Hindawi Publishing Corporation Applied and Environmental Soil Science Volume 2013, Article ID 268768, 7 pages http://dx.doi.org/10.1155/2013/268768 Research Article Molecular Identification of Fungal Communities in a Soil Cultivated with Vegetables and Soil Suppressiveness to Rhizoctonia solani Silvana Pompéia Val-Moraes, Eliamar Aparecida Nascimbem Pedrinho, Eliana Gertrudes Macedo Lemos, and Lucia Maria Carareto-Alves Departamento de Tecnologia, Universidade Estadual Paulista (UNESP/FCAV), Acesso Prof. Dr. Paulo Donato Castellane S/N, 14884-900 Jaboticabal, SP, Brazil Correspondence should be addressed to Silvana Pompéia Val-Moraes; valmoraes.silvana@gmail.com Received 20 March 2013; Revised 15 June 2013; Accepted 1 July 2013 Academic Editor: Philip J. White Copyright © 2013 Silvana Pompéia Val-Moraes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fungi constitute an important part of the soil ecosystem, playing key roles in decomposition, cycling processes, and biotic interactions. Molecular methods have been used to assess fungal communities giving a more realistic view of their diversity. For this purpose, total DNA was extracted from bulk soils cultivated with tomato (STC), vegetables (SHC), and native forest (SMS) from three sites of the Taquara Branca river basin in Sumaré County, São Paulo State, Brazil. This metagenomic DNA was used as a template to amplify fungal 18S rDNA sequences, and libraries were constructed in Escherichia coli by cloning PCR products. The plasmid inserts were sequenced and compared to known rDNA sequences in the GenBank database. Of the sequenced clones, 22 were obtained from the SMS sample, 18 from the SHC sample, and 6 from the STC sample. Although most of the clone sequences did not match the sequences present in the database, individual amplified sequences matched with Glomeromycota (SMS), Fungi incertae sedis (SMS), andNeocallimastigomycota (SHC).Most of the sequences from the amplified taxa represent uncultured fungi. Themolecular analysis of variance (AMOVA) indicated that fluctuations observed of haplotypes in the composition may be related to herbicide application. 1. Introduction Despite the importance of soil microbial communities in regulating soil ecosystem-level processes, such as the nutrient cycle and organicmatter decomposition, little is known about the structure of these microbial communities and the factors that influence it in soils.This lack of knowledge arises, in part, from the enormous complexity of soil microbial communi- ties, which are estimated to containmore than 4,000 different genomic equivalents in a single gram of soil [1]. Because of their broad ecological range, ready adaptation abilities, and wide spectrumof nutrient sources, filamentous and yeast-like fungi are able to colonize many different niches or substrates [2]. As integral components in the soil ecosystem, fungi play an important role as major decomposers of plant residues, releasing nutrients that sustain and stimulate plant growth [3]. In spite of their importance, there are very few reports on the fungal communities in soil. Comparative studies have reported that microbial com- munities can change in response to soil disturbances, and differences have been observed between microbial commu- nities in fields with different histories of soil amendment, irrigation, tillage, and plant community structure [4]. Knowl- edge of soil microorganisms is expanding with the advent of new methods available for characterizing organisms in nature [5]. Cultivation-independent approaches using rRNA gene sequence analysis have been used to explore the tax- onomic diversity of soil microbial communities. Recent technological advances in DNA-based methodologies have allowed rapid and accurate identification of fungal and yeast species from awide variety of samples [2]. Concerning rDNA genes, the small subunit 16S has been successfully used to http://dx.doi.org/10.1155/2013/268768 2 Applied and Environmental Soil Science assess bacterial diversity in