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    Soil Biology & Biochemistry 38 (2006) 23442349

    Principal component analysis and discriminant analysis (PCADA) for

    discriminating profiles of terminal restriction fragment length

    polymorphism (T-RFLP) in soil bacterial communities

    Sangkyu Parka, Youn Kyoung Kub, Mi Ja Seob, Do Young Kimb, Ji Eun Yeonb,Kyung Min Leeb, Soon-Chun Jeongb, Won Kee Yoonb,

    Chee Hark Harnc, Hwan Mook Kimb,

    aAjou University, 5 San, Wonceon-dong, Yeongtong-gu, Suwon, 443-749, South KoreabBio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, #52 Oun-dong, Yuseong-gu, Daejeon, 305-806, South Korea

    c

    Biotechnology Center, Nong Woo Bio Co., Ltd., 537-17 Jeongdan-ri, Ganam-myon, Yeoju-gun, Gyeonggi-do, South KoreaReceived 12 October 2004; received in revised form 24 January 2006; accepted 3 February 2006

    Available online 5 April 2006

    Abstract

    To assess the impact of a transgenic crop on soil environment, we compared soil bacterial communities from the rhizospheres of

    cucumber green mottle mosaic virus (CGMMV)-resistant transgenic watermelon (Citrullus vulgaris [Twinser] cv. Gongdae) and

    non-transgenic parental line watermelon at an experimental farm in Miryang, Korea. Soil microbial community structure was

    studied using terminal restriction fragment length polymorphism (T-RFLP) using HaeIII and HhaI enzymes on products from

    polymerase chain amplification reactions (PCR) of total DNA from rhizosphere. We used principal component analyses (PCA) to

    reduce dimensionality of T-RFLP profiles before comparison. On these PCA scores, we conducted discrimination analyses to

    compare soil microbial communities from the rhizosphere of transgenic and non-transgenic. Discriminant analyses indicatethat microbial communities from rhizosphere of transgenic and non-transgenic watermelon did not differ with significance at 95%

    level. Our study could be used as a model case to assess the environmental risk assessment of transgenic crops on soil microbial

    organisms.

    r 2006 Elsevier Ltd. All rights reserved.

    Keywords: Bacterial community; Rhizosphere; Transgenic watermelon; Citrullus vulgaris [Twinser] cv. Gongdae; T-RFLP; PCA; Discriminant analysis

    1. Introduction

    Soil is a complex and dynamic biological system and a

    major component in agro-ecosystems (Nannipieri et al.,2003). Crop plants interact with soil communities that crop

    species and microbial communities in the rhizosphere form

    strong links (Brimecombe et al., 2001). Recently, many

    transgenic crops have been introduced or ready to be

    introduced to agro-ecosystems all over the world (Nap

    et al., 2003). Transgenic crops can impact on agro-

    ecosystems and natural ecosystems through direct and

    indirect ways including gene flows, invasions and commu-

    nity/food web changes (Dale et al., 2002).

    Recently, we have been involved in the risk assessment of

    transgenic watermelon (Citrullus vulgaris [Twinser] cv.Gongdae) which was developed to have resistance to

    cucumber green mottle mosaic virus (CGMMV) infecting

    through soil. To assess environment risk of this transgenic

    watermelon, we attempted to develop a scientific method to

    examine the impact of transgenic crop on soil bacterial

    communities.

    In recent years, molecular techniques using polymerase

    chain reactions (PCR) of 16S ribosomal DNA such as

    amplified ribosomal DNA restriction analysis (ARDRA)

    (Massol-Deya et al., 1995) and terminal restriction

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    0038-0717/$- see front matter r 2006 Elsevier Ltd. All rights reserved.

    doi:10.1016/j.soilbio.2006.02.019

    Corresponding author. Tel.: +8242 8604660; fax: +82 42 8798669.

