Dynamics of microbial community structure and cellulolytic

Original article
Dynamics of microbial community structure and cellulolytic
activity in agricultural soil amended with two biofertilizers
Yong Zhao a,c
, Wu Li b
, Zhihua Zhou b
, Linghua Wang b
,Yingjie Pan c
, Liping Zhao b,
*
a
Department of Microbiology, Nanjing Agricultural University, Nanjing, Jiangsu Province 210095, China
b
Laboratory of Molecular Microbial Ecology and Ecogenomics, Department of Bioscience and Biotechnology,
College of Life Science and Biotechnology, Shanghai Jiao Tong University, #800 Dongchuan Road, Shanghai 200240, China
c
College of Food Science, Shanghai Fisheries University, Shanghai 200090, China
Received 4 November 2004; accepted 23 March 2005
Available online 09 June 2005
Abstract
Changes in the soil microbial community structure and cellulolytic activity may reflect the effects of different amendment or
management strategies. In this study, cellulolytic activity dynamics and microbial community structure in an agricultural soil
undergoing treatment-induced cellulose decomposition in response to two commercial biofertilizers (G andY) were investigated
under laboratory conditions. The rate of weight loss among filter paper strips buried in G-treated soil was significantly higher
than in untreated control soil (R), while that in Y-treated soil lower. A significant shift in the PCR-temperature gradient gel
electrophoresis (PCR-TGGE) fingerprints of fungal community members was observed during the process, while no dramatic
changes were observed in the bacterial community structure. The ITS3–4 sequence of one predominant TGGE band in a sample
from G-amended soil during the peak of cellulose decomposition was most similar to that of a wood-decaying species Meruli-
poria incrassata. Fungal species composition of the same sample was analyzed by clone library profiling and was found to differ
significantly from that of its parallel control sample. Several operational taxonomic units (OTUs) in a G-amended soil sample,
including the species represented by the predominant TGGE band, were suggested to be cellulose decomposing fungal species.
The data of this study demonstrate that structural shifts in the soil fungal community for cellulose degradation represent a
meaningful ecological indicator of the consequences of soil amendments with biofertilizers.
© 2005 Elsevier SAS. All rights reserved.
Keywords: Cellulolytic activity; Temperature gradient gel electrophoresis (TGGE); Clone library profiling; Microbial community; Biofertilizer
1. Introduction
Microbiological and biochemical properties of soil have
often been proposed as early and sensitive indicators of
anthropogenic effects on soil ecology, both in natural and
agroecosystems [1,39]. Microbial organisms involved in cel-
lulose decomposition comprise one of the most important
microbial soil groups, because they improve soil health and
quality through decomposition and transformation of organic
matter [18,32], and affect the function of other soil microbes
by supplying a carbon source [38]. Thus, microbial cellu-
lolytic activities in soil are important indicators of soil health
and quality [35].
Measuring cellulolytic activity in situ is one of the pri-
mary methods for determining microbial activity during cel-
lulose decomposition [29]. One mode of determining cellu-
lolytic activity is to introduce filter paper, cellophane or
unbleached cotton strips into the soil [6,17,19]. The cellu-
lolytic activity can be determined either by a decrease in the
cellulose weight or by a change in the tensile cloth strength
[29].
* Corresponding author. Tel.: +86 21 5474 3351 (O), 5474 4263
(Lab); fax: +86 21 5474 3348.
E-mail address: lpzhao@sjtu.edu.cn (Y. Zhao).
European Journal of Soil Biology 41 (2005) 21–29
www.elsevier.com/locate/ejsobi
1164-5563/$ - see front matter © 2005 Elsevier SAS. All rights reserved.
doi:10.1016/j.ejsobi.2005.03.002

Variation within a cellulolytic microbial community
reflects changes in microbial activity, and thus is an impor-
tant indicator of cellulose decomposition. Knowledge of
microbial succession on cellulose is particularly key to under-
standing the microbial aspects of residue decomposition [11].
A few studies have focused on the microbial dynamics of
cellulose decomposition [11,28]. Though most soil microor-
ganisms are not cultured as such [2], these studies used mostly
conventional culture-dependent methods.