natural ecosystems, offering the possibility to discover new species [6–9]. This method has been successful for the evaluation of bacterial communities in soil [8] of the region studied in the present paper. There have been few descriptions of soil fungal diversity based upon ribosomal RNA sequences. The purpose of this paper was to compare fungal communities from samples of a latosol under cultivation of tomatoes and vegetables, and, as undisturbed soil, a native forest; these samples were assessed by the analysis of metagenomic DNA from which 18S sequences from fungi can potentially be rDNA amplified. 2. Materials and Methods 2.1. Soil Samples. The surface horizons (0 to 30 cm) of a red latosol soil were sampled in February 2001 (summer season) from three sites in the Taquara Branca river basin in Sumaré County (22∘49󸀠13󸀠󸀠S, 47∘16󸀠08󸀠󸀠W), São Paulo State, Brazil. Mean annual rainfall and mean annual temperature are 1100mm and 21∘C, respectively. The cultivated fields had been managed for more than 10 years using conventional management practices and planted with tomatoes (Lycopersi- con esculentumMill) (STC) and vegetables (Brassica oleracea variety botrytis) (SHC) at the time of soil sampling. The third field had native undisturbed forest soil (SMS) and was characterized as suppressive to mycelial growth of the plant pathogen Rhizoctonia solani [10]. Chemical and physical soil properties were determined on air-dried soils according to the IAC soil analysis system [11]. 2.2. Cultivable Fungi. Fungal communities were extracted by shaking 10 g of bulk soil samples in 800mL of 8 𝜇L pyrophos- phate solution 0.1% containing penicillin (100mg⋅L−1) and streptomycin (100mg⋅L−1) at 200 rpm for 30min at 25∘C. After 10-fold serial dilution (100 𝜇L) were spread ontoMartin medium [12] containing G penicillin (five millions of unities) and streptomycin (2 grams in 100mg⋅L−1) and the plates incubated at 28∘C. For enumeration of the fungi, the colonies were counted daily until the tenth day.The serial dilution was carried out in triplicate. 2.3. DNA Procedures. Total microbial community DNA was extracted from the soil using a FastDNA Spin Kit for Soil (Bio 101, catalog # 6560-200) according to the manufacturer’s instructions using 1 g of from each soil sample. The primer pairs EF3 (5󸀠-TCCTCTAAATGACCAAGTTTG-3󸀠) and EF4 (5󸀠-GGAAGGG[G/A]TGTATTTATTAG-3󸀠) were used for 18S rDNA amplification [13]. Reactions were again carried out in a thermal cycler (PTC-100 Programmable Thermal Controller, MJ Research, Inc.) and the PCR products were purified by electrophoresis on 1% low melting temperature agarose (Gibco) and inserted into pGEM-T cloning vector (Promega, Madison, WI, USA, catalog # A3600) according to the manufacturers’ instructions. Clone libraries were constructed by transforming E. coli DH5𝛼. After screening for inserted clones, the recombinant plasmid DNA from the selected clones was isolated, purified, and quantified [14]. Sequencing PCR was carried out in microplates containing 100–150 ng template plasmid DNA, 1 𝜇L BigDye Terminator; 3.2 pmoles of oligonucleotide primer M13/pUC 1211 for- ward (5󸀠-GTA AAA CGA CGG CCA GT-3󸀠) and buffer 5x (400mM Tris-HCl pH 9; 10mM MgCl 2 ) to complete 10 𝜇L of reaction mixture. Reactions were performed with the fol- lowing cycling parameters: initial denaturation at 96∘C for 2min, then 40 cycles at 96∘C for 10 sec, annealing at 52∘C for 20 sec, and extension at 60∘C for 4min. The amplicons were sequenced by a Capillary Sequencer model ABI 3700 (Applied Biosystems, Foster City, CA, USA). 