FEMS Microbiology Letters 233 (2004) 115–123 www.fems-microbiology.org

Similarity of bacterial communities in sawdust- and straw-amended cow manure composts Stefan J. Green


, Frederick C. Michel Jr. c, Yitzhak Hadar a, Dror Minz




Faculty of Agricultural, Food and Environmental Quality Sciences, Hebrew University of Jerusalem, Rehovot, Israel The Volcani Center, Agricultural Research Organization, Institute of Soil, Water and Environmental Sciences, P.O. Box 6, Bet-Dagan 50-250, Israel c Department of Food, Agricultural, and Biological Engineering, Ohio State University, Ohio Agricultural Research and Development Center, Wooster, OH, USA Received 25 December 2003; received in revised form 28 January 2004; accepted 28 January 2004 First published online 14 February 2004

Abstract We analyzed bacterial communities in two cow manure composts derived from the same feed manure and composted in the same location, but composted with different carbon amendments, and in peat-based potting mixes amended with these composts. Bacterial communities were characterized by PCR-denaturing gradient gel electrophoresis (DGGE) analysis of extracted DNAs, and population fingerprints generated for each sample were compared. Sequence analyses of dominant DGGE bands revealed that members of the phylum Bacteroidetes were the most dominant bacteria detected in this study (19 of 31 clones). These analyses demonstrate that bacterial community profiles of individual composts were highly similar, as were profiles of compost-amended potting mixes. However, potting mix profiles differed substantially from the original compost profiles and from that of the peat base. These data indicate that highly similar bacterial populations were active in the two composts, and suggest that the effects of the initial carbon amendment on the mature compost bacterial communities were minor, while factors such as the feed manure and composting location may have been more influential. Ó 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved. Keywords: Compost; Bacteroidetes; b-Proteobacteria; DGGE

1. Introduction Composts are widely used soil amendments. They serve as sources of microbial biomass, organic matter, and inorganic nutrients, and can sustain microbial populations in field or greenhouse applications. Compost applications can reduce the severity of plant diseases and improve soil fertility [1–3], and can interact directly or indirectly with plants, affecting plant growth, flowering and resistance to disease [4–7]. Microbial analyses of compost can serve to confirm pathogen removal during the composting process, help identify microbial communities consistent with compost maturity, and to survey the microbial population in


Corresponding author. Fax: +972-3-960-4017. E-mail address: [email protected] (D. Minz).

mature composts with the perspective of encouraging the development of plant-growth promoting bacteria or disease suppressive microbial populations [3,8–11]. Cultivation of microorganisms and community-level enzymatic analyses have been used to analyze shifts in microbial populations during the composting process [11–14]. Molecular analyses, including fatty acid profiling and nucleic acid-based approaches offer several advantages over such techniques, such as the avoidance of culture bias and species-level identification, and have been employed previously in analyses of composts [9,10,15–18]. As with any analysis, molecular techniques such as denaturing gradient gel electrophoresis (DGGE) can be limited or biased at each step of the methodology (for review see [19] and references therein). Nonetheless, techniques such as DGGE provide a rapid means to assess dominant microbial populations even in complex microbial systems such as compost.

0378-1097/$22.00 Ó 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.femsle.2004.01.049


S.J. Green et al. / FEMS Microbiology Letters 233 (2004) 115–123

We utilized PCR-DGGE and sequencing analyses to characterize bacterial populations in two dairy (cow) manure composts and in potting mixes with compost incorporated. Fragments of bacterial 16S rDNA were amplified from DNA extracts and bacterial community profiles were generated by DGGE. This study had three basic purposes: (a) to analyze bacterial populations in two composts derived from identical cow manure feed, but composted with different amendments, (b) to examine shifts in bacterial population structure as a result of compost amendment to peat-based potting mixes and (c) to identify predominant bacterial populations in composts and compost-amended potting mixes.

ml styrofoam pots and incubated under greenhouse conditions (22–27 °C) for 2 days prior to sampling.