    E-mail address: [email protected] (H.M. Kim).

    http://www.elsevier.com/locate/soilbiohttp://localhost/var/www/apps/conversion/current/tmp/scratch462/dx.doi.org/10.1016/j.soilbio.2006.02.019mailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/current/tmp/scratch462/dx.doi.org/10.1016/j.soilbio.2006.02.019http://www.elsevier.com/locate/soilbio
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    fragment length polymorphism (T-RFLP) technique (Liu

    et al., 1997, Marsh, 1999) have become popular in

    determining whole soil microbial communities. T-RFLP

    evolved from several lines of techniques such as RFLP,

    PCR and nucleic acid electrophoresis and has several

    advantages over similar techniques based on 16S rDNA

    PCR. T-RFLP has greater resolution than denaturinggradient gel electrophoresis (DGGE) or temperature

    gradient gel electrophoresis (TGGE) and its output is

    digital profile which is easy for comparisons among

    different soil communities (Marsh, 1999).

    Principal component analysis (PCA) is often used to

    summarize T-RFLP profiles (Clement et al., 1998; Doll-

    hopf et al., 2001; Wang et al., 2004). However, PCA is

    generally not considered for statistical test between/among

    groups on a principal component space since PCA makes

    no prior assumption about the data structure (Thalib et al.,

    1999). General ways to discriminate groups using multiple

    observed variables are discriminant analysis (DA) and

    canonical variate analysis (CVA) that is multiple DA for

    more than 2 groups (Shaw, 2003). However, CVA cannot

    be applied to data sets where variable number exceeds the

    number of observations such as T-RFLP or chromato-

    graphic profiles (Thalib et al., 1999). Therefore, it is a

    general approach first to reduce dimensionality of high-

    dimensional spectral data such as metabolite profiles from

    GC-MS using several techniques including PCA, followed

    by DA or CVA (Kemsley, 1996; Mallet et al., 1996;

    Raamsdonk et al., 2001). Still it is not well established how

    to discriminate among T-RFLP data sets from different

    soil bacterial communities with statistical tests to provide

    more objective scientific basis for decision making inenvironmental risk assessment (Blackwood et al., 2003a, b;

    Grant and Ogilvie, 2003).

    Therefore, the objectives of our study were (1) to

    establish a procedure to conduct PCADA to discriminate

    T-RFLP profiles obtained from different soil bacterial

    communities and (2) to apply the procedure in comparison

    of soil bacterial communities associated with the rhizo-

    spheres of transgenic and non-transgenic watermelon to

    assess the risks of transgenic watermelon on soil environ-

    ments.

    2. Materials and methods

    2.1. Experimental design and soil sample collection

    The experimental site was set in a greenhouse at an

    experimental farm located in Miryang in Korea (1281470

    and 351300). Each treatment (transgenic and non-trans-

    genic C. vulgaris [Twinser] cv. Gongdae) has two replica-

    tion plots (3 5 m each) on which 57 watermelon plugs

    were planted. As plants grew 12 m long a month after

    planting, we collected soil from layers 515 cm deep around

    3 individual watermelon plugs for each plot.

    2.2. T-RFLP

    From soil in the rhizosphere of watermelon, total DNA

    was extracted for T-RFLP analyses using FastDNA SPIN

    KIT For Soil (Qbiogene, USA). The concentration of

    DNA was estimated using an UV spectrophotometer (Bio-

    Rad Smart Spec 3000). PCR were conducted usingextracted DNA (final concentration: 50 ng/50ml) as tem-

    plates to amplify 16S rRNA gene with fluorescence dye

    (FAM) labeled 8F-FAM (50-AGAGTTTGATCCTGGCT-

    CAG-30) and unlabeled 1492R (50-TACGGTTACCTTGT-

    TACGACTT-30) primers (Bioneer, Korea). The reactions

    were conducted using 50ml (final volume) mixtures

    containing 10 Taq buffer (Neurotics, Korea), 1 ml of

    each deoxyribonucleoside triphosphate (Promega, USA) at

    a concentration of 0.25 mM, 1 ml of each primer at a

    concentration of 10 pmol and 2 U ofTaq DNA polymerase

    (Neurotics Inc., Korea). Conditions for PCR were as

    follows: an initial denaturation step of 94 1C for 3 min, 25

    amplification cycles of denaturation (45 s at 941C);