2.4. Phylogenetic Analysis of 18S rDNA Sequences. The elec- tropherograms were generated by Sequencing Analysis 3.4 software. The computer program phred (available at http:// bozeman.mbt.washington.edu/phrap.docs/phred.html) was used to assign bases to the electropherograms. After eliminat- ing vector sequences, the program phrap (available at http:// bozeman.mbt.washington.edu/phrap.docs/phrap.html) was used to analyze the sequences. The ContiGEN.pl program was used to determine only nucleotide sequences above 400 bp in size and phred quality >20 (quality scores are assigned to each base call in automated sequencer traces) was selected [15]. All fragments used in this analysis were sequenced three times in order to confirm the base sequence. Since single base alterations are used to differentiate the groups, this high quality standard is absolutely necessary: any problems regarding quality of the sequences could negatively affect the accuracy of the final result. The program used for comparison was basic local alignment search tools (BLAST) [16] and the sequences were compared with those online at the GenBank; these sequences were identified as uncultured organisms. The 18S rDNA sequences of the representative clones were aligned against the most similar sequence using the Practical Extraction and Reporting Language (Perl) Program implemented by the Laboratory of Biochemistry of Microorganisms and Plants localized in UNESP/FCAV. Sequence alignmentswere first done usingClustalW (version 1.8) [17] and then adjusted using the BioEdit (version 5.0.9) Program [18]. Phylogenetic relationships were inferred by preferential alignments of the soil fungal sequences obtained from GenBank. This was done using the program MEGA5 (version 2.1) [19]. Bootstrap analysis was performed with 2,000 replicates [20]. 2.5. Genetic Diversity. Genetic diversity indexes were cal- culated using DNA sequences from the three soil samples classified according to the phylogenetic relationships revealed by the preferential alignments. Genetic distance: values of genetic distance were calcu- lated between groups of fungi from different soils and from the same soil. Estimates of genetic distanceswere used to eval- uate genetic divergence within and between fungal groups [21].The genetic distance within groups was estimated by the arithmetic mean of all individual pairwise distances between taxa within a group, and the genetic distance between groups was estimated by the arithmeticmean of all pairwise distances between two groups in the intergroup comparisons [22]. 2.5.1. Pairwise Fixation Index (𝐹ST) Values, Average Pairwise Differences, and Other Indexes. These values were calculated http://bozeman.mbt.washington.edu/phrap.docs/phred.html http://bozeman.mbt.washington.edu/phrap.docs/phred.html http://bozeman.mbt.washington.edu/phrap.docs/phrap.html http://bozeman.mbt.washington.edu/phrap.docs/phrap.html Applied and Environmental Soil Science 3 Table 1: Chemical and physical characteristics soils cultivated for vegetable (SHC) and tomato (STC) production and of forest soil (SMS). Parameters SMS SHC STC pH (in CaCl2) 4.8 5.6 5.9 Organic matter (g dm3(−1)) 50 56 24 P (mg dm3(−1)) 14 280 200 K (mmolc dm 3(−1)) 1.6 5.0 3.9 Ca (mmolc dm 3(−1)) 31 83 59 Mg (mmolc dm 3(−1)) 12 15 40 H + AL (mmolc dm 3(−1)) 52 31 15 CEC (mmolc dm 3(−1)) 97 134 118 Textural class Silty clay Silte clay Silte clay CEC: cation exchange capacity. to estimate if isolated groups from different soils were struc- tured according their origin and soil farming. Arlequin software [23] was used to estimate genetic structure among groups from different soils and intraspecific genetic diversity. The significance of differences in pairwise fixation index (𝐹ST) values and average pairwise differences between isolated groups were calculated using analysis of molecular variance (AMOVA). The 𝐹ST test was used to compare the genetic diversity within each group related to the total combined genetic diversity according to the equation 𝐹ST = (𝜃𝑇 − 𝜃𝑊)/ 𝜃 𝑇 , where 𝜃 𝑇 is the genetic diversity for all samples and 𝜃 𝑊 is the genetic diversity for each group [24].The statistical signif- icance of𝐹ST was calculated by randomly assigning sequences in the populations and for 1000 permutations. Average pairwise differenceswere estimated from comparisonswithin a group of different sequences between a given sequence and all other sequences [23]. To estimate genetic diversity within soil fungal groups, some