Correspondence: Richard C. Hamelin, Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du PEPS, PO Box 10380, Stn. Sainte-Foy, Québec, QC, Canada G1V 4C7. Tel.: +1 418 648 3693; fax: +418 648 5849; e-mail: Richard.Hamelin@RNCan.gc.ca
Chitinase genes isolated from plants, bacteria or fungi have been widely used in genetic engineering to enhance the resistance of crops and trees to fungal pathogens. However, there are concerns about the possible effect of chitinase-transformed plants on nontarget fungi. This study aimed at evaluating the impact of endochitinase-transformed white spruce on soil fungal communities. Endochitinase-expressing white spruce and untransformed controls were transplanted in soils from two natural forests and grown for 8 months in a greenhouse. Soil fungal biomass and diversity, estimated through species richness and Shannon and Rao diversity indices, were not different between transgenic and control tree rhizospheres. The fungal phylogenetic community structure was the same in soil samples from control and transgenic white spruces after 8 months. Soil type and presence of seedlings had a much more significant impact on fungal community structure than the insertion and expression of the ech42 transgene within the white spruce genome. The results suggest that the insertion and constitutive expression of the ech42 gene in white spruce did not significantly affect soil fungal biomass, diversity and community structure.
White spruce [Picea glauca (Moench) Voss] is an important native forest species in North America, with a wide geographic distribution, ranging from Newfoundland to Alaska. It is an important commercial species for pulpwood production, softwood lumber and reforestation (Farrar, 1996). However, when grown in nurseries and plantations, white spruce seedlings can be affected by a variety of fungal pathogens causing root rots (Hamelin et al., 1996; Jeng et al., 1997), with adverse effects on their survival.
One research avenue to limit losses caused by fungal pathogens is to increase tree resistance by the introduction of resistance genes through genetic modification (Bolar et al., 2000, 2001). One particularly interesting approach consists in using fungal genes that are inhibitory to fungal pathogens. Chitinases are digestive enzymes that break down glycosidic bonds in chitin, a linear homopolymer of β-1,4 linked N-acetylglucosamine. Because chitin is an important component of fungal cell walls, organisms producing high levels of chitinolytic enzymes were used as biocontrol agents against fungal plant pathogens. Previous work on the biocontrol agent Trichoderma harzianum demonstrated the strong inhibition effect of its chitinases against a broad range of fungal plant pathogens (Lorito et al., 1998; Gokul et al., 2000). Among the different chitinase genes, the ech42 gene produces an endochitinase that randomly hydrolyzes the chitin polymer chain and it was shown to have the strongest antifungal activity (Lorito et al., 1998). Moreover, the ech42 gene product is more effective than plant-encoded chitinases (Harman et al., 1993; Lorito et al., 1993). For this reason, the ech42 gene was used to genetically transform some crops, fruit and forest trees to increase their resistance against fungal pathogens (Lorito et al., 1998; Bolar et al., 2001; Mora & Earle, 2001; Noël et al., 2005; Tesfaye et al., 2005; Gentile et al., 2007). Black spruce and hybrid poplar were the first forest tree species to be transformed with the ech42 gene, and they showed, in vitro, an increased resistance to the root pathogen Cylindrocladium floridanum and the foliar rust Melampsora medusae, respectively (Noël et al., 2005).
Enhanced resistance toward fungal pathogens caused by ech42 gene insertion in plant genome is a noteworthy scientific accomplishment and this approach is an alternative to the selection of resistant plant genotypes through conventional breeding and chemical fungicide spreading.
However, during their lifetime, trees interact with many types of fungi, not only pathogenic ones. Leaves are colonized by fungal endophytes (Arnold et al., 2007; Jumpponen & Jones, 2009), roots are associated with mycorrhizal fungi (Read, 1991) and a large diversity of saprotrophic fungi are present in the soil and litter to decompose dead wood and leaves (Lonsdale et al., 2008; Stenlid et al., 2008). These fungi play important roles in tree nutrition and protection, nutrient cycling and humus formation in forest ecosystems. Most of these nontargeted fungi possess chitin in their cell wall, making them potentially as susceptible to transgenic plants overexpressing exogenoeous chitinase as pathogenic fungi. Depending on the fungal groups, the impact could be more or less significant as chitin content and microfibrils structure vary among them (Bartnicki-Garcia, 1968; Gooday, 1995).
