World J Microbiol Biotechnol DOI 10.1007/s11274-014-1633-0
SHORT COMMUNICATION
Shift of bacterial community structure in two Thai soil series affected by silver nanoparticles using ARISA Wariya Chunjaturas • John A. Ferguson • Wutthida Rattanapichai • Michael J. Sadowsky Kannika Sajjaphan
•
Received: 5 August 2013 / Accepted: 19 October 2013 Ó Springer Science+Business Media Dordrecht 2014
Abstract In this study we examined the influence of silver nanoparticles (SNP) on the bacterial community and microbial processes in two soils from Thailand, a Ayutthaya (Ay) and Kamphaengsaen soil series (Ks). Results of this analysis revealed that SNP did not affect to pH, electrical conductivity, cation exchange capacity, and organic matter in both the Ay and Ks series. Automated ribosomal intergenic spacer analysis (ARISA) analysis profiles showed that bacterial community decreased with increasing SNP concentration. Pearson’s correlation coefficient and multidimensional scaling analyses indicated that the effects of SNP on the bacterial community structure depended more on soil types than SNP application rates and incubation periods. Additionally, the results showed that SNP application rates affected on amount of CO2 emissions, while SNP application rates had no effect on N mineralization in both soil types. This study is the first investigation of the effects of SNP on bacterial community using ARISA analysis. Our results might be useful to evaluate the risk associated with the applications of SNP for consumer products and agricultural practices.
W. Chunjaturas W. Rattanapichai Department of Soil Science, Kasetsart University, Cha-Tuchak, Bangkok, Thailand J. A. Ferguson M. J. Sadowsky Department of Soil, Water and Climate, and BioTechnology Institute, University of Minnesota, St. Paul, MN, USA K. Sajjaphan (&) Department of Soil Science and Center for Advanced Studies in Agriculture and Food, KU Institute for Advanced Studies, Kasetsart University, 50 Phahon Yothin Rd., Cha-Tuchak, Bangkok 10900, Thailand e-mail: [email protected]
Keywords Silver nanoparticles Bacterial community structure Thai soil Microbial activity
Introduction A growing interest in nanotechnology has led to an increased worldwide production and application of nanoparticles. Nanoparticles are incorporated into various categories of consumer products, including cosmetics, textiles, electronics and medicines (Medina et al. 2007). Silver nanoparticles (SNP) are potent and broad-spectrum antibacterial agents with activity against diverse species of gram-positive and gram-negative bacteria (Kim et al. 2007). They show good antibacterial activity due to their large surface area to volume ratio, which provides better contact with microorganisms (Morones et al. 2005). SNPs have also been applied to clothes, socks and laboratory gowns, as well as in medical products, such as surgical gowns and dressing bandages, where they are claimed to have the ability to inhibit bacterial growth (Lee et al. 2007; Vigneshwaran et al. 2007). A possible pathway of SNP movement into soils is via wet or dry deposition, as well as from the application of organic wastes to agricultural fields, such as sewage-sludge or the use of plant growth–promoting sprays. The antimicrobial spectrum of silver is extensive, and Ag has also been reported to be effective against a variety of viruses (Han et al. 2005). Silver ions also have fungicidal and algicidal properties (Ratte 1999). Nanosilver is highly toxic to several strains of bacteria, including Vibrio cholerae (the bacterium causing cholera), E. coli and, most significantly, methicillin-resistant Staphylococcus aureus, which is responsible for infections that resist treatment by conventional antibiotics. Nanosilver at a
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concentration as low as 0.14 lg/ml has been reported to be toxic to several species of nitrifying bacteria, which play an important role in the environment by converting ammonia in the soil into a nitrogen form that can be used by plants. Nitrifying bacteria are also used by sewage treatment plants to convert raw sewage into less harmful products. The toxicity of nanosilver to these microorganisms has raised concerns that release of nanosilver to the environment may disrupt the operation of sewage treatment plants, as well as natural processes in the ecosystem that support plant life (Benn and Westerhoff 2008). However, there is little research concerning the influence of SNP on microorganisms in soil. Most studies investigated the impacts on microorganisms in vitro and thus, there is a great necessity to explore the probable effects of SNPs on microorganisms in even more complex environmental systems like soils. The objectives of the present study were to investigate the effects of SNP on bacterial community structure and some chemical properties on two soils from Thailand.