indexes were calculated using a distancemethodwith the p-distance substitutionnucleotide model. Average pairwise differences and nucleotide diver- sity were calculated for each group. In addition, molecular indexes such as number of gene copies and haplotypes, total number of loci, usable loci, polymorphic sites, and nucleotide diversity were calculated for each data set. 3. Results 3.1. Soil Analysis. The organic matter was lowest in soil cul- tivated with tomatoes. However, soils cultivated with vegeta- bles and suppressive native forest had similar organic matter contents. Soil pH, phosphorus, potassium, calcium, andmag- nesium were higher in cultivated soils (Table 1) than in uncultivated forest soil, probably reflecting the regular liming and fertilization to support crop productions. An increase in pHwas usually accompanied by a decrease in exchangeable al and an increase in cation exchange capacity (CEC) and other cations (K, Ca, and Mg). 3.2. Numbers of Cultivable Fungi. Most probable number method for fungi populations using a microassay technique [25] was deposited 40 𝜇L aliquots on individual selective agar plates is the drop plating method (3 replicates per dilution). Thenumber of cultivable fungi obtainedwas 1.78×105 CFU/g (colony-forming units per gram) for soil cultivated with vegetables, 1.45 × 105 CFU/g for natural forest, and 1.08 × 10 5 CFU/g for soil planted with tomatoes. 3.3. Phylogenetic Analysis of 18S rDNA Sequences. Of the 576 clones obtained for each soil sample, only 38 were found to have inserts of the expected size and quality, such as 22 for SMS, 18 for SHC, and 06 for STC. The rDNA fragments had about 400 bp of 18S rDNA, which was enough for phyloge- netic identification, at least to the taxon level of organisms belonging to groups represented in sequence databases. Most of 0.4 kb fragments of cloned 18S rDNA obtained from both soil samples did not match those in the database. The grouping of the clone sequences with the superior fungal sequences present in GenBank for the three soil samples is shown in Figure 1. Overall, the sequences were associated withGlomeromycota (2 sequences fromSMS), Fungi incertae sedis (1 sequence from SMS), and Neocallimastigomycota (1 sequence from SHC) and other sequences with uncultured fungi. Alignment of the sequences resulted in a phylogenetic tree with several clades, some of which contained at least one known sequence. Similarity values ranged from 69 to 97%. The evolutionary history was inferred by using the maximum likelihood method based on the Jukes-Cantor model [26]. The bootstrap consensus tree inferred from 2000 replicates [27] is taken to represent the evolutionary history of the taxa analyzed [27]. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (2000 replicates) is shown next to the branches [27]. Initial tree(s) for the heuristic searchwere obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach, and then selecting the topol- ogy with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 25.0042)). The analysis involved 58 nucleotide sequences. Codon positions included were first+second+third+noncoding. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 51 positions in the final dataset. Evolutionary analyses were conducted in MEGA5 [19]. All nucleotide sequences were submitted to NCBI and assigned accession numbers DQ641264, AY613927, AY645193 to AY645205, AY646688 to AY646692, AY646694, AY646- 696 to AY646699, AY646701, AY646702, AY646704, AY646- 707 to AY646711, DQ641264.1, DQ792517.1, DQ792532.1, DQ792534.1, DQ792535.1, DQ792536.1, DQ792544.1, DQ79- 2551.1, DQ792556.1, DQ792559.1, DQ792563.1. 