    annealing (45 s at 55 1C); and elongation (2 min at 70 1C);

    and a final extension step of 7 min at 72 1C (Ritchie et al.,

    2000). We combined products from six PCR runs (total

    volume: 300ml), followed by purification using Qiaquick

    PCR purification kit (QIAGEN, Germany). One micro-

    gram of purified PCR products was digested with the

    restriction endonuclease HaeIII (Promega, USA) and HhaI

    (Promega, USA) at 371C for 2 h. The reactions were

    conducted using 20 ml (final volume) mixtures containing

    2ml of the 10 buffer, 2 ml of the 10 Bovine Serum

    Albumin Acetylated supplied by the manufacturer (Pro-

    mega, USA) and 1 ml of the restriction endonuclease (10 U).Digests (12 ml) were mixed with 12 ml of formamide and

    0.5ml of size standard (GeneScan-1000 ROX, Applied

    Biosystems). The samples were denatured at 96 1C for

    4 min and then placed on ice. Lengths of restricted

    fragments were determined by using automated ABI

    DNA sequencer (Model 3100, Applied Biosystems, USA)

    for 1 h 32 min 10 s. The fluorescently labeled 50-terminal

    restriction fragments were detected and analyzed by the

    GeneScan 3.7 (Applied Biosystems, USA) and with size

    markers ranged between 29 and 677 which covered most of

    the major T-RFs.

    2.3. Statistical analysis

    T-RF peaks identified by Genescan 3.7 software from

    individual T-RFLP profiles were compiled and aligned to

    produce large data matrices (12 observation 113 peak

    variables for each restriction enzyme). Centered T-RFLP

    profiles data were used in PCA without any further

    normalization. We assigned 0 if there was no matching

    peak. PCA were applied to the weighted covariance data

    matrices to reduce their dimensionality. Using un-rotated

    PC scores, we conducted DA on 2 groups (transgenic vs.

    non-transgenic) and 4 groups (transgenic plot 1 vs. trans-

    genic plot 2 vs. non-transgenic plot 1 vs. non-transgenic

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    plot 2). We examined variances of each mode using several

    selecting criteria including Scree test (Cattell, 1966),

    Kaisers criterion and Rule N (Overland and Preisendorfer,

    1982; Termonia, 2001) and chose subspace dimension (m)

    (Jassby, 2000). We checked for normality of data sets using

    KolmogorovSmirnov test. All statistical analyses were

    performed with S-Plus 6 for Windows (Insightful Corp.,

    USA).

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    Fig. 1. Results of T-RFLP in soil bacterial 16s rDNA from the rhizosphere of non-transgenic and CGMMV transgenic watermelon. One out of 3 T-RFLP

    profiles was shown for each plot. A and C are T-RFLP profiles with HaeIII restriction enzyme while B and D were with HhaI enezyme from the

    rhizoshpere of non-transgenic watermelon and transgenic watermelon. E and G are T-RFLP profiles with HaeIII restriction enzyme while F and H were

    with HhaI enzyme from the rhizoshpere of CGMMV transgenic watermelon.

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    3. Results

    3.1. T-RFLP profiles

    We obtained 24 T-RFLP profiles out of soil DNAs

    extracted from rhizosphere of CGMMV-resistant water-

    melon and non-transgenic parental line with HaeIII andHhaI enzymes (Fig. 1). Most T-RFs occurred between 50

    and 550 bp size range. After alignment of T-RFs, we could

    identify 113 different T-RF occurrences with both HaeIII

    and HhaI enzymes. T-RFLP profiles appeared to be

    different even among samples from the same plot.

