• Compost;
  • Bacteroidetes;
  • β-Proteobacteria;
  • DGGE


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

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.


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

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 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.

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.

2Material and methods

  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

2.1Composts and potting mixes

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 g−1 dw d−1[20]. Compost samples were collected directly from several locations within the mature compost pile and mixed manually.

Table 1.  Chemical analyses of sawdust and straw cow manure composts
 pHEC (mΩ/cm)Percent solidsPercent ashPercent volatile solidsPercent volatile nitrogenPercent total carbonN-NH3 (μg/g)N-NH3 (μg/g)C/N Ratio
Sawdust compost7.611.057.428.971.13.741.578.31826.511.3
Straw compost7.523.051.033.766.33.935.4247.24191.09.1

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 l−1 dolomitic lime, 3.1 g l−1 calcium carbonate, 1.1 g l−1 each of superphosphate, potassium nitrate and gypsum and 17.5 g l−1 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 l−1 dolomitic lime, 3.1 g l−1 calcium carbonate and 17.5 g l−1 of 14–14–14 (N–P–K) Osmocote slow-release fertilizer. The pH of the compost mixes ranged from 6.6 to 6.7. All potting mixes were irrigated in 500 ml styrofoam pots and incubated under greenhouse conditions (22–27 °C) for 2 days prior to sampling.

2.2DNA 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.

2.3PCR amplification

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 μL) 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 μg (per 50 μL) 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 μL 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 two-minute 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 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 5 to 3 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 enhance 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].

2.4Denaturing 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 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).

2.5Band 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 μl 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 μl 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-T® Easy Vector System (Promega, Wisconsin, USA) was used for cloning of re-amplified, 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).

Table 2.  Partial sequence analysis of bacterial 16S rDNA genes recovered from composts and peat-based potting mixtures
Band numberGenBank sequence Accession No.SampleNearest relative (BLAST)
   NameAccession No.% SimilarityPhylogeny
P1AY332573Peat potting mixtureChryseobacterium sp. CPW406AJ45720698Bacteroidetes
P3AY332574Peat potting mixtureUncultured SphingobacteriaceaeAJ25260298Bacteroidetes
P6aAY332575Peat potting mixtureOxalobacter sp. p8EAJ49603899β-Proteobacteria
P6bAY332576Peat potting mixtureUncultured AcidobacteriumAJ29257996Acidobacteria
P9AY332577Peat potting mixtureUncultured bacteriumAJ45987499α-Proteobacteria
P19AY332578Peat potting mixtureTelluria mixtaX6558994β-Proteobacteria
S1-2AY332579Sawdust compostUncultured bacteriumAJ31815389Bacteroidetes
S1-A2AY332580Sawdust compostCytophaga sp. TUT1013AB09858195Bacteroidetes
S1-3AY332581Sawdust compostUncultured bacteriumAJ31813094Fibrobacteres
S1-A3AY332582Sawdust compostUncultured bacterium PHOS-HE36AF31443593Bacteroidetes
S1-5AY332583Sawdust compostUncultured BacteroidetesAF54336395Bacteroidetes
S1-6AY332584Sawdust compostRhizobium sp. RM1-2001AF33166297α-Proteobacteria
S1-7AY332585Sawdust compostUncultured bacteriumAJ42111493Actinobacteria
S2AY332586Saw potting mixtureChryseobacterium scophthalmumAJ27100996Bacteroidetes
S5AY332587Saw potting mixtureChryseobacterium sp. CPW406AJ45720698Bacteroidetes
S6AY332588Saw potting mixtureBacteroidetes sp. RW262AF49369497Bacteroidetes
S7AY332589Saw potting mixtureFlavobacterium mizutaiiAJ43817593Bacteroidetes
S8AY332590Saw potting mixtureFlavobacterium mizutaiiAJ43817594Bacteroidetes
S-T9AY332591Saw potting mixtureUncultured BacteroidetesAJ3J818189Bacteroidetes
S10AY332592Saw potting mixtureExiguobacterium sp.AF27571599Firmicutes
S11AY332593Saw potting mixtureThermomonas haemolyticaAJ30018597γ-Proteobacteria
S13AY332594Saw potting mixtureBacterium str. 47077AF22783097Bacteroidetes
T2AY332595Straw potting mixtureChryseobacterium sp. CPW406AJ45720698Bacteroidetes
T4AY332596Straw potting mixtureChryseobacterium scophthalmumAJ27100994Bacteroidetes
T5AY332597Straw potting mixtureChryseobacterium scophthalmumAJ27100995Bacteroidetes
T7AY332598Straw potting mixtureCytophaga sp.AB01526488Bacteroidetes
T8AY332599Straw potting mixtureUncultured bacterium PHOS-HE36AF31443594Chlorobi
T9AY332600Straw potting mixtureUncultured BacteroidetesAJ31818189Bacteroidetes
T10AY332601Straw potting mixtureOxalobacter sp. p8EAJ49603899β-Proteobacteria
T12AY332602Straw potting mixtureXanthomonas axonopodisAE01208297γ-Proteobacteria
T15AY332603Straw potting mixtureBacterium str. 47077AF22783097Bacteroidetes