2. Material and methods

2.3. PCR amplification

2.1. Composts and potting mixes

Portions of 16S rDNA genes were amplified from extracted DNA samples using primer sets targeting bacteria. Each PCR mix contained 1.5 U (per 50 lL) of Taq polymerase (Red Taq, Sigma Chemical Co.), and the following reagents: 1 Sigma PCR buffer, 0.20 mM PCR nucleotide mix (Promega, Madison, WI), 4.0 mM MgCl2 , 6.25 lg (per 50 lL) bovine serum albumin (BSA) (Roche Diagnostics, Mannheim, Germany) and 25 pmol of each primer. Samples were initially PCR amplified using primer set 11F/907R (sequences and references below) in a reaction volume of 25 lL in a Tgradient thermal cycler (Whatman Biometra). These reactions were conducted with a touchdown PCR procedure as follows: samples were initially denatured for 3 min at 95 °C and then cycled 35 times through three steps: denaturation (94 °C; 30 s), annealing (initially 66 °C; final temperature 62 °C; 30 s), elongation (72 °C; 50 s). A twominute incubation at 72 °C was added to the end of each PCR program. The annealing temperature was dropped 0.5 °C for 4 cycles, 0.2 °C for 10 cycles and then maintained at the final annealing temperature for the remainder of the reaction. PCR products generated from the touchdown PCR were diluted 1–5 with water and used as template for a second, nested PCR with the primers 341F-GC/907R. Nested PCR was conducted with the following conditions: PCR mixes were initially denatured for 3 min at 95 °C; and then cycled 27 times through three steps: denaturation (94 °C; 30 s), annealing (64 °C; 30 s) and elongation (72 °C; 30 s). A two-minute

Dairy manure was blended with wheat straw (straw compost) or a mix of hardwood sawdust and wood shavings (sawdust compost) and composted in windrows on a concrete surface, as described elsewhere [20]. Chemical analyses of the two composts, presented in Table 1, were conducted by the Service Testing and Research Lab, OARDC, Ohio State University. Both composts were mature, with stability levels (respiration) of less than 1 mg CO2 -C g1 dw d1 [20]. Compost samples were collected directly from several locations within the mature compost pile and mixed manually. Three peat-based potting mixes were formulated. The first treatment, a peat control, consisted of 60% sphagnum peat moss and 40% coarse perlite amended with 4.6 g l1 dolomitic lime, 3.1 g l1 calcium carbonate, 1.1 g l1 each of superphosphate, potassium nitrate and gypsum and 17.5 g l1 of 14–14–14 (N–P–K) Osmocote slow-release fertilizer (Scotts Company, Marysville, OH). The pH of this mix was 6.1. The second and third treatments consisted of 50% sphagnum peat moss, 40% coarse perlite, and 10% sawdust- or straw-amended cow manure compost, respectively, all on a volume basis. The compost-amended potting mixes were amended with 4.6 g l1 dolomitic lime, 3.1 g l1 calcium carbonate and 17.5 g l1 of 14–14–14 (N–P–K) Osmocote slowrelease fertilizer. The pH of the compost mixes ranged from 6.6 to 6.7. All potting mixes were irrigated in 500

2.2. DNA extraction Total DNA was extracted from triplicate samples of peat, composts, and from the three potting mix treatments (2 days after wetting) using the UltraClean Soil DNA Isolation Kit (MoBio Laboratories, Inc, California, USA) directly after sampling. DNA was extracted from approximately 0.25 g of peat or potting mix material and from 0.1 g of the two composts. All DNA extracts were checked for size and quality by electrophoresis on 0.7% agarose gels stained with ethidium bromide.

Table 1 Chemical analyses of sawdust and straw cow manure composts

Sawdust compost Straw compost


EC (mX/cm)

Percent solids

Percent ash

Percent volatile solids

Percent volatile nitrogen

Percent total carbon

N-NH3 (lg/g)

N-NH3 (lg/g)

C/N Ratio





















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incubation at 72 °C was added to the end of each PCR program. Amplification products from outer and nested PCRs were checked for size and yield by gel electrophoresis on 2% agarose gels stained with ethidium bromide. Gels stained with ethidium bromide were photographed on a UV transillumination table (302 nm) with a Kodak digital camera (Rochester, NY). PCR primer sequences, from 50 to 30 end, are listed here: 11F, GTT TGA TCM TGG CTC AG (modified from primer 8F [21]), 341F-GC containing a 40-bp GC-clamp to en-


hance separation in DGGE (clamp sequence in italics), CGC CCG CCG CGC CCC GCG CCC GTC CCG CCG CCC CCG CCC GCC TAC GGG AGG CAG CAG [22], and 907R, CCG TCA ATT CMT TTG AGT TT [22]. 2.4. Denaturing gradient gel electrophoresis DGGE analyses were performed with a D-Gene system (Bio-Rad, CA) using the following ingredients and conditions: 1 TAE buffer (40 mM Tris–HCl, 20

Table 2 Partial sequence analysis of bacterial 16S rDNA genes recovered from composts and peat-based potting mixtures Band number

GenBank sequence Accession No.




