0001 | ||
| 5 | 0.0538 | 0.0026 | 0.0537 | 0.8866 | 0.0158 | ||
| 6 | 0.0002 | 0.0000 | 0.0444 | 0.0328 | 0.0151 | ||
| uidA/nptII | 1 | 0.4422 | 0.0582 | 0.0245 | 0.2234 | 0.7441 | |
| 2 | 0.1665 | 0.0061 | 0.0003 | 0.0009 | 0.0107 | ||
| 3 | 0.0006 | 0.0106 | 0.0993 | 0.0260 | 0.0303 | ||
| 4 | 0.0013 | 0.0164 | 0.1397 | 0.1766 | 0.7435 | ||
| 5 | 0.0021 | 0.0027 | 0.0038 | 0.3540 | 0.0438 | ||
| 6 | 0.1675 | 0.3352 | 0.3120 | 0.5110 | 0.8221 | ||
| Control | 1 | 0.2303 | 0.3993 | 0.0339 | 0.0017 | 0.0149 | |
| 2 | 0.0709 | 0.0526 | 0.0118 | 0.0458 | 0.0523 | ||
| 3 | 0.0828 | 0.0555 | 0.1227 | 0.2750 | 0.0145 | ||
| 4 | 0.0046 | 0.0548 | 0.3781 | 0.0466 | 0.1363 | ||
| 5 | 0.0484 | 0.4061 | 0.8796 | 0.2897 | 0.0761 | ||
| 6 | 0.0009 | 0.0084 | 0.0058 | 0.0209 | 0.0121 | ||
For correction for multiple comparisons (30 tests per treatment), the minimum P value for the 0.0500 family-wise error rate is 0.0017. For the Monte Carlo error (1,000 permutations), the 95% margin of error for the minimum P value is <0.0001. All libraries within each treatment were compared to each other twice. If at least one of the two comparisons resulted in a value lower than the minimum P value, the libraries were considered heterogeneous.
Diversity indices were compiled for each of the three treatments (Table 5). All diversity indices increased from the control treatment to the uidA/nptII treatment to the cryIA(b)/uidA/nptII treatment.
TABLE 5.
Diversity indices for bacterial OTUs associated with the rhizospheres of genetically modified and non-genetically modified P. glauca trees
| Treatment | Species richness indices
|
Evenness or dominance indices
|
|||||
|---|---|---|---|---|---|---|---|
| No. of OTUs recorded | hanno | Chao-1 | Margalef | Alpha | Shannon evenness | Simpson | |
| cryIA(b)/uidA/nptII | 314 | 5.45 | 750.01 | 49.912 | 325.03 | 0.948 | 198.09 |
| uidA/nptII | 294 | 5.34 | 633.51 | 46.667 | 269.35 | 0.940 | 153.61 |
| Control | 270 | 5.23 | 588.39 | 43.000 | 225.63 | 0.934 | 131.51 |
DISCUSSION
The aims of this study were to characterize the rhizospheres of genetically modified and control P. glauca trees growing in an experimental plantation and to determine whether the treatments had an effect on microbial composition and diversity. Analysis of 1,728 rhizosphere bacterial clones (576 clones per treatment) using a highly discriminating culture-independent molecular approach indicated that there were statistically significant differences between the microbial communities inhabiting the rhizospheres of trees carrying the cryIA(b) and uidA/nptII transgenes, trees carrying only the uidA/nptII transgenes, and control trees. Inclusion of the Cramér-von Mises statistic allowed us to consider the species compositions of the rhizospheres while taking genetic diversity into account. In all cases, partial 16S rRNA gene cloned libraries were more similar within treatments than among treatments. However, before conclusions concerning the biological significance of these results are reached, the methodology and analyses should be discussed.
One can argue that the first potential weakness of this study is that the site was not replicated. We sampled the only experimental site with transgenic conifers in Canada, and therefore it was not possible to repeat our sampling at different sites. It is possible that our results represent an artifact of the site studied and cannot be generalized. However, the treatments were properly replicated within the experimental design at the study site, and the observed differences were significant for the treatments. This suggests that the observed differences were clearly associated with the treatments.
Another potential explanation for our observations is that the treatments were responsible for the observed differences but that factors associated with the treatments other than the actual transgenes were the real cause. For example, transformed P. glauca calli were screened using antibiotics to eliminate untransformed cells. Clearly, this could not be done with the untransformed callus cultures. This selection with antibiotics could have been responsible for the observed differences in microbial communities in our treatments. However, if this were the case, we would not expect to observe differences between the transformed trees containing the cryIA(b) and uidA/nptII constructs and the transformed trees containing only the uidA/nptII construct, since they were screened with the same antibiotics.
Another possibility is that the transformation events themselves, not the gene constructs, were responsible for the observed effects. Although this is entirely possible, it is difficult to imagine that this effect would be more important among treatments than within treatments. Therefore, our conclusion is that the transgenes themselves were responsible for the observed differences.