3.4. Genetic Diversity and Pairwise Fixation Index (𝐹ST), Average Pairwise Differences, and Other Indexes. The highest genetic diversity sampled was distributed within the soil groups (98.48%) and a minor portion was sampled among the soil groups (1.52%) (Table 2(a)). The 𝐹ST value was the 4 Applied and Environmental Soil Science 70 59 45 73 Fungi incertae sedis Glomeromycota 92 41 13 4 0 0 Fungi incertae sedis Basidiomycota Fungi incertae sedis 80 70 45 73 69 20 30 0 40 16 23 2 0 0 3 0 Glomeromycota Ascomycota Fungi Ascomycota Fungi Neocallimastigomycota Neocallimastigomycota 97 96 79 38 51 0 25 0 13 13 8 4 15 2 1 0.5 AY613928.1 SMS C05 AY645193.1 SMS C05 AY645195.1 SMS F11 AY645197.1 SHC G01 AY613927.1 SMS C07 AY645198.1 SMS H11 AY645196.1 SMS B08 AY645199.1 SHC A02 DQ792544.1 SHC G08 DQ792556.1 STC H04 AY646704.1 SMS G09 AY646707.1 SMS H12 JQ040259.1 Mortierella hyalina AY645205.1 SMS G11 EF177621.1 Uncultured Acaulospora EF177611.1 Uncultured Glomus AY646696.1 SHC E09 AY646692.1 SMS H10 AY645200.1 SHC D12 AY646690.1 SHC B12 DQ792532.1 STC F10 DQ792563.1 SMS A04 AY646695.1 STC G09 DQ792517.1 SHC A09 AY645201.1 SHC A08 AY645194.1 SMS A06 AJ878783.1 Mucor hiemalis DQ792551.1 SHC A05 AY646694.1 SHC B06 DQ792531.1 STC E01 AY646698.1 SMS G07 AY646697.1 SMS B11 DQ792534.1 STC B11 EF190318.1 Calvatia gigantea AM397678.1 Olpidium brassicae AB556934.1 Uncultured Glomus AY646701.1 SMS F12 AY646702.1 SMS D01 AY452805.1 Erysiphe AY646711.1 SMS B12 AY646691.1 SHC F11 AY646708.1 SMS F01 AY929084.1 Uncultured mycorrhizal fungus DQ641264.1 SMS H06 DQ812940.1 Pestalotiopsis versicolor AY645203.1 SMS F09 AY645204.1 SMS G10 AY646709.1 SHC A03 HQ260108.1 Uncultured fungus DQ067604.1 Caecomyces sp. DQ792559.1 STC B02 AY645202.1 SHC B09 AY646693.1 SHC E09 DQ792536.1 SHC C02 AY646699.1 SHC B07 AY646689.1 SHC B08 DQ792535.1 SHC C08 AY646710.1 SMS B07 aquilegiae Figure 1: Molecular phylogenetic analysis by maximum likelihood method. highest for the tomato crop (STC) compared to the native forest (0.01888); the 𝐹ST value for SHC and SMS was slightly lower (0.01750) (Table 2(b)). Average pairwise differences and number of polymorphic sites for the STC sample showed the highest values (Table 2(c)). The three soils do not have shared haplotypes. 4. Discussion Theregular liming and fertilization tomaintain soil fertility to the crop production probably altered the amount of organic matter in the soil (Table 1). Soil organic matter (SOM) is the most often reported characteristic of long-term experiments Applied and Environmental Soil Science 5 Table 2: Genetic distance between the soil and the values diversity. (a) Soils groups Sum of squares Percentage of variation Among populations 1.149 1.52 Within populations 17.167 98.48 (b) Soils groups SMS SHC STC SMS 0.00000 SHC 0.01750 0.00000 STC 0.01888 0.00000 0.00000 Among all 0.01517 (c) Indexes SMS SHC STC Number of sequences used 22 18 6 Number of haplotypes 17 11 6 Number of shared haplotypes 0 0 0 Total number of sites 3212 3212 3212 Number of polymorphic sites 884 784 1881 Nucleotide diversity 0.9667 ± 0.0301 1.0000 ± 0.0388 1.0000 ± 0.0962 Average pairwise difference 0.443121 ± 0.220213 417.8990900 ± 0.533024 1047.733276 ± 10.551729 and can be identified as a valuable indicator of agroecosys- tems development within the specific agro ecological condi- tions and agricultural practice [28]. Although not observed for soil in which tomato was cultivated, changes in soil condi- tions due to the surface residue accumulation in continuous crops are often characterized by an increase in soil organic matter [29]. Crop residues influence soil organic matter dynamics to the greatest extent by increasing or decreasing decomposition and nutrient availability, thereby sustaining soil fertility and sustainability of agroecosystems [30]. Thus, tillage or soil management can have significant impacts on soil properties and microbial community structure. Accord- ing to a study conducted for 24 years, the productivity of no-till compared favorably with that of moldboard plow and chisel plow systems [31]. Crop residues can influence soil organic matter dynamics to the greatest extent by increas- ing or decreasing decomposition and nutrient availability, thereby sustaining fertility of the ground and sustainability of agroecosystems.Thus, tillage or soil management can have significant impacts on soil properties and microbial commu- nity