Molecular techniques based on DNA analysis are expected
to circumvent the problems associated with culture-dependent
methods, and are gaining popularity for elucidating micro-
bial population structures and dynamics in environmental
samples [2,24]. Molecular fingerprinting techniques such as
temperature and denaturing gradient gel electrophoreses
(TGGE and DGGE) are powerful tools for investigating
microbial diversity in a wide range of samples [9,21,23,25,44].
Cloning and sequencing may allow us to analyze phyloge-
netic community member types in various environments
[13,24,34]. For example, Weber et al. [40] used molecular
methods to examine bacterial populations that colonized and
degraded rice straw.
Biofertilizers may contain several microbial species that
benefit soil health and quality, plant growth and suppress soil-
borne plant pathogens [43]. Thus, biofertilizers are expected
to be ideal supplements to standard chemical fertilizers [27].
Currently, many biofertilizer products are marketed in China.
However, the use of biofertilizers may alter soil microbial
community structure and function, as well as microbial activ-
ity [10], thus necessitating an in-depth examination of biof-
ertilizer impact.
In the present study, the composition and dynamics of cel-
lulolytic microbial communities were analyzed using DNA-
based approaches, and cellulolytic activity was measured by
the weight loss of buried filter paper strips. The study objec-
tives were (1) to document the effects of two biofertilizers (G
and Y) on the rate of cellulose decomposition and microbial
community structure; (2) to examine the relationship between
the rate of cellulose decomposition and the variation in micro-
bial community structures; (3) to detail microbial commu-
nity composition in treated soils when the rate of cellulose
decomposition is highest.
2. Materials and methods
2.1. Soil sampling and treatments
The original soil used in this study was sampled from the
top layer (0–10 cm) in a field plot located at Shanghai Jiao
Tong University Experimental Farm (Shanghai; 121°24′E,
31°0′N), was characterized as silty-loam (WHC 45%, pH 5.8)
and was never exposed to biofertilizer. Freshly collected soil
samples were pooled and gently crushed to a size that would
passage through a 2 mm sieve. The sieved samples were then
stored for up to 48 h at room temperature until use. The soil
samples were then divided into four quarters of approxi-
mately 6 kg each. One quarter was mixed with 500 ml biof-
ertilizer G diluted 200-fold in sterile water (the recom-
mended field dose) (G). One quarter was treated with 500 ml
biofertilizer Y diluted 300-fold in sterile water (the recom-
mended field dose) (Y). The third quarter of the sample was
used as an absolute control (A) and was treated by autoclave
at 121 °C for 20 min, then mixed with 500 ml sterile water.
The last quarter of the sample was used as a relative control
(R) and was mixed with 500 ml sterile water.
Two brands of commercial biofertilizer frequently used in
farms around Shanghai were selected for this study. Biofer-
tilizer G was produced by Chendu Nengsheng Bioengineer-
ing Co., Ltd. (Sichuan Province, China) and was character-
ized as being a yellow–brown liquid at pH 3.5. Biofertilizer
Y was produced byYiyijiu Bioengineering Co., Ltd. (Shang-
hai City, China) and was characterized as a light-yellow liq-
uid at pH 2.5. Both biofertilizers contained living microbial
organisms beneficial to crop growth according to their prod-
uct labels. Analyses of the temperature gradient gel electro-
phoresis (TGGE) performed on the V3 region and of DNA
sequencing of the 16S rRNA gene indicated that biofertilizer
G contained five bacterial species related to Pseudomonas
sp., Lactobacillus sp., Enterococcus sp., Streptococcus sp. and
Bacillus mucilaginosus (sequence accession number in Gen-
Bank: AY944300-AY944304), and Biofertilizer Y contained
two bacterial species related to Lactobacillus sp. and Bacil-
lus sp. (sequence accession number in GenBank: AY944305,
AY944306). No fungal organisms were identified in both biof-
ertilizers by PCR amplification of ITS3–4 regions of fungi.
DNA sequencing analysis was performed with BLAST
(Entrez, NIH).
A sterile dried filter paper strip (3 cm × 7 cm in size) was
weighed and then buried in 100 g treated soil loaded onto a
sterile plate (diameter 9 cm). Sixty replicates were prepared
for each treated soil sample. A total of 240 plates were incu-
bated at 20 °C in the dark. The soil moisture content was
adjusted to 80% of field capacity before loading onto the plates
and was maintained by daily addition of sterile deionized
water. During the incubation period, six replicate plates of
each treated soil sample were removed from the incubator
every 5 days for cellulolytic activity and microbial commu-
nity analysis.