    3.2. Principal component analyses of T-RFLP profiles

    PCA on T-RFLP profiles with HaeIII and HhaI enzymes

    calculated scores for HaeIII data set and HhaI data set

    (Fig. 2). Three principal components (PCs) explained

    71.2% of the total variability in the HaeIII data set while2 PCs explained 59.8% of the total variability in the HhaI

    data set (Fig. 2). PCA plots showed that there was no

    distinct separation between PCA scores of T-RFLP profiles

    associated with transgenic and non-transgenic watermelon

    (Fig. 2).

    3.3. Discriminant analyses on PCA scores

    DA on the PCA scores of T-RFLP profiles showed that

    PCA scores of 2 groups (transgenic vs. non-transgenic) did

    not differ with a statistical significance at 95% level for

    both HaeIII and HhaI data sets (Table 1). To examine the

    impact of variable number of PCs used for DA, we

    repeated DA with different number of PCs up to subspace

    dimension (m) but no significant difference was detected.

    We could observe increase of Hotellings T2, which meant

    lower probability for null hypothesis as number of PCs in

    DA increase. DA on PCA scores of each plot also showed

    no statistically significant differences among PCA scores

    from different plots (Wilks lambda 40.05) (Table 2).However, DA showed that there were considerable, still

    not significant at 95%, differences between PCA scores of

    T-RFLP profiles from different plots of transgenic water-

    melon (Table 2). With 3 PCs used in DA, probability of

    Hotellings T2 was 0.055 for LM1LM2 comparison which

    was almost significant at 95% level. As in 2-group

    comparison, we also observed the pattern of lower

    probability for null hypothesis with increasing number

    of PCs.

    4. Discussion

    Our results demonstrated that PCADA approach on T-

    RFLP profiles could make a statistical hypothesis test with

    a conclusion that soil bacterial communities associated

    with transgenic and non-transgenic watermelon were not

    different at 95% significance level. Recent progresses in

    metabolic profiling and chemometric data analysis area

    suggest that dimensions of any electrochemical data such

    as GC/HPLC chromatogram may be reduced using multi-

    variate tools such as PCA and then scores of PCA may be

    further analyzed by statistical test such as DA (Raamsdonk

    et al., 2001; Charlton et al., 2004). Our study adopted

    PCADA approach on T-RFLP profile data to decide

    whether soil bacterial communities associated with trans-genic and non-transgenic watermelon differ with statistical

    ARTICLE IN PRESS

    Fig. 2. PCA results extracted from T-RFLP profiles of soil bacterial

    community from the rhizosphere with HaeIII treatment (A) and with

    HhaI treatment (B). Only the first principal component (PC1) and the

    second principal component (PC2) were shown. Closed circles indicate T-

    RFLP profiles from parental watermelon plot 1 while closed squares for

    parental line plot 2. Open circles indicate T-RFLP profiles from transgenic

    watermelon plot 1 while open squares indicate for transgenic watermelon

    plot 2.

    Table 1

    Results from DA on PCA scores of two groups ( transgenic vs. non-

    transgenic) extracted from T-RFLP results using HaeIII and HhaI

    enzymes

    Number of PCs HaeIII HhaI

    Hotellings T2 p Hotellings T2 p

    2 0.0714 0.9316 2.0904 0.1796

    3 0.9866 0.4464

    Number of PCs indicates the number of PCs used in DA.

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    significance. In particular, our study focused on how many

    PCs should be used for DA.

    We detected a trend that increasing PC number used in

    DA increased the probability of rejecting null hypothesis

    (Tables 1 and 2). In addition, our data which were not

    shown in this paper revealed that p value for Wilks lambda

    decreased to 0.016 with 4 PCs used for DA in 4-group

    comparison for HaeIII data set. We interpret this trend as a

    result of overfitting (Defernez and Kemsley, 1997).