structure. Although CFUs provide only a rough idea of the soil fun- gal community, the results showed that it seems to be affected by vegetation type and management intensity, being lowest for tomato, where cultivation makes use of a variety of pesti- cides. Since most colonies on plates stem from fungal spores [32], it is possible that soil from tomato favored few specific spore-producing fungi. The results of this work are partially consistent with previous studies about shifts in microbial community structure versus changes in soil management; with no tillage, the microbial community shifts towards a higher proportion of fungi [33]. In general, high soil fertility and nutrient availability favors the bacterial community and low fertility favor the fungi [34]. This result can be associated with the suppressiveness to mycelia growth of the plant pathogen R. solani found in this soil [10]. However, no colony was assayed against R. solani in this work. Concerning this, the vast majority of natural soils inhibit germination and growth of fungi to a certain extent, a phenomenon known as soil fungistasis. Furthermore, there is a long list of exam- ples on suppression of soil-born fungal and bacterial root pathogens by mycorrhiza [34]. The STC soil DNA sample amplified few fungal 18S rDNA sequences, though cultivable fungi have been isolated from this soil. This probably reflects the observation that plate count techniques favor the isolation of fast-growing, low-substrate-specific, and spore-producing fungi [35], while molecular methods favor numerically dominate fungi with relatively high amounts of vegetative mycelium. Some fungal species were favored and/or affected by the soil husbandry, such as vegetable and tomato cultivation, when compared to soil of a native forest. In the context it is important to mention that cultivation of tomato makes use of a variety of pesticides [36], the intensive management of which impacts soil microorganisms in a generally harmful manner, although this is difficult to quantify exactly [37]. In a maize-French bean field trial it was observed that organic fertilizers particularly farm yard manure and plant compost, have impact on the fungal population, its diversity and the physic and chemical properties of the soil than not adding an organic amendment [38]. The structure and operation of the soil microbial community reflect the interaction between many biotic and abiotic factors. Among the most important factors is the quality of organic substrates available [39]. 6 Applied and Environmental Soil Science The types of nutritional substrates are different in soils with contrasting quality of organic matter, with direct effects on the nature of microbial communities and active soil fauna. Additionally, organic matter affects the structural properties of the soil such as aggregation and aeration, which can affect the growth of organisms that live in soil [40]. The content of organic substances affect enzymatic activities and the activity of most enzymes as matter content increases reflecting higher microbial communities and further stabilization of enzymes by humic materials [41]. The important justification presented in soil metagenomic studies on the low frequency of sequences belonging to fungi, despite fungi being major constituents of the soil biomass, that this are present in the form of hyphae and because of this fungi DNA extracted fromsoil is approximately 10 times lower than bacterial DNA extracted from soil in bacterial diversity studies [42]. 