Furthermore, random integration of multiple copies of transgenic DNA into the recipient plant genome may also result in unexpected (pleiotropic) effects, leading to the alteration of physiological processes such as changes in plant root exudation (Li et al., 2009) and plant development. For example, the expression of an endochitinase-encoding gene from T. harzianum in apple tree resulted in reduced growth in transformed lines (Bolar et al., 2000), suggesting an impact on some metabolic pathways. Also, similar transgenic apple trees, transformed with endochitinase and exochitinase genes from Trichoderma atroviride, showed a negative correlation between growth and endochitinase activity. This growth reduction appeared to be associated with high lignin content and overexpression of peroxidase and glucanase activity (Faize et al., 2003), which are both involved in plant disease response to fungal pathogens. These examples show that the insertion and expression of the chitinase genes not only increase plant antifungal activities, but also affect apple tree metabolism. These metabolism modifications could, in turn, have unexpected effects on nontarget fungal communities.
Therefore, the field deployment, as part of plantation or reforestation programs, of genetically engineered trees exhibiting antimicrobial activity, like endochitinase transformed trees, raises the issues of the potential risks for nontargeted fungal communities. Until now, investigation of the impact of chitinase-transformed plants on nontarget fungi has mainly focused on mycorrhizal fungi (Vierheilig et al., 1993; Pasonen et al., 2005; Schäfer et al., 2009; Stefani et al., 2010), and conclusions from those studies are conflicting (Vierheilig et al., 1993; Pasonen et al., 2005; Stefani et al., 2010). No study has investigated the impact of chitinase-transformed trees on soil fungal diversity at the community level by contrasting soil fungal phylotype diversity recovered in control plant rhizosphere with that recorded in transgenic plant rhizosphere. However, Stefani et al. (2010) showed that ech42-transformed white spruce released endochitinase into soil through root exudates and the level of endochitinase in roots of transgenic white spruce was up to 10 times higher than that in roots of untransformed white spruce.
Therefore, the objective of the present study was to assess the impact of endochitinase-transformed (Et) white spruce on soil fungal communities in two natural forest soils. To do so, soil fungal libraries were constructed using the ITS1-F/LR5 primer pair to amplify in a single PCR reaction the entire internal transcribed spacer (ITS) and nuclear large-subunit (nLSU) fragment. We hypothesized that the fungal diversity recorded in soil samples associated with control white spruce is more diverse than that in soil samples associated with Et white spruce after 8 months of interaction.
Materials and methods
Experimental design and soil sampling
Embryogenic cell line PG653 of white spruce [Picea glauca (Moench) Voss] was transformed by Noël et al. (2005). For each soil fungal community, the potential impact of ech42-transformed (Et) white spruce was assessed by contrasting the soil fungal diversity recorded in the rhizosphere associated with three untransformed (control) and three transformed white spruce seedlings. Seedlings were transplanted in 2.5 dm3 pots after a dormancy period of 2 months, filled with two natural forest soils. The soil fungal communities were imported from soil collected in a white spruce stand (organic soil type, 46°79′N, 71°34′W) and a yellow birch/sugar maple hardwood stand (mineral soil type, 47°01′N, 71°35′W). Soil samples were removed from the top layer to a depth of 20 cm, transported in coolers, sieved (4-mm mesh) and kept at 4 °C until distributed into pots 5 days later. Basic soil chemical analyses were performed according to the protocols by Carter (1993) to assess their organic and mineral features based on the carbon content. Seedlings were disposed in a factorial design. The treatment factors tested were soil type (organic or mineral), transplantation (no seedling, control or Et white spruce) and sampling time [at the time of transplantation (T0) and 8 months later (T8)]. Four soil samples (c. 10 g) were cored per pot and soil samples from the same pot were then pooled before soil genomic DNA (gDNA) extraction. Soil samples were kept at −80 °C until processing.