The analysis used in this study is relatively new. It is possible that the results obtained are in fact attributable to the method of analysis. Although the analysis is sound and intuitive, the differences observed could be biologically insignificant, even though they are statistically significant. We believe that this is not the case. The within-treatment comparisons were all much more similar (even though they were for different blocks) than the between-treatment comparisons. Also, the presence of multiple unique bacterial clones associated with specific treatments is indicative of a true treatment effect and could have biological importance. Unfortunately, the clones do not have close homology in the public databases for the gene sequenced. This makes it difficult to assess whether the observed trend could be extrapolated to bacterial groups.
In light of these findings, biological reasons rather than methodological reasons better support the results obtained and strongly suggest that trees within the treatments impacted the rhizosphere-inhabiting bacterial communities. Interestingly, the impact seems to be associated with both the B. thuringiensis-transformed trees, modified using the cryIA(b)/uidA/nptII gene set, and the uidA/nptII-modified trees, but the effects are different for these two treatments. The ecological diversity indices calculated also support this trend. Although this observation was surprising, there may be an explanation for it. A study conducted in 2003 established that a DNA construct composed of the nptII transgene remained detectable by PCR in potato field soil after 4 years of laboratory incubation (8). The two genetic markers used in the present study code for the production of GUS (uidA) and neomycin phosphotransferase II (nptII). They are now commonly used in plant genetic modification as means to assess the successful insertion of transgenic material, as well as to infer the level of expression. The uidA gene product catalyzes the cleavage of a wide variety of β-glucuronides. As most plants assayed to date lack detectable glucuronidase activity, the incorporation of this gene in a plant genome allows detection of color formation in plant parts expressing this enzyme when chromogenic glucuronidase substrates are added to the growth media. The nptII gene codes for a small enzyme (25 kDa) catalyzing the phosphorylation of several antibiotics, including neomycin and kanamycin, which also allows bioassays measuring the success of a genetic modification to be conducted when suitable antibiotics are added to the growth media (18). Genetic transformation of plants with these markers results in exogenous DNA, RNA, and subsequent protein structures that are present within the plant tissue and could logically have the same potential to impact soil microbial communities as pest resistance transgenes would have.
The effects of B. thuringiensis-modified plants on soil microorganisms have been studied in other plant systems. Leaves of cotton genetically modified with B. thuringiensis [cryIA(b) and cryIA(c)] were placed in soil for decomposition, and microbial communities were monitored using community level physiological profiling and restriction fragment length polymorphism. Although some degree of variability was detected, the authors attributed the shift to the genetic modification of the plant itself rather than to the nature of the transgenic DNA (10). Plate counts revealed that minimal differences were observed when the microfloras of B. thuringiensis-producing genetically modified and B. thuringiensis (M-Trak)-treated potato plants were compared (11). The effects of B. thuringiensis [cryIA(b)]-transformed corn on microorganisms, earthworms, nematodes, protozoans, and fungi in soil were also studied. Using various techniques, no effect of the root exudate-released toxin or plant biomass on target organisms was measured (38). Studies using culture-independent approaches also yielded similar results; B. thuringiensis [cryIA(b)]-modified KeMingDao rice straw did not have significant persistent effects on the abundance of soil microorganisms when it was incubated in flooded paddy soil under laboratory conditions, although some significant differences were detected in the early stages of the experiment (47). With B. thuringiensis [cryIIIB(b)]-transformed corn there were also no differences between transgenic and nontransgenic microbial communities as determined using terminal restriction fragment length polymorphism analysis (7).
In our opinion, the main explanations supporting the clear microbial shift observed in this study compared to other studies with similar aims could be narrowed down to two possibilities. The first possibility is related to the high resolution of the methodological approach used. We used a robust culture-independent approach that accounted for the high microbial diversity usually found in rhizosphere soils, and an important number of 16S rRNA gene clones (1,593 clones in 686 OTUs) were identified and classified into major bacterial groups, which was usually not the case in most similar studies performed to date. The second possibility to explain why we found a significant difference is that rhizosphere microbial population shifts following B. thuringiensis transformation of plants have been studied before in different plant systems but not in the context of trees. However, it is difficult at this point to clearly define the potential implications for the trees, for the bacterial community outside the rhizosphere, and for soil processes in general.
Further studies are clearly needed to better understand the mechanisms involved in the microbial community structure shifts observed. Since the results obtained involved a single sampling date, it would also be interesting to track the shifts over a period of time to see if the differences are associated with certain growth stages of the plants or with seasonal variations. Microbial species or groups of interest could also be monitored quantitatively using recent advancements in real-time PCR. Finally, studies measuring functional genes to assess microbe functional diversity instead of taxonomic diversity could also add another perspective to this question.
Acknowledgments
This project was supported by the Canadian Biotechnology Strategy Fund.
We thank France LeBlanc and Josyanne Lamarche for technical assistance, as well as Josyanne Lamarche for input and review of the manuscript.
Footnotes
Published ahead of print on 27 April 2007.
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