Peat potting mixture



Peat potting mixture

P6a P6b P9 P19 S1-2 S1-A2 S1-3 S1-A3

AY332575 AY332576 AY332577 AY332578 AY332579 AY332580 AY332581 AY332582

Peat potting mixture Peat potting mixture Peat potting mixture Peat potting mixture Sawdust compost Sawdust compost Sawdust compost Sawdust compost



Sawdust compost



Sawdust compost

S1-7 S2

AY332585 AY332586

Sawdust compost Saw potting mixture



Saw potting mixture

S6 S7

AY332588 AY332589

Saw potting mixture Saw potting mixture



Saw potting mixture



Saw potting mixture

S10 S11

AY332592 AY332593

Saw potting mixture Saw potting mixture

S13 T2

AY332594 AY332595

Saw potting mixture Straw potting mixture



Straw potting mixture



Straw potting mixture

T7 T8

AY332598 AY332599

Straw potting mixture Straw potting mixture



Straw potting mixture

T10 T12

AY332601 AY332602

Straw potting mixture Straw potting mixture



Straw potting mixture

Nearest relative (BLAST) Name

Accession No.

% Similarity


Chryseobacterium sp. CPW406 Uncultured Sphingobacteriaceae Oxalobacter sp. p8E Uncultured Acidobacterium Uncultured H. bacterium Telluria mixta Uncultured bacterium Cytophaga sp. TUT1013 Uncultured bacterium Uncultured bacterium PHOS-HE36 Uncultured Bacteroidetes Rhizobium sp. RM1-2001 Uncultured bacterium Chryseobacterium scophthalmum Chryseobacterium sp. CPW406 Bacteroidetes sp. RW262 Flavobacterium mizutaii Flavobacterium mizutaii Uncultured Bacteroidetes Exiguobacterium sp. Thermomonas haemolytica Bacterium str. 47077 Chryseobacterium sp. CPW406 Chryseobacterium scophthalmum Chryseobacterium scophthalmum Cytophaga sp. Uncultured bacterium PHOS-HE36 Uncultured Bacteroidetes Oxalobacter sp. p8E Xanthomonas axonopodis Bacterium str. 47077







AJ496038 AJ292579 AJ459874 X65589 AJ318153 AB098581 AJ318130 AF314435

99 96 99 94 89 95 94 93

b-Proteobacteria Acidobacteria a-Proteobacteria b-Proteobacteria Bacteroidetes Bacteroidetes Fibrobacteres Bacteroidetes







AJ421114 AJ271009

93 96

Actinobacteria Bacteroidetes




AF493694 AJ438175

97 93

Bacteroidetes Bacteroidetes







AF275715 AJ300185

99 97

Firmicutes c-Proteobacteria

AF227830 AJ457206

97 98

Bacteroidetes Bacteroidetes







AB015264 AF314435

88 94

Bacteroidetes Chlorobi




AJ496038 AE012082

99 97

b-Proteobacteria c-Proteobacteria





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mM acetic acid, 1 mM EDTA [pH 8.3]) and 1-mm thick polyacrylimide gels (6%). Gels contained a 20–60% denaturant gradient and were electrophoresed for 17 h at 100 V and 60 °C. Gels were stained with GelStar nucleic acid stain (Biowhittaker Molecular Applications, Rockland, ME) and photographed on a UV transillumination table (302 nm) with a Kodak digital camera (Rochester, NY).

product moment correlation coefficient [24] and by unweighted pair group method with arithmetic averages (UPGMA) clustering [25]. This approach compares profiles based on both band position and intensity.