Endochitinase activity was measured using needles since a correlation between needle endochitinase activity and systemic resistance had already been demonstrated (Noël et al., 2005). To quantify endochitinase activity, 25 mg of needles from three trees per treatment were used. Needles were homogenized in 1.5 mL of sodium acetate buffer (96 mM sodium acetate, 0.1% sodium dodecyl sulfate, 0.1% Triton X-100, 10 mM Na2EDTA) as described by Bolar et al. (2000). The procedure was adapted from Côté & Rutledge (2003) with the following modifications: samples were homogenized twice for 45 s each in a FastPrep (Q-BIOgene, Carlsbad, CA) apparatus and centrifuged twice at 13 000 g for 5 min. Supernatant (5 mL) was mixed with 50 mL of 0.2 mM 4-methylumbelliferyl-β-d-N,N′,N″-triacetylchitotrioside (Sigma-Aldrich Co., St. Louis, MO) dissolved in 100 mM sodium acetate buffer and incubated for 30 min at 37 °C. The reaction was stopped by adding 145 μL of 0.2 M sodium carbonate. Fluorescence was measured at 365 excitation/450 emission using a fluorolite 1000 microtiter-plate reader (Dynatech Laboratories, Chantilly, VA) and compared with a 4-methylumbelliferone (MU) (Sigma-Aldrich Co.) standard curve. The amount of protein was measured using the DC protein assay (Bio-Rad laboratories). The biological analyses were performed in triplicate and endochitinase activity is reported in nM MU released min−1 μg−1 protein.
Ergosterol extraction and HPLC
Soil fungal biomass was determined for each replicate using 250 mg (wet weight) of soil. The ergosterol extraction protocol followed the microwave-assisted extraction protocol as described by Montgomery et al. (2000). Samples were analyzed using an HPLC system equipped with a Waters 1524 binary pump, a Waters 717 plus autosampler and a Waters 2487 dual absorbance detector (Waters Corporation, Milford, MA). Ergosterol was separated from other organic soil compounds on a 4.6 × 250 mm Zorbax Rx-C18 reverse-phase column packed with an ODS 5 μm preceded by a Zorbax guard-column (Agilent Technologies, Palo Alto, CA). The mobile phase was methanol, acetonitrile (55 : 45, v/v), at a flow rate of 2 mL min−1. The experiment was performed at room temperature. Absorbance was read at 283 nm. Ergosterol content was determined using a standard curve based on the ergosterol/7-dehydrocholesterol (Sigma-Aldrich Co.) area ratio. Data were processed on waters breeze v.3.3 software (Waters Corporation).
DNA extraction and amplification
Total gDNA was extracted from ∼250 mg (dry weight) of soil using the PowerSoil™ DNA kit from MO BIO Laboratories Inc. (Solana Beach, CA) following the manufacturer's instructions. ITS regions and the large 28S subunit (LSU) of the nuclear rRNA were amplified with the forward fungal specific primer ITS1-F (Gardes & Bruns, 1993) and the reverse universal primer LR5 (Noël et al., 2005). PCR reactions were performed in a final volume of 25 μL and contained 1 × PCR buffer, 1.5 mM MgCl2, 200 μM of each dNTP (Invitrogen), 0.2 μM of each primer, 1 U of Platinum®Taq DNA polymerase (Invitrogen) and 1 μL of extracted DNA. The PCR cycle was as follows: 94 °C for 3 min, 35 cycles at 94 °C for 30 s, 50 °C for 30 s, 72 °C for 1 min and a final elongation at 72 °C for 5 min. PCR reaction was performed in triplicate for each of the 36 samples.