5. Conclusion Tomato cultivation appeared to reduce the abundance and diversity at compared to vegetable cultivated and native forest soil. However, this conclusion must be with caution since soil sampling was confined to selected experimental plots. There is need for a wider study area for to find of fungal diversity. The occurrence several 18S sequences that have not been grouped to any phylum, suggests the existence of new phyla in the soil studied in this paper. References [1] D. H. Buckley and T. M. Schmidt, “The structure of microbial communities in soil and the lasting impact of cultivation,” Microbial Ecology, vol. 42, no. 1, pp. 11–21, 2001. [2] J. I. Mitchell and A. Zuccaro, “Sequences, the environment and fungi,”Mycologist, vol. 20, no. 2, pp. 62–74, 2006. [3] K. Ritz and I. M. Young, “Interactions between soil structure and fungi,”Mycologist, vol. 18, no. 2, pp. 52–59, 2004. [4] M. A. Liebig, D. L. Tanaka, and B. J. Wienhold, “Tillage and cropping effects on soil quality indicators in the Northern Great Plains,” Soil and Tillage Research, vol. 78, no. 2, pp. 131–141, 2004. [5] V. Torsvik and L. Øvreås, “Microbial diversity and function in soil: from genes to ecosystems,” Current Opinion in Microbiol- ogy, vol. 5, no. 3, pp. 240–245, 2002. [6] R. M. Pereira, E. L. da Silveira, D. C. Scaquitto et al., “Molecular characterization of bacterial populations of different soils,” Bra- zilian Journal of Microbiology, vol. 37, no. 4, pp. 439–447, 2006. [7] E. L. da Silveira, R. M. Pereira, D. C. Scaquitto et al., “Bacte- rial diversity of soil under eucalyptus assessed by 16S rDNA sequencing analysis,” Pesquisa Agropecuária Brasileira, vol. 41, no. 10, pp. 1507–1516, 2006. [8] S. P. Val-Moraes, M. J. Valarini, R. Ghini, E. G. M. Lemos, and L. M. Carareto-Alves, “Diversidade de bactérias de solo sob vegetação natural e cultivo de hortaliças,” Revista Ciência Agronômica, vol. 40, no. 1, pp. 7–16, 2009. [9] E. A. N. Pedrinho, E. G. M. Lemos, R. M. Pereira et al., “Ava- liação do impacto do lodo de esgoto na microbiota do solo utilizando o gene 16s rRNA,”Arquivos do Instituto Biológico, São Paulo, vol. 76, no. 3, pp. 443–448, 2009. [10] R. Ghini and M. M. H. Zaroni, “Relação entre coberturas vege- tais e supressividade de solos aRhizoctonia solani,” Fitopatologia Brasileira, vol. 26, pp. 10–15, 2001. [11] B. van Raij, J. C. de Andrade, H. Cantarella, and J. A. Quaggio, Análise Quı́mica ParaAvaliação da fertilidade de Solos Tropicais, Instituto Agronômico, Campinas, Brasil, 2001. [12] J. P. Martin, “Use of acid, rose Bengal and streptomycin in the plate method for estimating soil fungi,” Soil Science, vol. 69, pp. 215–232, 1950. [13] E. Smit, P. Leeflang, B.Glandorf, J. D.VanElsas, andK.Wernars, “Analysis of fungal diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis,” Applied and Environmental Microbiology, vol. 65, no. 6, pp. 2614–2621, 1999. [14] J. Sambrook, E. F. Fritsch, and T. Maniatis, “Gel electrophoresis of DNA,” in Molecular Cloning Laboratory Manual, C. Nolan, Ed., vol. 1, pp. 6.1–6.62, Cold Spring Harbor Laboratory Press, New York, NY, USA, 1989. [15] B. Ewing, L. Hillier, M. C. Wendl, and P. Green, “Base-calling of automated sequencer traces using phred. I. accuracy assess- ment,” Genome Research, vol. 8, no. 3, pp. 175–185, 1998. [16] S. F. Altschul, T. L. Madden, A. A. Schäffer et al., “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs,” Nucleic Acids Research, vol. 25, no. 17, pp. 3389–3402, 1997. [17] J. D. Thompson, D. G. Higgins, and T. J. Gibson, “Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice,”Nucleic Acids Research, vol. 22, no. 22, pp. 4673–4680, 1994. [18] P. Hall, BioEdit—Version 5.0.6. Raleigh, North Carolina State University, Department of Microbiology, 2001. [19] K. Tamura, D. Peterson, N. Peterson, G. Stecher, M. Nei, and S. Kumar, “MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and max- imum parsimony methods,” Molecular Biology and Evolution, vol. 28, no. 10, pp. 2731–2739, 2011. [20] D. Swofford, G. Olsen, P. Waddel, and M. Hillis, “Phylogenetic inference,” inMolecular Systematics, D. M. Hills, C. Mortiz, and B.K.Mable, Eds., pp. 407–514, SinauerAssociation, Sunderland, Mass, USA, 1996. [21] M. Nei, “Genetic distance between populations,” American Naturalist, vol. 106, no. 949, pp. 283–292, 1972. [22] M. Nei and S. Kumar, Molecular Evolution and Phylogenetics, Oxford University Press, Oxford, UK, 2000. [23] S. Schneider, D. Roeslli, and L. Excoffier, ARLEQUIN: Software for Population Genetics