2.2. Measurement of cellulolytic activity
Cellulolytic activity was represented by the cellulose
decomposition rate, which is the percentage of dry weight
loss of the buried filter paper strips after each incubation
period. For each treatment, the mean weight loss was calcu-
lated for the six replicates sampled at each time point. T-tests
were performed using Microsoft Excel 2000 software (Mi-
crosoft Corporation, Washington) to estimate the significant
differences among the four treatment regimes on soil cellu-
lolytic activity. Statistical significance was determined at the
0.05 level (P < 0.05).
22 Y. Zhao et al. / European Journal of Soil Biology 41 (2005) 21–29

2.3. Extraction and purification of DNA from soil samples
The soil samples were removed from the six replicate fil-
ter paper strips and were pooled and sub-sampled. A total
DNA sample was then obtained as follows: 500 mg of sub-
sampled soil was added to a 10 ml polypropylene tube with
350 mg sterile glass beads (diameter 1 mm) and 2 ml DNA
extraction buffer (100 mM Tris; 100 mM EDTA; 200 mM
NaCl; 2% PVPP; 3% CTAB; pH 9.0). The tube was vortexed
for 10 min, after adding 500 µl lysozyme (100 mg ml−3
). The
tube was vortexed again for 5 min and then incubated at 37 °C
for 30 min. After the incubation period, the tube was vor-
texed for 5 min and 2 ml SDS buffer (100 mM Tris; 200 mM
NaCl; 3% SDS; pH 9.0) was added. The tube contents were
mixed by inverting five times, and then incubated at 65 °C
for 30 min. The supernatant was collected after centrifuga-
tion at 6000 × g for 10 min at room temperature and trans-
ferred into a new 10 ml centrifuge tube. The supernatants
were extracted with an equal volume of chloroform/
isoamyl:iso-amyl alcohol (24:1, v/v). The aqueous phase was
recovered by centrifugation at 16,000 × g for 20 min and pre-
cipitated with 0.6 vol of isopropanol and 0.1 vol of 3 M NaAc
at room temperature for 1 h. The pellet of crude nucleic acids
was obtained by centrifugation at 16,000 × g for 20 min at
room temperature, washed with cold 70% ethanol, and resus-
pended in sterile deionized water, to yield a final volume of
500 µl.
Crude DNA extracts were purified with 1 vol of phenol,
then passed through a Biocolor 3S DNA purification column,
according to the manufacturer’s instructions (Shanghai Bio-
color Biotechnology Co., Ltd., Shanghai, China), and stored
at –20 °C until used.
2.4. Polymerase chain reaction
For TGGE analysis, a primer pair of P2 (ATTACCGCG-
GCTGCTGG) and P3-GC (CGCCCGCCGCGCGCGGC-
GGGCGGGGCGGGGGCACGGGGGGCCTACGGGAGG-
CAGCAG) was used to amplify variable region three (V3) of
the 16S rRNA bacterial genes [22]. A primer pair of ITS3
(GCATCGATGAAGAACGCAGC) and ITS4-GC (CGCC-
CGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGG-
GGGTCCTCCGCTTATTGATATGC) was used to amply
ITS3–4 regions of fungi [41]. Each 25 µl PCR reaction con-
tained a final concentration of the following reagents: 10 ng
of purified genomic DNA, 100 pM of each of the primers,
200 µM dNTPs, 1 × Taq Reaction Buffer, 1.3 mM MgCl2,
and 1 unit Taq Polymerase (Promega Corporation, USA). A
Hybaid PCR Express thermal cycler (Hybaid Limited, Ash-
ford, UK) was used in the PCR amplifications. ForV3 region,
the amplification was carried out following a ‘touchdown
PCR’ process [22]. For ITS3–4 regions, amplified program
was as follows: one cycle of 94 °C for 3 min; 94 °C for 45 s,
51 °C for 45 s, and 72 °C for 1 min (30 cycles); and a final
extension at 72 °C for 6 min.