Library construction and sequencing
The PCR products of replicates from the same transplantation factor were pooled and then purified using the QIAquick PCR purification kit (Qiagen, Rockville, MD). The DNA concentration was measured using an ND-1000 Spectrophotometer (Nanodrop Technologies, Wilmington, DE). PCR products were cloned using the Qiagen PCR Cloning plus Kit according to the manufacturer's instructions. Fivefold molar excess of PCR products were incubated for 2 h at 14 °C with the pDrive Cloning Vector. Six libraries at T0 and six libraries at T8 were constructed. After an overnight incubation at 37 °C, 150 white bacterial colonies per library were spiked and transferred into 25 μL of PCR mixture for amplification as described above, except that the annealing temperature was set to 55 °C. Amplicons were sequenced on an ABI 3730xl (Applied Biosystems, Foster City, CA) on both strands using the ITS1-F and LR5 primers.
ITS and nLSU sequences were edited and assembled using sequencher v4.6 (GeneCodes, Ann Arbor, MI). The similarity threshold for ITS-nLSU sequences belonging to the same operational taxonomic unit (OTU) was set to 99% because of the low sequence divergence of nLSU. Consensus sequences of each OTU were identified, with the closest sequences found in the NCBI GenBank database using blastn (Altschul et al., 1990). PCR-generated chimeric sequences were determined from blast hits displaying a conspicuous incongruence between the ITS and the nLSU sequences and were excluded from the data sets. Sequences were aligned using muscle software v3.6 (Edgar, 2004) with two iterations. For each treatment, the number of OTUs and the Shannon diversity index were computed with dotur software v1.53 (Schloss & Handelsman, 2005) using a 1% distance level. The fungal OTUs turnover between libraries at T0 and T8 and between control and Et white spruce libraries was calculated with the nonparametric maximum likelihood estimator (NPMLE, θ index) based on species proportions (Yue & Clayton, 2005) using sons software v1.0 (Schloss & Handelsman, 2006). Sequence alignments were converted into distance matrices using dnadist program (Jukes–Cantor as a substitution model) from the phylip package to produce neighbor-joining trees with mega 3.1 software (Kumar et al., 2004) and with the r package ape v2.3 (Paradis et al., 2004).
Fungal community phylogenetic analyses
To test whether Et white spruce influences species membership in soil fungal communities, the phylogenetic relatedness of fungal species found in soil samples from control and Et white spruce was measured by computing the standardized effect size of the mean pairwise distance (SESMPD). The mean pairwise distance was weighed by fungal OTU abundance. The r package picante v4.0.1 (Kembel et al., 2009) was used to compute the SESMPD metric for control and Et white spruce libraries, at T0 and T8. The observed phylogenetic relatedness was compared with null communities generated by randomly shuffling (500 times) the tip labels across the tips of the phylogenies. Significant positive SESMPD values (P>0.95) show a phylogenetically even dispersion, indicating that species associated with a treatment are more distantly related to each other than by chance, whereas significant negative SESMPD values (P<0.05) show phylogenetic clustering. This indicates that species associated with a treatment are more closely related to each other than to the species in other treatments relative to a null model of phylogeny. SESMPD values close to zero associated with nonsignificant P-values indicate that OTUs are spread randomly across the tree. unifrac (Lozupone & Knight, 2005) was used to perform a principal coordinate analysis (PCoA) using normalized abundance weights as it treats each sample equally instead of treating each unit of branch equally. The similarity of fungal phylotype distribution between soil, control and Et white spruce libraries was also evaluated with a double principal coordinate analysis (DPCoA) (Pavoine et al., 2004) using the r package ade4 v1.4-11. The DPCoA allows to compare inter- and intralibrary fungal phylotype variability and to compute the Rao diversity index.
Nucleotide sequences were deposited in the NCBI GenBank database and are registered under the accession numbers EU689158–EU690957 and EU690958–EU692757, for ITS and nLSU sequences, respectively.
Soil chemical analyses
The organic (org) and mineral (mnl) soils sampled in this study mainly contrasted by their content in total carbon (org: 48.93%; mnl: 4.87%) and nitrogen (org: 1.68%; mnl: 0.33%). The pH recorded in both soils was similar (org: 3.2; mnl: 3.7). The remaining soil chemical analyses are presented in Supporting Information, Table S1.