Data Analysis. Version 2.000, University of Geneva, Geneva, Switzerland, 2000. [24] A. P. Martin, “Phylogenetic approaches for describing and comparing the diversity ofmicrobial communities,”Applied and EnvironmentalMicrobiology, vol. 68, no. 8, pp. 3673–3682, 2002. [25] M. C. Jahnel, E. J. B. N. Cardoso, and C. T. S. Dias, “Deter- minação do número mais provável de microrganismos do solo pelo método de plaqueamento por gotas,” Revista Brasileira de Ciência do Solo, vol. 23, no. 3, pp. 553–559, 1999. [26] T. H. Jukes and C. R. Cantor, “Evolution of protein molecules,” in Mammalian Protein Metabolism, H. N. Munro, Ed., pp. 21– 132, Academic Press, New York, NY, USA, 1969. [27] J. Felsenstein, “Confidence limits on phylogenies: an approach using the bootstrap,” Evolution, vol. 39, pp. 783–791, 1985. [28] M. Körschens, “Soil organic matter and environmental protec- tion,” Archives of Agronomy and Soil Science, vol. 50, pp. 3–9, 2004. Applied and Environmental Soil Science 7 [29] A. D. Halvorson, B. J.Wienhold, andA. L. Black, “Tillage, nitro- gen, and cropping system effects on soil carbon sequestration,” Soil Science Society of America Journal, vol. 66, no. 3, pp. 906– 912, 2002. [30] D. W. Reeves, “The role of soil organic matter in maintaining soil quality in continuous cropping systems,” Soil and Tillage Research, vol. 43, no. 1-2, pp. 131–167, 1997. [31] K. R. Olson, S. A. Ebelhar, and J. M. Lang, “Effects of 24 years of conservation tillage systems on soil organic carbon and soil productivity,” Applied and Environmental Soil Science, vol. 2013, Article ID 617504, 10 pages, 2013. [32] F. A. A. M. de Ley and J. M. Lynch, “Functional diversity of the rhizosphere,” in Proceedings of the 4th International Work- shop on Plant-Growth Promoting Rhizobacteria, A. Ogoshi, K. Kobayashi, Y. Homma, F. Kadoma, N. Kondo, and S. Akico, Eds., pp. 38–43, Organisation for Economic Co-operation and Development, Paris, France, 1997. [33] K. Hedlund, “Soil microbial community structure in relation to vegetation management on former agricultural land,” Soil Biology and Biochemistry, vol. 34, no. 9, pp. 1299–1307, 2002. [34] S. J. Grayston, G. S. Griffith, J. L. Mawdsley, C. D. Campbell, and R. D. Bardgett, “Accounting for variability in soil microbial communities of temperate upland grassland ecosystems,” Soil Biology and Biochemistry, vol. 33, no. 4-5, pp. 533–551, 2001. [35] J. S. States, “Useful criteria in the description of fungal com- munities,” in The Fungal Community, D. T. Wicklow and G. C. Carroll, Eds., pp. 185–200, Marcel Dekker, New York, NY, USA, 1981. [36] L. M. Zavatti and R. B. Abakerli, “Reśıduos de agrotóxicos em frutos,” Pesquisa Agropecuária Brasileira, vol. 34, no. 3, pp. 473– 480, 1999. [37] A. Nesci, G. Barros, C. Castillo, and M. Etcheverry, “Soil fungal population in preharvest maize ecosystem in different tillage practices in Argentina,” Soil and Tillage Research, vol. 91, no. 1-2, pp. 143–149, 2006. [38] H. Swer, M. S. Dkhar, and H. Kayang, “Fungal population and diversity in organically amended agricultural soils of Megha- laya, India,” Journal of Organic Systems, vol. 6, no. 2, 2011. [39] D. A.Wardle, “A comparative assessment of factors which influ- ence microbial biomass carbon and nitrogen levels in soil,” Biological Reviews of the Cambridge Philosophical Society, vol. 67, no. 3, pp. 321–358, 1992. [40] G. D. Bending, M. K. Turner, and J. E. Jones, “Interactions between crop residue and soil organic matter quality and the functional diversity of soil microbial communities,” Soil Biology and Biochemistry, vol. 34, no. 8, pp. 1073–1082, 2002. [41] R. G. Burns, “Enzyme activity in soil: location and a possible role inmicrobial ecology,” Soil Biology and Biochemistry, vol. 14, no. 5, pp. 423–427, 1982. [42] J. Borneman, P. W. Skroch, K. M. O’Sullivan et al., “Molecular microbial diversity of an agricultural soil inWisconsin,”Applied and Environmental Microbiology, vol. 62, no. 6, pp. 1935–1943, 1996. 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