2.6Clustering analysis of DGGE profiles

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 Pearson's 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.


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

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

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 VS−1 h−1[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).

3.2DGGE analysis of original potting mix components and potting mixes

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.


Figure 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 Pearson's product–moment correlation coefficients (r value) generated from the DGGE banding patterns.

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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=74%) than to peat or peat mix profiles (r=40%).


Figure 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 Pearson's product–moment correlation coefficients (r value) generated from the DGGE banding patterns.

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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).


Figure 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.

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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).


Figure 4. DGGE analysis of bacterial populations in peat-based potting mixes without compost (A), with sawdust-amended compost (B) and straw-amended 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.

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3.3Sequence 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 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 β-Proteobacteria (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).


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

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 β-proteobacterial populations, closely related to Oxalobacter sp. and Telluria mixta (Bands P6a, P19 and T10), were detected in peat-only 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.


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References

This research was supported by research Grant No. US-3108-99 from BARD, the United States–Israel Bi-national Agriculture Research and Development Fund, by EU grant RECOVEG Project, and by a Baron de Hirsch travel grant to S.J.G. We gratefully acknowledge the review and commentary of the paper by H.A.J. Hoitink.


  1. Top of page
  2. Abstract
  3. 1Introduction
  4. 2Material and methods
  5. 3Results
  6. 4Discussion
  7. Acknowledgements
  8. References
  • [1]
    Hoitink, H.A.J., Fahy, P.C. (1986) Basis for the control of soilborne plant pathogens with composts. Annu. Rev. Phytopathol. 24, 93114.
  • [2]
    Dick, W.A., 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., Keener, H.M., Eds.), pp.622–644 Renaissance Publications, Worthington, OH.
  • [3]
    El-Din, S.M.S.B., Attia, M., Abo-Sedera, S.A. (2000) Field assessment of composts produced by highly effective cellulolytic microorganisms. Biol. Fertil. Soils 32, 3540.
  • [4]
    Hoitink, H.A.J., Boehm, M.J., 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., Keener, H.M., Eds.), pp.601–621 Renaissance Publications, Worthington, OH.
  • [5]
    Alvarez, M.A., Gagne, S., 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, 194199.
  • [6]
    Craft, C.M., 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, 15501557.
  • [7]
    Roe, N.E., Stoffella, P.J., Graetz, D. (1997) Composts from various municipal solid waste feedstocks affect vegetable crops. 1. Emergence and seedling growth. J. Am. Soc. Hort. Sci. 122, 427432.
  • [8]
    Boehm, M.J., Madden, L.V., 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, 41714179.
  • [9]
    Herrmann, R.F., Shann, J.F. (1995) Microbial community changes during the composting of municipal solid waste. Microb. Ecol. 33, 7885.
  • [10]
    Michel Jr, F.C., Marsh, T.J., Reddy, C.A. (2002) Bacterial community structure during yard trimmings composting. In: Microbiology of Composting (Insam, H., Riddech, N., Klammer, S., Eds.), pp.25–42 Springer, Heidelberg.
  • [11]
    Levanon, D., Pluda, D. (2002) Chemical, physical and biological criteria for maturity in composts for organic farming. Compost. Sci. Utiliz. 10, 339346.
  • [12]
