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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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doi:10.1016/j.soilbio.2006.02.019

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

E-mail address: hwanmook@kribb.re.kr (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:hwanmook@kribb.re.krmailto:hwanmook@kribb.re.krhttp://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

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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.

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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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