For the clone libraries to analyze fungal community, the
PCR conditions were the same as those described above but
using primer pairs without the GC-clamp ITS3 and ITS4.
2.5. TGGE analysis
Before TGGE analysis, each PCR product was recondi-
tioned for five cycles to reduce single-stranded and heterodu-
plex DNA [33]. A 3 µl reconditioned sample (approximately
50 ng of DNA) was mixed with 5 × gel loading dye and loaded
on an 8% (w/v) polyacrylamide, 7 M urea, 20% formamide
gel and subjected to electrophoresis using Biometra TGGE
Maxi for 3.5 h at a constant voltage of 200 V in 1 × TAE
running buffer. Electrophoresis was carried out over a tem-
perature range of 38–54 °C for bacteria and 35–48 °C for
fungi. The gel was then subjected to silver staining [4]. The
stained gels were immediately photographed using a digital
camera DSC-F717 (SONY, The Japanese). The digital finger-
printing images were analyzed using the UVIBAND/MAP
V.99 software (UVItec Limited, UK). The Dice similarity
coefficients among the TGGE patterns were calculated
according to Sneath and Sokal [31]. Cluster analysis of data
and generation of dendrogram were performed using the Clus-
ter program (software developed by UVItec Limited, UK).
Bands, marking the different structural characteristics
between the treatments in TGGE fingerprinting, were excised,
purified and re-amplified for further sequencing analysis with
a clone library approach in which five clones were selected
to sequence for each band.
2.6. Clone libraries and sequence analysis
Clone libraries consolidating the amplified ITS3–4 prod-
ucts representing significant fungal structural shifts at time
points with the highest cellulose degradation were con-
structed for the samples collected at 20 days from G treat-
ment (G20) and R control (R20). The PCR products were
ligated into pGEM-T Easy Vector according to the manufac-
turer’s instructions (Promega, Madison, WI) and were then
transformed into E. coli DH5a. The resulting clones were
screened by insert length. Positive clones were sequenced with
an automatic sequencer (ABI PRISM 377 DNA Sequencer;
PE Biosystems, Foster City, USA) by Shanghai BioAsia Bio-
technology Co., Ltd.
The sequences with a similarity of greater than 99% were
regarded as being of the same OTU. The closest matches of
single-sequenced OTUs were determined in the EBI/NCBI
database by FASTA3 (European Bioinformatics Institute,
UK).
The clone library coverage was calculated using the fol-
lowing equation:[30]:
C=关1 − 共 n/N 兲兴×100%
where n is the number of unique sequences and N is the total
number of sequences.
2.7. Nucleotide sequence accession numbers
The nucleotide sequences were submitted to the Gen-
Bank database through NCBI under the following accession
numbers: AY704731–AY704761.
23
Y. Zhao et al. / European Journal of Soil Biology 41 (2005) 21–29

3. Results
3.1. Cellulolytic activity
Weight loss recorded for the filter paper strips buried in
soil A was less than 1% while more dramatic weight losses
were observed in the other three treated soils (G,Y, R) (Fig. 1).
The results indicated that cellulose decomposition in the soil
samples was mainly a biological process rather than the result
of abiotic actions. Sub-samples from treatment A were
excluded from further DNA analysis.
The decomposition rate in the other three treated soils (G,
Y, R) was slow for the first 5 days, accelerated from 6th to
20th days and then began declining (Fig. 1). The filter paper
strips were decomposed up to 80% after 50 days. During the
decomposition process, G showed the highest cellulolytic
activity while R was ranked as second andY as third. T-tests
indicated that G significantly increased the cellulolytic activ-
ity of agricultural soil during the first 40 days of decomposi-
tion (*P < 0.05). In contrast,Y significantly restrained cellu-
lolytic activity (*P < 0.05). After 40 days, there was no
significant difference in cellulolytic activity between G and
R (P > 0.05).
3.2. Changes of microbial community structure analyzed
by PCR-TGGE
The DNA extraction method in present study yielded up
to 10 µg of high molecular weight (approximately 23 kb) DNA
per g of dry soil. Prior to PCR amplification, DNA samples
were phenol extracted and purified over a DNA purification
column (Shanghai Biocolor Biotechnology Co., Ltd., Shang-
hai). TGGE profiles of bacterial and fungal community in the
three treated soils showed high reproducibility within repli-
cates, indicating that DNA samples were of high quality for
further analysis.