Materials and methods Soil collection and preparation Two agricultural soils from Nakhonpathom Province, Thailand were selected for this study. Surface soil samples (0–10 cm) from a Ayutthaya series (Ay) clay soil and a Kamphaengsaen series (Ks) sandy loam soil were air-dried and passed through a 2 mm sieve for further study. Soil microcosms set up Aliquots (150 g dry weight basis) of nonsterilized soil were adjusted to 8 % moisture, placed into jam jars, and maintained at this moisture during the experiments. Each soil microcosm was amended with different concentrations of SNPs (0, 50, 100, 250 and 500 lg/g) with three replicates per treatment. All samples were incubated at room temperature. Soil chemical properties, bacterial community structure and soil microbial processes were examined at 0, 2, 4 and 8 weeks. Soil properties and chemical analysis Soil pH was determined using a 1:1 ratio of soil to deionized water. Electrical conductivity (EC) was determined using a 1:5 ratio of soil to deionized water. Textural classification was measured using the pipette method (Gee and Bauder 1986). Organic matter content was determined by wet oxidation and titration using the Walkley and Black method (Nelson and Sommers 1982).
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The cation exchange capacity (CEC) was determined with 1 M NH4OAc solution buffered at pH 7 (Soil Survey Laboratory Staff 1992). The basic cations (Ca, Mg, K and Na) in the NH4OAc solution were determined by atomic absorption spectrophotometry (AAS). Exchangeable Al was extracted by 1 M KCl and the Al in the solution was determined by AAS. Analysis of the ARISA bacterial community profiles At each time point, bacterial community structure was analysed by using the polymerase chain reaction and automated ribosomal intergenic spacer analysis (PCR– ARISA). Total bacterial genomic DNA was extracted from soil samples using the Power Soil DNA kits (MoBio laboratory, CA., USA). PCR–ARISA was performed following the method of Borneman and Triplett (1997) with slight modifications. Reaction mixtures contained PCR buffer (Promega), 2.5 mM MgCl2, 500 lg of bovine serum albumin per ml, 200 lM of each dNTP, 400 lM of each primer, 2.5 U of Taq polymerase, and approximately 350 ng of template DNA, in a final volume of 50 ll. The primers were 1406f, 50 TGYACACACCGCCCGT 30 (universal, 16S rRNA gene), and 23Sr, 50 GGGTT BCCCCATTCRG 30 (bacterial-specific, 23S rRNA gene). Primer 1406f was 50 end labeled with the phosphoramidite dye 5-FAM. Reaction mixtures were held at 94 °C for 2 min, followed by 30 cycles of amplification at 94 °C for 15 s, 55 °C for 15 s, and 72 °C for 45 s, and a final extension of 72 °C for 2 min. PCR products, along with 1 ll of an internal size standard were added to 20 ll of deionized formamide, and the mixture was denatured at 95 °C for 5 min, followed by 2 min on ice. Sample fragments were then discriminated by using the ABI 310 genetic analyzer (Perkin-Elmer), in which DNA was electrophoresed in a capillary tube filled with electrophoresis polymer rather than in a polyacrylamide gel. The samples were run under standard ABI 310 denaturing electrophoresis conditions for 1 h each, with the POP-4 polymer, and the data were analyzed by using the GeneScan 3.1 software program (Perkin-Elmer). The program output was a series of peaks (an electropherogram). The sizes were estimated by comparison to fragments in the internal size standard. The performances of two Rhodamine X-labeled internal size standards, the GeneScan-2500 size standard (Perkin-Elmer) and a custom 200–2,000 bp standard (Bioventures, Inc.), were compared for the sizing of large fragments (up to 1,200 bp). In addition, the GeneScan software calculated the fluorescence contained in each peak, which was proportional to the quantity of DNA in the fragment. The relative amount of each fragment in the PCR product was estimated as the ratio between the fluorescence (peak area) of the fragment of
World J Microbiol Biotechnol
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Fig. 1 Dendrogram showing ARISA profiles of bacterial communities from Ay and Ks series amended with different concentration of SNP
interest and the total fluorescence of all fragments in the profile. The community profiles were normalized and analyzed by using BioNumerics v.3.5 software (Applied Maths, Sint-Martens-Latem, Belgium). Community profile similarities were calculated by using Pearson’s product-moment correlation coefficient, with 1 % optimization. Results were analysed with accounting for the covariance structure by using the multidimensional scaling (MDS) and multivariate analysis of variance (MANOVA),