    Defernez and Kemsley (1997) suggest that overfitting

    should be strongly suspected when the number of PCs

    used in DA exceeds (ng)/3, where n is number ofobservation and g is the number of groups. According to

    them, our analyses might suffer from overfitting if we use 4

    PCs for comparison of 2 groups or 3 PCs for comparison

    of 4 groups. In this context, our 4-group comparison using

    3 PCs for HaeIII data set (Table 2) are not entirely free

    from overfitting concerns. Obviously, it is recommended to

    increase the number of observations to make analyses out

    of overfitting problem. There are trade-offs for us to decide

    how many PCs to be used for DA. The more PCs we use

    for DA, the higher the power of discrimination is while the

    analyses becomes more susceptible to overfitting problem.

    Regarding the number of PCs for DA, we recommend to

    use PCs below the threshold from Defernez and Kemlsy

    rule ((ng)/3) within subspace dimension (m), which can be

    determined by Scree test and Rule N.

    Recently, there has been an issue on how to analyze T-

    RFLP profile data (Blackwood et al., 2003a, b; Grant and

    Ogilvie, 2003). Blackwood et al. (2003a) used cluster

    analysis to present different microbial communities with

    redundancy analysis for statistical significance test on

    which Grant and Ogilvie (2003) commented that ordina-

    tion methods may be better tool than cluster analysis when

    data have no strong structure defined a priori. In

    experiments for environment risk assessment on transgenic

    crops where transgenic and non-transgenic crops and their

    impacts are usually compared, we would not assume that

    transgenic crops are different before we conduct an

    evaluation. Since cluster analysis is, in essence, for

    classification which is based on differences among groups,

    we prefer to use ordination techniques such as PCA to

    examine whether transgenic and non-transgenic crops are

    significantly different.

    Although we showed that soil bacterial communities

    associated with transgenic and non-transgenic watermelon

    did not differ significantly, our study only focused on

    bacterial communities at a given time in a year. Since soil

    communities are very dynamic and complex with manydifferent groups of organisms, it is necessary to extend our

    assessment with longer time scale (seasonal succession) and

    wider organisms such as fungi and nematodes to provide

    better scientific basis for environment risk evaluation

    process.

    In conclusion, we showed an established PCADA

    procedure can be applied to T-RFLP profile data with

    recommendations on the number of PCs in DA. It might be

    possible to apply our procedure to similar data set with

    high dimensions (many variables) such as chromatogram/

    electrospectrum type data. Our approach would be a useful

    step for decision in environment risk assessment before

    release of transgenic crops to environment.

    Acknowledgments

    This research was supported by grants from KRIBB

    Research Initiative Program, Crop Functional Genomics

    Center and by a program for development of risk

    assessment technologies of LMOs from the Korea Institute

    of Science and Technology Evaluation and Planning

    (KISTEP). We would like to thank to Sang Mi Park, Sang

    Lyul Han and Yoon Sup Shin at Miryang Station of Nong

    Woo Bio Co. for cultivating watermelon.

    ARTICLE IN PRESS

    Table 2

    Results of DA on PCA scores of each plot (LM1 vs. LM2, vs. WT1 vs. WT2) from PCAs of T-RFLP profiles using HaeIII and HhaI enzymes

    PC no. HaeIII HhaI

    Lambda p Group T2 p Lambda p Group T2 p

    2 0.469 0.422 LM1LM2 2.497 0.152 0.280 0.122 LM1LM2 4.121 0.066

    LM1WT1 0.261 0.778 LM1WT1 1.137 0.374LM1WT2 2.333 0.167 LM1WT2 3.422 0.092

    LM2WT1 1.197 0.357 LM2WT1 1.941 0.214

    LM2WT2 0.117 0.891 LM2WT2 1.758 0.241

    WT1WT2 1.206 0.354 WT1WT2 0.615 0.568

    3 0.179 0.179 LM1LM2 4.529 0.055

    LM1WT1 0.153 0.924

    LM1WT2 1.375 0.338

    LM2WT1 3.577 0.086

    LM2WT2 2.492 0.157

    WT1WT2 0.710 0.581

    PC no. indicates number of principal components (PC) used in DA. Lambda stands for Wilks lambda while T2 for Hotellings T2. LM indicates plots with

    CGMMV resistant transgenic watermelon while WT indicates plots with non-transgenic watermelon.

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