No dramatic changes were observed in the TGGE profile
of 16S rDNA V3 regions of all the DNA samples (Fig. 2A).
There were on average ca. 24 bands for each lane (Fig. 2A).
Though there was some variation among a few of the faint
bands, no differences were observed among any of the DNA
samples for the most intense 13 bands (Fig. 2A). Similarity
coefficients among all of the DNA samples reached 90%
(Fig. 2B). The above results indicate that the bacterial com-
munity structure in the tested soil did not change dramati-
cally with amendments by the two biofertilizer types during
the incubation period.
Significant variations were found among the TGGE pro-
files of the ITS3–4 region for fungal members of all the DNA
samples (Fig. 3A). The similarity coefficients ranged from
11% to 83%. The similarity values among samples collected
at the same time tend to be higher than those collected at
different times, as shown by the cluster analysis (Fig. 3B).
The similarity coefficients of the fungal community among
the three treatments were higher at the beginning of the cel-
lulose decomposition (during 5 days after the cellulose filter
paper strips had been buried), then decreased during the
10–20 days period, and increased again after being buried for
more than 30 days (Fig. 3B).
Fig. 1. Effects of different amendment on the cellulose decomposi-
tion, estimated from the weight loss of cellulose filter paper strips
buried in soil. G, Y, R, A represent G amendment, Y amendment,
relative control and absolute control, respectively. Error bars indi-
cate S.E. of six replicates.
Fig. 2. TGGE profiles of amplified V3 region of 16S rDNA fragments representing the bacterial community in different amended soil samples
(A) and cluster analysis result (B). G5,Y5, R5 represent G,Y, R amended for 5 days, respectively; G10,Y10, R10 represent G,Y, R amended for
10 days, respectively; G30,Y30, R30 represent G,Y, R amended for 30 days, respectively; G50,Y50, R50 represent G,Y, R amended for 50 days,
respectively.

No intense bands were detected in the TGGE profile of
the three samples collected after the cellulose filter paper strips
were buried for 5 days, however, an intense band (Band 1)
appeared in the soil treated withY biofertilizer after the paper
strips were buried for 10 days (Fig. 3A).Another intense band
(Band 2) appeared in G-treated soil after incubation for
20 days, and several intense bands (Bands A–C) appeared in
all three of the treated soil samples (indicated with arrows in
Fig. 3A).After incubation for 20 days, some of the faint bands
disappeared. In addition, specific intense bands appeared for
G,Y and R treated for 30 days (Bands D–F, respectively, indi-
cated with arrows in Fig. 3A).
3.3. Phylogenetic analysis of fungal species represented by
Band1 and Band2
Bands 1 and 2 (Fig. 3A) were excised from the TGGE gel,
re-amplified and cloned. The amplified inserts of five selected
white clones from each band-clone library maintained their
original TGGE patterns. The sequences of the five selected
clones were identical. According to the closest sequence
homology, the fungus represented by Band1 (AY704761)
matched to a plant pathogen fungus, Peniophora aurantiaca,
with a similarity of 66%, while Band 2 (AY704735) corre-
sponded to a wood-decaying fungus species, Merulipora
incrassata, with a similarity of 63%.
3.4. Phylogenetic analysis of fungal community
composition of samples G20 and R20
The fungal community composition in sample G20 that
showed the highest cellulose decomposition activity was com-
pared to that of its parallel control sample R20 by clone library
analysis. Forty and 32 clones were selected and sequenced
from clone libraries G20 and R20, respectively. In total, 30 dif-
ferent OTUs, denoted OTU-ZY01 to OTU-ZY30, were iden-
tified with a similarity cutoff of 99%. The screening cover-
age for the G20 and R20 libraries reached to 70% and 68.75%,
respectively.
The relative abundance of each OTU in the two libraries
is shown in Fig. 4. OTU-ZY01 to ZY04 were common to
both G20 and R20 libraries. OTU-ZY05 to ZY19 were spe-
cific to the G20 library, and OTU-ZY20 to ZY30 were spe-
cific to the R20 clone library. Most of the OTUs only appeared
in single clones. However, sequences of OTU-ZY01 to
ZY08 and OTU-ZY20 to ZY21 were found in more than one
clone, marking the largest structural shift between the two
fungal communities.