3. Results 3.1. Physical and chemical analyses of sawdust- and straw-amended cow manure composts

2.5. Band excision, cloning and sequencing Dominant bands chosen by visual inspection were excised from DGGE gels visualized on a Dark Reader transillumination table (Clare Chemical Research, Inc., Dolores, CO). Using sterile razor blades, pieces of acrylamide containing DNA bands were excised and placed in 2 ml plastic tubes with 200 ll of TE and 8 glass beads of approximately 1 mm diameter. Tubes were vortexed for 10 min, incubated at 37 °C for 30 min, and 1 ll of the liquid was used as template for PCR with the 341F-GC/907R primers. Generated PCR products were verified to represent the appropriate band by a second DGGE analysis. The pGEM-TÒ Easy Vector System (Promega, Wisconsin, USA) was used for cloning of reamplified, excised DGGE bands. Transformed clones were screened by suspending colony material in PCR mixes, amplifying with the 341F-GC/907R primer set, and DGGE analysis. Plasmids from selected colonies were purified with the WizardTM Plus Miniprep DNA Purification System (Promega, Wisconsin, USA) as instructed by the manufacturer. Clones were sequenced using the Applied Biosystems PRISM Dye Terminator Cycle Sequencing Ready reaction kit supplied with AmpliTaq DNA polymerase. The sequencing products were analyzed with an Applied Biosystems 377 DNA sequencer. Sequences were submitted to the CHIMERA_CHECK program located at the Ribosomal Database Project [23], and suspect sequences were removed from analyses. Sequences were filed in GenBank Accession Nos. AY332573–AY332603 (Table 2).

A detailed physical and chemical description of the developing and mature sawdust and straw composts is presented elsewhere [20]. These analyses indicated significantly different chemical and physical profiles for the two composts. In particular, the temperature profiles of the developing composts were significantly different; the sawdust compost reached a temperature of 60 °C after 10 days, while the straw compost required 6 weeks to reach this temperature [20]. Furthermore, the sawdust compost maintained a temperature of 60 °C throughout the remainder of the composting period monitored (120 d), while the straw compost maintained peak heating for a significantly shorter time period (approximately 50 d) [20]. Prior to amendment to the peat-based potting mixes, the composts were analyzed for pH, electrical conductivity, percent solids, percent ash, volatile solids, total nitrogen, total carbon, ammonia and nitrate nitrogen (Table 1). Based on these analyses and CO2 evolution rates of less than 1.0 mg CO2 g VS1 h1 [20], both composts were considered mature. Although the composts were similar in pH, percent solids, and percent volatile solids, they differed substantially in percent ash content and total carbon (10–20% difference), and had even greater disparity in levels of electrical conductivity and inorganic nitrogen, with the straw-amended compost containing 2–3 times as much ammonia and nitrate as the sawdust-amended compost. Particle size, color and texture of the composts also differed substantially (data not shown).

2.6. Clustering analysis of DGGE profiles

3.2. DGGE analysis of original potting mix components and potting mixes

Similarity of bacterial community DGGE profiles between samples was estimated by cluster analysis. Normalizations and analyses of DGGE gel patterns were done with BioNumerics software version 3.0 (Applied Maths, Kortrijk, Belgium). During this processing, the different lanes were defined, common bands were selected as positions for normalization, the lanes were normalized to compensate for differences in migration distance due to gel heterogeneity, and bands were detected. The normalized banding patterns were used to generate dendrograms by calculating the PearsonÕs

Bacterial population profiles of the original materials (peat and composts) prior to wetting, and potting mix treatments with and without compost (2 days after wetting) were generated via PCR-DGGE analysis. DGGE profiles of triplicate samplings were analyzed by cluster analysis and found to be highly reproducible. For example, triplicate community profiles of the individual straw and sawdust compost samples had UPGMA Pearson correlation coefficients (r) of greater than 92% (Fig. 1). For other analyses, representative profiles of each sample are presented.

S.J. Green et al. / FEMS Microbiology Letters 233 (2004) 115–123


Fig. 1. Dendrogram depicting the relatedness of bacterial communities from triplicate samples of a straw- and sawdust-amended compost made from the same dairy manure. Bacterial community profiles were generated by PCR-DGGE analysis as described in the text. The UPGMA algorithm was applied to a similarity matrix of PearsonÕs product–moment correlation coefficients (r value) generated from the DGGE banding patterns.

UPGMA analysis of the DGGE profiles showed that bacterial community profiles clustered into three groups consisting of the original composts, potting mixes amended with composts, and peat and peat mix (Fig. 2). Compost profiles and compost-amended mix profiles, respectively, were closely related (r > 83%) and together were more closely related to each other (r ¼ 74%) than to peat or peat mix profiles (r ¼ 40%). Inspection of the DGGE profiles of the two composts prior to incorporation into potting mixes showed 10 bands common to both composts, including bands S1-2, S1-a2, S1-3, S1-a3, S1-5, S1-6 and S1-7 (data not shown). Upon wetting, substantial shifts in the population profiles were observed between the source materials (peat and compost) and the potting mixes (Fig. 2). A direct comparison of the sawdust compost and sawdust compost potting mix revealed 7 bands present in both profiles, while 4 newly developed bands were detected in the potting mix and 5 bands previously present in the compost profile were not detected (Fig. 3). A similar shift between straw compost and straw compost-amended potting mixes was also seen (data not shown).