The average (± SD) endochitinase activity recorded in needles was 152.57 (± 36.79) nM MU min−1 mg−1 proteins and 1324.75 (± 303.45) nM MU min−1 mg−1 proteins for control and Et white spruce, respectively. Endochitinase activity recorded in Et white spruce was significantly higher (df=18; P<0.001) than that in control white spruce.
Fungal biomass in control and Et white spruce rhizosphere
The average soil fungal biomass, measured by the concentration of ergosterol, was 7.5 times greater in organic soil than in mineral soil at T0. The level of ergosterol at T8 was 1.44 ± 0.172 and 1.60 ± 0.315 μg g−1 in organic soil samples associated with control and Et white spruce, respectively. In mineral soil samples, the ergosterol content recorded at T8 was 0.19 μg g−1 of soil in both control and Et white spruce. The Et white spruce had no effect on soil fungal biomass (df=18; P=0.79).
Fungal diversity in organic and mineral soil
The slope of the sequence-based rarefaction curves (Fig. 1) was lower for organic soil than for mineral soil at T0 and T8, indicating that the soil fungal diversity was more saturated in organic soil samples. The fungal communities observed at T0 were qualitatively and quantitatively different in organic and mineral soils. The organic soil was primarily colonized by Ascomycetes (82.5%), whereas the mineral soil harbored mainly Basidiomycetes (70.3%). Fungal OTU richness at T0 was 41 and 71 in organic and mineral soils, respectively. In the organic soil, the two most abundant OTUs were an uncultured fungus belonging to the Dothideomycetes (40.4% of total sequences) and an Acremonium sp. (30.4%). In the mineral soil, the two most abundant OTUs were identified as a Tricholomataceae (26.9%) and an Agaricales (8.5%). The fungal community overlap based on the NMPLE (θ) was only 2.5% between the organic and mineral soil at T0.
Comparison of fungal communities recorded in soil samples from control and Et white spruce
The number of OTUs along with the Shannon and Rao indices computed for each transplantation treatment at T0 and T8 are displayed in Table 1. The fungal species richness recorded in the organic soil had increased slightly for each transplantation treatment after 8 months in the greenhouse (Fig. 1). In the mineral soil, fungal species richness was similar between T0 and T8, except for soil samples associated with Et white spruce. For this treatment, the number of OTUs decreased from 47 to 35 between T0 and T8. Nevertheless, the level of fungal species richness observed at T8 in soil samples associated with Et white spruce was similar to the level observed in the other two treatments (Fig. 1).
Table 1. Fungal species richness, Rao and Shannon diversity indices and NPMLE θ computed in fungal DNA libraries according to each transplantation treatment
Fungal species richness and the Shannon and Rao diversity indices were not significantly different between the three transplantation treatments or between the two soil types according to the Poisson linear regression and the anova, respectively (Table 1).
Analyses of the standardized effect size of the mean pairwise distance (SESMPD, Table 2) showed that the structure of the fungal community recorded in organic soil samples from controls was random at T0 and T8. As for that of soil samples from Et white spruce, it was even at T0 and random at T8. In the mineral soil, the observed fungal community was clustered in control and Et white spruce at T0 and random at T8 (see Fig. S1a and b).
Table 2. Measures of the fungal phylogenetic structure recorded in organic and mineral soil samples associated with control and Et white spruce at T0 and T8
Standardized effect size of the mean pairwise distance
Control white spruce
Et white spruce
The PCoA (Fig. 2) showed the fungal community found in organic soil samples to be highly different from that found in mineral soil samples at T0. At T8, the PCoA showed the fungal communities from the two soil types to cluster together, independent of the level of transplantation treatment (no seedling, control and Et white spruce), except for the no seedling treatment in organic soil, which clustered with the fungal communities from organic soil at T0. The results from db-RDA showed that the fungal phylotypes recorded in soil samples associated with Et white spruce did not differ significantly (P>0.05) from the fungal phylotypes observed in soil samples associated with control white spruce.