Table 1 presents the fungal species that were the most
closely matched to the 30 sequenced OTUs from libraries
G20 and R20. Among the most highly matched fungal spe-
cies, with the exception of several unknown fungi (OTU-
ZY19, ZY21, ZY30), most were soil-borne fungi (Table 1).
Fig. 3. TGGE profiles of amplified ITS3–4 region fragments representing the fungal community in different amended soil samples (A) and
cluster analysis result (B). G5,Y5, R5 represent G,Y, R amended for 5 days, respectively; G10,Y10, R10 represent G,Y, R amended for 10 days,
respectively; G20, Y20, R20 represent G, Y, R amended for 20 days, respectively; G30, Y30, R30 represent G, Y, R amended for 30 days,
respectively.
Fig. 4. The relative abundance and distribution of 30 OTUs in G20 and
R20 clone library. G20 and R20 represent the soil samples of G, R
amended for 20 days, respectively.
25

Many species (matching OTU-ZY02, ZY03, ZY08, ZY12,
ZY16, ZY18, ZY20, ZY22, ZY26 in identity) were plant-
root pathogen or soil nematophagous fungi; several (OTU-
ZY01, ZY25, ZY29) were function-unknown soil fungi; some
(OTU-ZY04, ZY05, ZY06, ZY09, ZY10, ZY14 and ZY17)
may be related to cellulose decomposition. OTU-
ZY04 matched to a xylanase-producing fungus Chaetoi-
mium globosum, with a similarity of 96%; OTU-ZY05 and
ZY09 both correlated to a wood-decaying fungus, Merulipo-
ria incrassate, with the same similarity of 63%. OTU-
ZY06 matched with 91% similarity to Ascobolus immerses,
a species that may utilize fibrous residues in the feces of her-
bivorous animals. OTU-ZY10 was found to be 93% similar
to a xylanase-producing thermophilic fungus isolated from
Japanese soil, Scytalidium thermophilum. OTU-ZY14
matched to a leaf litter ascomycete strain, with a similarity of
96%. OTU-ZY17 corresponded to the cellulose- and xylan-
utilizing species Inocybe nitidiuscula, with a similarity of
83%. The sequence of OTU-ZY05 in library G20 was con-
sistent with that of Band2 in sample G20.
Table 1
Species of fungi with ITS3–4 sequences most similar to 30 OTUs from G20 and R20 clone libraries a
OTU numbers Fragment size
(bp)
Accession numbersb
Fungal species with most similar sequence Similarity
(%)
Ascomycota
ZY10 338 AY704740 Scytalidium thermophilum (AB085927) 93
ZY14 350 AY704744 Leaf litter ascomycete strain its267 (AF502791) 96
ZY24 331 AY704754 Tricladium splendens (AY204636) 97
ZY25 340 AY704755 Salal root associated fungus UBCTRA1522.5 (AF284133) 87
Orbiliomycetes
ZY02 372 AY704732 Arthrobotrys amerospora (AF106533) 92
Pezizomycetes
ZY06 341 AY704736 Ascobolus immersus (AJ271628) 91
Sordariomycetes
ZY04 339 AY704734 Chaetoimium globosum (AY429056) 96
ZY07 393 AY704737 Cordyceps gunnii (AJ536551) 74
ZY11 353 AY704741 Bionectria ochroleuca (AJ509863) 98
ZY12 369 AY704742 Verticillium dahliae (AF104926) 91
ZY20 435 AY704750 Cf.Verticillium sp. 254/HP3 (AY172097) 96
ZY22 337 AY704752 Fusarium oxysporum (X78259) 100
ZY28 349 AY704758 Mycoleptodiscus terrestris (U97332) 99
Dothideomycetes
ZY08 346 AY704738 Alternaria mali (AY154683) 100
ZY15 346 AY704745 Cladosporium elatum (AF393699) 89
ZY16 344 AY704746 Didymella cucurbitacearum (AY293804) 99
ZY26 342 AY704756 Didymella cucurbitacearum (AY293804) 98
Eurotiomycetes
ZY13 334 AY704743 Geomyces sp. T489/9b (AY345348) 100
ZY27 353 AY704757 Penicillium brocae (AF484397) 97
Leotiomycetes
ZY23 333 AY704753 Chalara sp. LL-16.3 (AY188359) 92
Basidiomycota
ZY19 378 AY704749 Basidiomycete isolate wb436 (AF461413) 67
ZY21 400 AY704751 Basidiomycete from a bamboo (U65614) 63
ZY30 377 AY704760 Basidiomycete isolate wb436 (AF461413) 66
Basidiomycetes
ZY01 394 AY704731 Uncultured basidiomycete clone d484.32 (AY254866) 95