Peat only control treatments also experienced a substantial shift in population profile upon wetting (Fig. 2). However, the peat and peat mix populations were generally undetectable in compost-amended treatments with the exception of bands P6a, P19 and an unsequenced band (Fig. 4; Table 2). 3.3. Sequence analysis of DGGE bands Partial sequencing of 31 bands (500–550 bases per band) excised from the DGGE profiles was conducted. Sequences were submitted to the NCBI BLAST search engine [26] and a putative phylogenetic position was assigned to each sequence based on the results (Table 2). The sequences consisted of 16 dominant bands excised from DGGE analyses of the sawdust compost and sawdust compost potting mix (band names beginning with S), 9 dominant bands excised from DGGE analyses of straw compost potting mix (band names beginning with T), and 6 dominant bands excised from DGGE analyses of peat-only potting mix (band names beginning with P). The recovered sequences were distributed

Fig. 2. Dendrogram depicting the relatedness of bacterial communities from peat, the peat-potting mix treatment, sawdust- and straw-amended composts, and the compost-amended potting mix treatments. Bacterial community profiles were generated by PCR-DGGE analysis as described in the text. The UPGMA algorithm was applied to a similarity matrix of PearsonÕs product–moment correlation coefficients (r value) generated from the DGGE banding patterns.


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Fig. 3. DGGE analysis of bacterial populations in sawdust-amended compost (A) and peat-based potting mix amended with sawdust-amended compost (B). Black lines indicate bands that are present in both samples. Arrows indicate bands present in only one of the samples. Bands excised, sequenced and subject to phylogenetic analysis are labeled and results are listed in Table 2.

Fig. 4. DGGE analysis of bacterial populations in peat-based potting mixes without compost (A), with sawdust-amended compost (B) and strawamended compost (C). Locations of peat-only potting mix bands are indicated by black lines. Arrows indicate bands found in peat-only potting mix and in compost-amended potting mixes. Bands excised, sequenced and subject to phylogenetic analysis are labeled and results are listed in Table 2.

unevenly among 7 phylogenetic groupings: Acidobacteria (1 sequence), Actinobacteria (1 sequence), Bacteroidetes (formerly Cytophaga–Flavobacter–Bacteroides, 19 sequences), Chlorobi (1 sequence), Fibrobacteres (1 sequence), Firmicutes (1 sequence), and Proteobacteria (7 sequences). The majority of these had high similarity to sequences of known bacteria or environmental sequences (19 sequences, equal to or greater than 95% identity), although 4 sequences, within the Bacteroidetes, were more distantly related to known sequences (<90% identity). Compost potting mixes were dominated by members of the phylum Bacteroidetes (13 of 18 sequences). Of these 13 Bacteroidetes sequences, 4 belonged to the

genus Chryseobacterium (bands S2, S5, T2 and T5) and were most closely related to C. scophthalmum, isolated from a dairy environment, or to Chryseobacterium. sp. CPW406 (Table 2). Bands ST-9 and T9 (recovered from sawdust and straw potting mixes, respectively) were distantly related to an uncultured Bacteroidetes sequence isolated from a compost-based industrial filter (88% sequence identity), and highly similar to each other (>99% identity, data not shown). Likewise, band S13 and T15, were highly similar to each other (99% identity). Peat sequences included 2 members of the Bacteroidetes (P1, also most closely related to Chryseobacterium sp. CPW406; and P3), 3 members of the b-Proteobac-

S.J. Green et al. / FEMS Microbiology Letters 233 (2004) 115–123

teria (P6a, P9 and P19), and a single member of the Acidobacteria (P6b). Band P6a had 99% identity to the sequence of a protease-producing bacterial isolate from an Antarctic soil bioreactor, while band P19 had 94% identity to that of the polysaccharide degrading Telluria mixta, formerly Pseudomonas mixta [27]. It should be noted that only a single clone was sequenced for each band. It is possible for multiple sequences to migrate to identical positions on DGGE gels [28], thus reducing the observed diversity of a sample. This may help explain the presence of different sequences recovered from bands at the same position but from the two different composts (e.g., bands S10/S1-6 and bands S7/S1-a3, Fig. 3).