The DPCoA showed that 88.7% of the fungal phylotype variability was explained by intralibrary differences for all libraries, either from control or Et white spruce, for both soil types. The fungal phylotype diversity estimated by the Rao diversity index was not affected by the presence of Et white spruce in the two soil types according to the anova. It was significantly higher in libraries from mineral soil than in libraries from organic soil (df=17; P=0.015). In the organic soil, the fungal community overlap (θ index, Table 1) between T0 and T8 in the control and Et white spruce treatments was c. 33%, while it was 65% between control and Et white spruce treatments at T8. In the mineral soil, the fungal community overlap between T0 and T8 in the control and Et white spruce treatments was c. 12%. At T8, the fungal community overlap between control and Et white spruce treatments was 64%.
This study aimed to examine the possible impact of Et trees on two soil fungal communities. Despite the fact that chitinolytic activity in Et white spruce increased 8.7 times compared with untransformed white spruce, the results showed that the two soil fungal communities considered were not significantly affected after 8 months. Soil fungal biomass, fungal OTU richness, Shannon and Rao diversity indices were not significantly different between control and Et white spruce. Investigation of the soil fungal phylogenetic structure by SESMPD analyses showed the structure of the fungal community to be similar in the rhizosphere associated with control and Et white spruce at T8. If endochitinase overexpression had affected some fungal taxa, the fungal species turnover would have resulted in two distinct phylogenetic structures between control and Et white spruce at T8. The results from the unifrac analysis showed the two fungal communities recorded in organic and mineral soils to be less divergent from each other at T8 than at T0, probably because of the soil colonization by common greenhouse fungi and mycorrhizal fungi associated with white spruce roots. This also suggests that the soil type and the presence of seedlings had a much more significant impact on fungal community structure than the insertion and expression of the ech42 transgene within the white spruce genome. This is not really surprising because mycorrhizae are a major constituent of the overall soil fungal community and it has been suggested that they might not persist long in the absence of their host (Hacskaylo, 1973; Harvey et al., 1980; Amaranthus & Perry, 1987). In our study, phylotypes of potential mycorrhizal fungi, identified as Wilcoxina, Clavulina, Rhizoscyphus, a Thelephoraceae and an Agaricales, were present in control and Et libraries at T0 and T8, but absent from the ‘no seedling’ libraries.
The results based on fungal biomass and observed fungal diversity are in accordance with previous studies investigating the potential impact of the constitutive overexpression of pathogenesis-related/antifungal proteins in genetically modified plants on nontarget fungi. Vierheilig et al. (1993) showed that chitinase-transformed Nicotiana sylvestris was equally colonized by the endomycorrhizal fungus Glomus mosseae 8 weeks after inoculation, while tobacco resistance against Rhizoctonia solani was enhanced compared with the control. Transformed aubergines (Solanum melongena) expressing Dm-AMP1 defensin protein within roots and root exudates showed a reduced growth of the phytopathogenic fungus Verticillum albo-atrum without consequence on root colonization by G. mossae (Turrini et al., 2004). Girlanda et al. (2008) did not find significant differences in the species richness of fungal rhizosphere and phyllosphere communities associated with glucanase- and chitinase-transformed tomato (Solanum lycopersicum) and wild-type plants after 2 and 8 months of interaction under greenhouse conditions. They showed that the establishment and development of endomycorrhizal symbiosis was similar between transgenic and control tomatoes.