Hymenomycetes
ZY03 413 AY704733 Ceratobasidium sp. AGO (AF354094) 99
ZY05 402 AY704735 Meruliporia incrassata (AJ419913) 63
ZY09 397 AY704739 Meruliporia incrassata (AJ419913) 63
ZY17 407 AY704747 Inocybe nitidiuscula (AJ534934) 83
ZY18 415 AY704748 Ceratobasidium sp. CAG4 (AF354081) 99
ZY29 388 AY704759 Salal root associated fungus UBCTRA1041.2 (AF284135) 88
a
Sequences were compared to those in EBI and NCBI database.
b
Sequence were submitted to NCBI database.

4. Discussion
4.1. Cellulolytic activity effects during cellulose
decomposition processes in soils treated with biofertilizers
In this study, we found that soil cellulose decomposition
is a biotic process in which microbial organisms are involved
[7,29]. Though cellulose decomposition in the three treated
soils (G, Y, R) almost reached 80% after incubation for
50 days, their decomposition rates and cellulolytic activities
were different from each other throughout the incubation
period. For example, 60% of the cellulose was decomposed
in G-treated soil after the filter paper strip was buried for over
20 days, but the same amount of cellulose decomposition
required more than 30 days in R treatment and nearly 45 days
in Y treatment (Fig. 1). T-tests indicated that the differences
in cellulolytic activity among the three treatments were sig-
nificant (*P < 0.05). The above results indicate that biofertil-
izers had significantly different effects on cellulolytic activ-
ity in agricultural soils, suggesting that the soil microbial
community structure might also be affected by the biofertil-
izers during cellulose decomposition.
4.2. Effect on microbial community structure during
the cellulose decomposition process
The TGGE profiles of soil bacterial communities were sig-
nificantly different from those of the two biofertilizers (data
not shown), indicating that there was no contamination of the
soil bacterial TGGE profiles with the bacteria from the biof-
ertilizers. Furthermore, TGGE profiles illustrating bacterial
communities by banding patterns showed no dramatic
changes, as demonstrated in Fig. 2A (see the 13 intense bands
that were observed throughout the incubation period, regard-
less of the treatment). Similar results were determined for
other studies [8,14,26]. In previous studies, herbicide treat-
ments or different management styles were found to have no
dramatic effect on the soil bacterial community, as deter-
mined by PCR-based methods. PCR-TGGE fingerprinting
and clone library profiling, however, have revealed dramatic
changes among fungal communities (Fig. 3A and Fig. 4). The
changes might be owing to the different effects on soil fungal
community by the two biofertilizers, because no fungal organ-
isms were detected in either of the biofertilizers, and the origi-
nal soil used in the different treatments were identical. Wu
[42] also found that Bt-transgenic rice straw had a significant
effect on fungal diversity in agricultural soil. Hunt et al. [12]
found that different agricultural management had an effect
on below-ground fungal communities. It appeared that the
fungal community in the soils was more sensitive to treat-
ments than was a bacterial community.
TGGE/DGGE methods have been used for successful
analysis of microbial diversity in a wide range of environ-
ment samples [9,21,23,25,44]. The superiority of these tech-
niques is due to their high throughput, reliability and repro-
ducibility [21], while there are some limitations of TGGE/
DGGE, such as incomplete DNA extraction, PCR bias,
co-migration, etc. [13,23]. Another limitation of
TGGE/DGGE is related to the problem of resolution [23].