    Boulter, J.I., Trevors, J.T., Boland, G.J. (2002) Microbial studies of compost: bacterial identification, and their potential for turfgrass pathogen suppression. World J. Microb. Biotech. 18, 661671.
  • [13]
    Tiquia, S.M., Wan, J.H.C., Tam, N.F.Y. (2002) Microbial population dynamics and enzyme activities during composting. Compost. Sci. Utiliz. 10, 150161.
  • [14]
    Ryckeboer, J., Mergaert, J., Coosemans, J., Deprins, K., Swings, J. (2003) Microbiological aspects of biowaste during composting in a monitored compost bin. J. Appl. Microbiol. 94, 127137.
  • [15]
    Klamer, M., Baath, E. (1998) Microbial community dynamics during composting of straw material studied using phospholipid fatty acid analysis. FEMS Microbiol. Ecol. 27, 920.
  • [16]
    Peters, S., Koschinsky, S., Schwieger, F., Tebbe, C.C. (2000) Succession of microbial communities during hot composting as detected by PCR-single-strand-conformation polymorphism-based genetic profiles of small-subunit rRNA genes. Appl. Environ. Microbiol. 66, 930936.
  • [17]
    Dees, P.M., 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, 207216.
  • [18]
    Alfreider, A., Peters, S., Tebbe, C.C., Rangger, A., Insam, H. (2002) Microbial community dynamics during composting of organic matter as determined by 16S ribosomal DNA analysis. Compost. Sci. Utiliz. 10, 303312.
  • [19]
    Muyzer, G., Smalla, K. (1998) Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie Leeuwenhoek 73, 127141.
  • [20]
    Changa, C.M., Wang, P., Watson, M.E., Hoitink, H.A.J., F.C. Michel Jr. (2003) Assessment of a commercial maturity test kit for composted manures. Compost. Sci. Utiliz. 11, 125143.
  • [21]
    Lane, D.J. (1991) 16S/23S rRNA sequencing. In: Nucleic Acid Techniques in Bacterial Systematics (Stackebrandt, E., Goodfellow, M., Eds.), pp.115–175 Wiley, Chichester, UK.
  • [22]
    Muyzer, G., Hottenträger, S., Teske, A., 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., de Bruijn, F.J., Eds.), pp.1–23 Kluwer, Dordrecht, The Netherlands.
  • [23]
    Maidak, B.L., Cole, J.R., C.T. Parker Jr., 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., Woese, C.R. (1999) A new version of the RDP (Ribosomal Database Project). Nucleic Acids Res. 27, 171173.
  • [24]
    Vauterin, L., Vauterin, P. (1992) Computer-aided objective comparison of electrophoresis patterns for grouping and identification of microorganisms. Eur. Microbiol. 1, 3741.
  • [25]
    Sneath, P.H.A., Sokal, R.R. (1973) Numerical Taxonomy. W.H. Freeman and Co, San Francisco, CA.
  • [26]
    Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 33893402.
  • [27]
    Bowman, J.P., Sly, L.I., Hayward, A.C., Spiegel, Y., 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, 120124.
  • [28]
    Schmalenberger, A., 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, 251262.
  • [29]
    Boehm, M.J., Wu, T.Y., Stone, A.G., Kraakman, B., Iannotti, D.A., Wilson, G.E., Madden, L.V., 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, 162168.
  • [30]
    Manz, W., Amann, R., Ludwig, W., Vancanneyt, M., Schleifer, K.-H. (1996) Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the phylum Cytophaga-Flavobacter-Bacteroides in the natural environment. Microbiology 142, 10971106.
  • [31]
    Verkhovtseva, N.V., Osipov, G.A., Bolysheva, T.N., Kasatikov, V.A., Kuzmina, N.V., Antsiferova, E.J., Alexeeva, A.S. (2002) Comparative investigation of vermicompost microbial communities. In: Microbiology of Composting (Insam, H., Riddech, N., Klammer, S., Eds.), pp.99–108 Springer, Heidelberg.
  • [32]
    Yamaguchi, S., Yokoe, M. (2000) A novel protein-deamidating enzyme from Chryseobacterium proteolyticum sp. nov., a newly isolated bacterium from soil. Appl. Environ. Microbiol. 66, 33373343.
  • [33]
    McSpadden-Gardener, B.B., Weller, D.M. (2001) Changes in populations of rhizosphere bacteria associated with take-all disease of wheat. Appl. Environ. Microbiol. 67, 44144425.
  • [34]
    McBain, A.J., Bartolo, R.G., Catrenich, C.E., Charbonneau, D., Ledder, R.G., Rickard, A.H., Symmons, S.A., Gilbert, P. (2003) Microbial characterization of biofilms in domestic drains and the establishment of stable biofilm microcosms. Appl. Environ. Microbiol. 69, 177185.
  • [35]
    Friedrich, U., Prior, K., Altendorf, K., 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, 721734.