4. Discussion Cow manure was amended with two different exogenous carbon sources (either straw or sawdust) prior to composting, resulting in mature composts with chemical profiles that differed substantially, particularly with regards to levels of mineral nitrogen. These composts were then amended to peat-based potting mixes to emulate common greenhouse potting mixes [29]. Analyses of bacterial community composition, by PCR-DGGE and sequencing, revealed striking similarities between the two composts, and between potting mixes amended with the composts. Despite significant differences in the composting process (e.g., peak heating temperature and length of peak heating) produced by the addition of different exogenous carbon sources, these amendments appear not to have been significant in determining the bacterial population profile of the mature composts. The similarity observed may be a result of the composts having being produced at the same physical location and thus being exposed to similar microbial populations after peak heating, or of the same manure feedstock being used in both composts. The profiles of potting mixes amended with straw and sawdust composts were also remarkably similar to each other, while both differed from the original compost profiles, and from those of the peat and peat mix. Population profiles of potting mixes amended with compost shared few populations with either the peat or peat potting mix, indicating that the observed similarity was not an artifact derived from the high percentage of peat in the potting mixes. Additionally, the similarity of the compost-amended potting mixes, and their corresponding difference from both of the original compost profiles, is an indication that the profiles represent active microbial populations. Several b-proteobacterial populations, closely related to Oxalobacter sp. and Telluria mixta (Bands P6a, P19 and T10), were detected in peatonly and compost amended potting mixes, suggesting that these populations were derived from the peat.


In this study, members of the phylum Bacteroidetes were the predominant group of bacteria detected. This phylum contains a wide variety of bacteria known for their utilization of macromolecules such as protein, starch, cellulose and chitin [30], and its members have previously been detected, via molecular methods, in various composts [10,18,31]. In this study, the most frequently detected Bacteroidetes sequences belonged to the genus Chryseobacterium, and were detected in peat control and compost-amended potting mixes. Chryseobacterium spp. have been primarily isolated from or detected in organic rich environments such as wastewater, bioreactors, dairy environments, and diseased plant rhizospheres [18,32–34]. In particular, Alfreider et al. [18] showed the development of Chryseobacterium sp. in a compost derived from domestic organic waste. The increasing detection of this genus in composts and other high-organic environments warrants further analysis into the activity of these organisms. The abundance of Bacteroidetes was paralleled by the absence of Actinobacteria, which, although frequent constituents of compost microbial communities, are not always numerically dominant [6,10,13,15]. Friedrich et al. [35] also observed a predominance of Proteobacteria and Bacteroidetes (58.8% and 31.7% of clones, respectively), and a concomitant minority of Actinobacteria (2% of clones) in a molecular community analysis of a compost-based waste gas biofilter. Likewise, Dees and Ghiorse [17] detected Actinobacteria in hot compost only when using specific primers, a result attributed to an abundance of Bacillus-type DNA hindering detection of the less abundant Actinobacteria. Using specific primers, Actinobacteria were readily detected in our composts and potting mixes (data not shown), but their relative absence from general bacterial analyses suggests that they were not dominant members of the bacterial community. The similarity of the potting mix profiles, both different from the original composts but closely related to each other, suggests that the active bacterial populations in both composts were highly similar. Since reproducibility of microbial populations in mature composts is useful for greater commercial application of composts, these results encourage further investigation into the source of such similarity. We consider the possibility that the proximity of the two composting piles promoted similarity in the mature compost bacterial population profiles.

Acknowledgements This research was supported by research Grant No. US-3108-99 from BARD, the United States–Israel Binational Agriculture Research and Development Fund, by EU grant RECOVEG Project, and by a Baron de