For instance, in soil samples there might be as many as 104
different genomes [36,37]. It is obvious that TGGE/DGGE
cannot separate all of the 16S rDNA fragments obtained from
such a variety of microorganisms. In general, these electro-
phoresis techniques will only display the rDNA fragments
obtained from the predominant species present in the com-
munity [23]. Several different studies revealed that bacterial
populations that make up 1% or more of the total community
might be detected by TGGE/DGGE [20,22]. So it is possible
that the TGGE fingerprinting might mask bacterial commu-
nity perturbations of low abundance members in our study.
4.3. Relationship between variation of cellulolytic activity
and fungal community shift
It has been suggested that succession of microbial com-
munities in the cellulose decomposition process can be
divided into three stages: initial colonization of cellulose
decomposition microbial community (stage 1), proliferation
of this community (stage 2) and thriving of the secondary
microorganisms (stage 3) [28]. Two slopes in the cellulose
decomposition curves (Fig. 1) demonstrated that cellulose
decomposition in the three treated soils corresponded to the
above stages, but that the treatments yielded staging differ-
ences. For all three treatments, stage 1 extended throughout
1–5 days, while stage 2 covered 5–20 days for treated soil G
and R, and 5–30 days for soilY. The variations within a fun-
gal community structure also corresponded to the three stages
(Fig. 3A, B). The similarity coefficients of the fungal com-
munity among the three treatments were higher at stage 1,
then decreased at stage 2, and increased again at stage 3.
Intense bands of differing molecular weights appeared in the
three treatments at stage 2, suggesting that certain fungal
populations were selectively enriched around the filter paper
strips during this period. Bands of similar molecular weight,
such as bands A–C in the three treatments at stage 3, might
indicate that treatments did not have a great effect on the com-
munity structure of secondary microorganisms. The above
results suggest that cellulolytic activity variation resulting
from different biofertilizers was consistent with a fungal com-
munity shift resulting from the change of microbial groups
involving in cellulose degradation.
4.4. The biological characteristics of the fungal
community structure
For internal transcribed spacer (ITS) region of fungi has a
fast rate of evolution, resulting in greater sequence variation
between closely related species [3], and there is lack of an
exhaustive database of fungal reference sequences [3,13], in
contrast to bacteria, sequence-based taxonomic identifica-
tion of fungi is more difficult. Chen et al. [5] identified seven
unusual clinical yeast isolates by evaluating ITS2 sequence
27

polymorphisms and observed that the sequence similarity
between the isolates and type strains ranged from 57.5% to
100%. Korabecna et al. [15] examined 66 fungal isolates
belonging to 19 species by RFLP analysis of the 5.8S rRNA
gene and the ITS region, and found intraspecies variability in
the examined region of 11 species. So it is possible that close
fungal species may have relatively low ITS sequence similar-
ity. In our study, we used the primer pair ITS3 and ITS4,
which was designed by White et al. [41], and the primer pair
targeted at 5.8S ribosomal RNA gene and partial ITS 2 se-
quences of fungi [41]. All sequences in our study were
checked by CHIMERA CHECK program of the RDP (ver-
sion 2.7) [16] and personal judgment to avoid artifacts.
In Table 1, the closest matched fungal species of seven
OTUs play different roles in cellulose decomposition, sug-
gesting that the seven OTUs might also be related to cellu-
lose decomposition. Interestingly, with the exception of OTU-
ZY04 that appeared in both of library G20 and R20, the other
cellulose decomposition OTUs were only found in G20. It
appears that the presence of these fungal species in G20 is
consistent with its ability to decompose cellulose to a greater
degree. Whether these species are responsible for the stron-
ger decomposition abilities observed in sample G20 requires
further investigation and validation.
In conclusion, we found that different biofertilizers can
have different effects. The structural shift of the fungal com-
munity studied by molecular techniques (i.e. PCR-TGGE,
cloning and sequencing) during cellulose decomposition in
soil corresponded highly with cellulolytic activity. The above
result suggests that using molecular methods for monitoring
cellulose degradation in the fungal community is a practical
approach to investigate the effects of biofertilizers on soil.
Acknowledgements
This work was supported by two grants (2002-4-4-2 and
2003-15-2) from Shanghai Agricultural Council Foundation
and a grant from the High Tech Development Program of
China (863 Project: 2001AA214131).
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