Among the few studies that investigated more specifically the potential impact of transgenic trees producing exogenous antifungal protein on nontarget fungi, most of them did not observe any deleterious effect. Transgenic silver birch (Betula pendula) overexpressing sugar beet chitinase had similar levels of root colonization by the ectomycorrhizal fungus Paxillus involutus compared with untransformed silver birch (Pasonen et al., 2005). Nevertheless, these chitinase-expressing silver birches were shown to be more resistant to the leaf spot disease of birch caused by Pyrenopeziza betulicola (Pappinen et al., 2002). Vauramo et al. (2006) showed no negative effect on fungal biomass associated with decaying leaf litter by contrasting the ergosterol content of litters made of control and chitinase-expressing silver birch leaves after 8 months of decomposition in the field. Pasonen et al. (2009) showed that the mycorrhizal colonization rate of a transformed line of silver birch overexpressing chitinase 3.7 times compared with the control was similar to that of untransformed trees and other wild-type clones. They did not find evidence of a difference in the fungal structure community associated with transgenic and wild-type genotypes clearly related to the expression of sugar beet chitinase IV. Stefani et al. (2010) showed that an increase of up to 10 times in endochitinase levels within root tissues of transformed white spruce lines did not prevent the colonization and development of ectendomycorrhizal symbiosis by Wilcoxina sp. under greenhouse conditions. Moreover, they showed that the increased chitinase activity in transgenic root exudates had no effect on soil fungal biomass. Only one study reported a significant negative effect on root colonization of ech42-expressing apple trees by Glomus intraradices and G. mosseae (Schäfer et al., 2009).
Until now, many studies investigating the potential impact of transgenic plants on nontarget organisms have shown that plant genotype and environmental conditions account for stronger community shifts than transgene insertion and expression (Rasche et al., 2006; Bradley et al., 2007; Lamarche & Hamelin, 2007; Pasonen et al., 2009). According to the literature, fungal pathogens seem to more often be affected by chitinase overexpression in chitinase-transformed plants than mycorrhizal fungi and other soil fungi, such as saprotrophs. Contrasting susceptibilities to chitinolytic enzymes between fungi are not well known. Fungal cell walls are highly complex structures composed of lipids, proteins, polysaccharides and other substances such as aminopolysaccharides (e.g. chitin and chitosan), neutral polymers (e.g. cellulose, β-glucan, α-glucan, glycogen and mannan) and/or polyuronides (e.g. mucoran) (Bartnicki-Garcia, 1970). The mere presence of chitin in the cell wall is not a guarantee in itself that chitinase enzymes will successfully degrade the polymer. A study on the impact of T. harzianum endochitinase in transgenic tobacco on the survival of nematode (Meloidogyne hapla) eggs, mainly composed of chitin, did not show any difference between nontransgenic and transgenic lines (Brants et al., 2000). Another study comparing T. harzianum endochitinase activity on nine different fungi demonstrated different levels of antifungal activity (Lorito et al., 1993). Furthermore, it has already been demonstrated that rice chitinase exhibits different antifungal activities against four pathogenic fungi. This difference in antifungal activity was directly correlated to the surface microstructure and the proportion of chitin in the fungal cell wall (Yan et al., 2008), which differ considerably among fungal groups (Bartnicki-Garcia, 1968; Gooday, 1995).
The results presented here support the hypothesis that chitinase-transformed plants have no impact on fungal diversity as no significant difference was detected between control and Et white spruce in a greenhouse controlled experiment. Nevertheless, it could not be excluded that possible changes due to endochitinase overexpression in transgenic white spruce were too weak to generate detectable effects on soil fungal diversity. Therefore, the next step will be to investigate the potential effect of chitinase-transformed plants on rare fungal species through high-throughput sequencing to saturate soil fungal diversity and increase the level of detection of rare OTUs. It will also be interesting to focus more on the potential impact of chitinase-transformed plants on arbuscular mycorrhizae as conflicting results appear in the literature.
We thank A. Noël and C. Levasseur for providing the genetically transformed white spruce used in this study. We also thank M. Bernier-Cardou for helping us with statistical analyses, and I. Lamarre and Dr P. Tanguay for the revision of the manuscript. We also acknowledge those who developed the different analytical and statistical tools we used; their work is very valuable. This work was supported by grants from the Canadian Regulatory System for Biotechnology.
J.L. and F.O.P.S contributed equally to this work.