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Hirsch travel grant to S.J.G. We gratefully acknowledge the review and commentary of the paper by H.A.J. Hoitink. References [1] Hoitink, H.A.J. and Fahy, P.C. (1986) Basis for the control of soilborne plant pathogens with composts. Annu. Rev. Phytopathol. 24, 93–114. [2] Dick, W.A. and McCoy, E.L. (1993) Enhancing soil fertility by addition of compost. In: Science and Engineering of Composting: Design, Environmental, Microbiological and Utilization Aspects (Hoitink, H.A.J. and Keener, H.M., Eds.), pp. 622–644. Renaissance Publications, Worthington, OH. [3] El-Din, S.M.S.B., Attia, M. and Abo-Sedera, S.A. (2000) Field assessment of composts produced by highly effective cellulolytic microorganisms. Biol. Fertil. Soils 32, 35–40. [4] Hoitink, H.A.J., Boehm, M.J. and Hadar, Y. (1993) Mechanisms of suppression of soilborne plant pathogens in compost-amended substrates. In: Science and Engineering of Composting: Design, Environmental, Microbiological and Utilization Aspects (Hoitink, H.A.J. and Keener, H.M., Eds.), pp. 601–621. Renaissance Publications, Worthington, OH. [5] Alvarez, M.A., Gagne, S. and Antoun, H. (1995) Effect of compost on rhizosphere microflora of the tomato and on the incidence of plant growth-promoting rhizobacteria. Appl. Environ. Microbiol. 61, 194–199. [6] Craft, C.M. and Nelson, E.B. (1996) Microbial properties of composts that suppress damping-off and root rot of creeping bentgrass caused by Pythium graminicola. Appl. Environ. Microbiol. 62, 1550–1557. [7] Roe, N.E., Stoffella, P.J. and Graetz, D. (1997) Composts from various municipal solid waste feedstocks affect vegetable crops. 1. Emergence and seedling growth. J. Am. Soc. Hort. Sci. 122, 427–432. [8] Boehm, M.J., Madden, L.V. and Hoitink, H.A.J. (1993) Effect of organic-matter decomposition level on bacterial species – diversity and composition in relationship to Pythium damping-off severity. Appl. Environ. Microbiol. 59, 4171–4179. [9] Herrmann, R.F. and Shann, J.F. (1995) Microbial community changes during the composting of municipal solid waste. Microb. Ecol. 33, 78–85. [10] Michel Jr, F.C., Marsh, T.J. and Reddy, C.A. (2002) Bacterial community structure during yard trimmings composting. In: Microbiology of Composting (Insam, H., Riddech, N. and Klammer, S., Eds.), pp. 25–42. Springer, Heidelberg. [11] Levanon, D. and Pluda, D. (2002) Chemical, physical and biological criteria for maturity in composts for organic farming. Compost. Sci. Utiliz. 10, 339–346. [12] Boulter, J.I., Trevors, J.T. and Boland, G.J. (2002) Microbial studies of compost: bacterial identification, and their potential for turfgrass pathogen suppression. World J. Microb. Biotech. 18, 661–671. [13] Tiquia, S.M., Wan, J.H.C. and Tam, N.F.Y. (2002) Microbial population dynamics and enzyme activities during composting. Compost. Sci. Utiliz. 10, 150–161. [14] Ryckeboer, J., Mergaert, J., Coosemans, J., Deprins, K. and Swings, J. (2003) Microbiological aspects of biowaste during composting in a monitored compost bin. J. Appl. Microbiol. 94, 127–137. [15] Klamer, M. and Baath, E. (1998) Microbial community dynamics during composting of straw material studied using phospholipid fatty acid analysis. FEMS Microbiol. Ecol. 27, 9–20. [16] Peters, S., Koschinsky, S., Schwieger, F. and Tebbe, C.C. (2000) Succession of microbial communities during hot composting as detected by PCR-single-strand-conformation polymorphism-









[25] [26]







based genetic profiles of small-subunit rRNA genes. Appl. Environ. Microbiol. 66, 930–936. Dees, P.M. and Ghiorse, W.C. (2001) Microbial diversity in hot synthetic compost as revealed by PCR-amplified rRNA sequences from cultivated isolates and extracted DNA. FEMS Microbiol. Ecol. 35, 207–216. Alfreider, A., Peters, S., Tebbe, C.C., Rangger, A. and Insam, H. (2002) Microbial community dynamics during composting of organic matter as determined by 16S ribosomal DNA analysis. Compost. Sci. Utiliz. 10, 303–312. Muyzer, G. and Smalla, K. (1998) Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie Leeuwenhoek 73, 127–141. Changa, C.M., Wang, P., Watson, M.E., Hoitink, H.A.J. and Michel Jr., F.C. (2003) Assessment of a commercial maturity test kit for composted manures. Compost. Sci. Utiliz. 11, 125–143. Lane, D.J. (1991) 16S/23S rRNA sequencing. In: Nucleic Acid Techniques in Bacterial Systematics (Stackebrandt, E. and Goodfellow, M., Eds.), pp. 115–175. Wiley, Chichester, UK. Muyzer, G., Hottentr€ager, S., Teske, A. and Wawer, C. (1996) Denaturing gradient gel electrophoresis of PCR-amplified 16S rDNA – a new molecular approach to analyse the genetic diversity of mixed microbial communities. In: Molecular Microbial Ecology Manual (Akkermans, A.D.L., van Elsas, J.D. and de Bruijn, F.J., Eds.), pp. 1–23. Kluwer, Dordrecht, The Netherlands. Maidak, B.L., Cole, J.R., Parker Jr., C.T., Garrity, G.M., Larsen, N., Li, B., Lilburn, T.G., McCaughey, M.J., Olsen, G.J., Overbeek, R., Pramanik, S., Schmidt, T.M., Tiedje, J.M. and Woese, C.R. (1999) A new version of the RDP (Ribosomal Database Project). Nucleic Acids Res. 27, 171–173. Vauterin, L. and Vauterin, P. (1992) Computer-aided objective comparison of electrophoresis patterns for grouping and identification of microorganisms. Eur. Microbiol. 1, 37–41. Sneath, P.H.A. and Sokal, R.R. (1973) Numerical Taxonomy. W.H. Freeman and Co., San Francisco, CA. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. Bowman, J.P., Sly, L.I., Hayward, A.C., Spiegel, Y. and Stackebrandt, E. (1993) Telluria mixta (Pseudomonas mixta Bowman, Sly, and Hayward 1988) gen. nov., comb. nov., and Telluria chitinolytica sp. nov., soil-dwelling organisms which actively degrade polysaccharides. Int. J. Syst. Bacteriol. 43, 120–124. Schmalenberger, A. and Tebbe, C.C. (2003) Bacterial diversity in maize rhizospheres: conclusions on the use of genetic profiles based on PCR-amplified partial small subunit rRNA genes in ecological studies. Mol. Ecol. 12, 251–262. Boehm, M.J., Wu, T.Y., Stone, A.G., Kraakman, B., Iannotti, D.A., Wilson, G.E., Madden, L.V. and Hoitink, H.A.J. (1997) Cross-polarized magic-angle spinning C-13 nuclear magnetic resonance spectroscopic characterization of soil organic matter relative to culturable bacterial species composition and sustained biological control of Pythium root rot. Appl. Environ. Microbiol. 63, 162–168. Manz, W., Amann, R., Ludwig, W., Vancanneyt, M. and Schleifer, K.-H. (1996) Application of a suite of 16S rRNAspecific oligonucleotide probes designed to investigate bacteria of the phylum Cytophaga-Flavobacter-Bacteroides in the natural environment. Microbiology 142, 1097–1106. Verkhovtseva, N.V., Osipov, G.A., Bolysheva, T.N., Kasatikov, V.A., Kuzmina, N.V., Antsiferova, E.J. and Alexeeva, A.S. (2002) Comparative investigation of vermicompost microbial communities. In: Microbiology of Composting (Insam, H., Riddech, N. and Klammer, S., Eds.), pp. 99–108. Springer, Heidelberg. Yamaguchi, S. and Yokoe, M. (2000) A novel protein-deamidating enzyme from Chryseobacterium proteolyticum sp. nov., a newly

S.J. Green et al. / FEMS Microbiology Letters 233 (2004) 115–123 isolated bacterium from soil. Appl. Environ. Microbiol. 66, 3337– 3343. [33] McSpadden-Gardener, B.B. and Weller, D.M. (2001) Changes in populations of rhizosphere bacteria associated with take-all disease of wheat. Appl. Environ. Microbiol. 67, 4414–4425. [34] McBain, A.J., Bartolo, R.G., Catrenich, C.E., Charbonneau, D., Ledder, R.G., Rickard, A.H., Symmons, S.A. and Gilbert, P.


(2003) Microbial characterization of biofilms in domestic drains and the establishment of stable biofilm microcosms. Appl. Environ. Microbiol. 69, 177–185. [35] Friedrich, U., Prior, K., Altendorf, K. and Lipski, A. (2002) High bacterial diversity of a waste gas-degrading community in an industrial biofilter as shown by a 16S rDNA clone library. Environ. Microbiol. 4, 721–734.

Similarity of bacterial communities in sawdust - Wiley Online Library

b The Volcani Center, Agricultural Research Organization, Institute of Soil, Water and Environmental Sciences, P.O. Box 6, Bet-Dagan 50-250, Israel c Department of Food, Agricultural, and Biological Engineering, ... Table 1, were conducted by the Service Testing and. Research Lab, OARDC, Ohio State University. Both.

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