forms of discriminant analysis, subroutines of Bionumerics software. Soil microbial activity analysis Soil CO2 respiration and N mineralization were used to determine soil microbial activity. Soil microcosms were set-up as described above. The CO2 trap contained 10 ml of 1 M NaOH. At each sampling time, the jam jars were opened, all of the
123
Second discriminant
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World J Microbiol Biotechnol
0 ppm 50 ppm 100 ppm 250 ppm 500 ppm
0 ppm 50 ppm 100 ppm 250 ppm 500 ppm
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(A) Second discriminant
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(A) 0 weeks 2 weeks 4 weeks 8 weeks
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(B) (B) Fig. 2 Multivariate analysis of variance (MANOVA) ARISA profiles of bacterial communities from Ay series showing effects of SNP concentrations (a) and incubation periods (b)
Fig. 3 Multivariate analysis of variance (MANOVA) ARISA profiles of bacterial communities from Ks series showing effects of SNP concentrations (a) and incubation periods (b)
Results and discussion NaOH in the trap and 150 g of soil were removed. The amounts of CO2 evolved and trapped in the 1 M NaOH were determined as described by Hopkins (2008). The mineralization of N during soil microcosm incubations was examined by measuring accumulations of NO3–N and extractable NH4–N in 10 g aliquots of soil using the method described by Keeney and Nelson (1982).
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Effect of SNP on some soil chemical properties The soil samples had the following characteristics. The Ay series soil had a clay texture with 7.02 % sand, 34.58 % silt, and 58.40 % clay; 1.85 % organic matter; pH of 4.88, EC of 2.35 dSm-1; and CEC of 13.39 cmol kg-1. In
World J Microbiol Biotechnol
contrast, the Ks series had a sandy loam texture with 54.99 % sand, 44.17 % silt, and 0.84 % clay; 5.76 % organic matter; pH of 6.84; EC of 1.45 dSm-1; and CEC of 30.96 cmol kg-1. The investigation of the effect of SNP on soil properties indicated that different concentrations of SNP (50, 100, 250, and 500 lg/g soil) and incubation periods of 0, 2, 4 and 8 weeks did not affect pH, EC, CEC, and organic matter in both the Ay and Ks soil series (data not shown). Similar results were previously reported by Ha¨nsch and Emmerling (2010). They found that no treatment effects were found for pH and soil organic carbon. Effect of SNP application on bacterial community structure The results from ARISA profiles showed the effects of different SNP concentrations and incubation times on bacterial community structure. The analysis of bacterial amplicon distributions from SNP concentrations and incubation periods showed that bacterial community structure decreased with increasing SNP concentration and increasing incubation period in both the Ay and Ks series soils. The Pearson correlation coefficient analysis (Fig. 1) and MDS analysis (data not shown) showed that the effects of SNP on bacterial community structure depended on soil type more than the SNP concentration and incubation period. Multivariate analysis of variance (MANOVA) was performed to determine if SNP concentrations and incubation periods were significant variables affecting bacterial community structure. For the Ay series soil, the results indicated that community composition did not cluster by concentration (Fig. 2a) or incubation period (Fig. 2b), except for the control samples. In contrast, for the Ks series soil, the results showed that microbial constituents of soils clustered by both SNP concentrations (Fig. 3a) and incubation periods (Fig. 3b). Effect of SNP application on soil microbial activity The results of soil respiration illustrated that the amount of CO2 emissions decreased with increasing SNP application rates and incubation periods in both soil types (data not show). Moreover, the results showed that SNP application rate affected the amount of CO2 emissions. Results of N mineralization studies distinctly showed that the available nitrogen in the ammonium (NH4?) form in the Ay series soil decreased steadily from initial amount to the second week, and then leveled off until week 8 (data not shown). In contrast, the nitrate–N (NO3-) in the Ay series increased steadily from the initial amount to the second week, and then leveled off until week 8. The ammonium form in the Ks soil slightly decreased and its
trend was downward from the beginning to week 8, while the nitrate form slightly increased to week 4 and then decreased to week 8 (data not shown). However, the results indicated that the amount of SNP application rates had no effect on N mineralization in both soil types. Taken together, our results indicate that SNP concentrations and incubation times can alter microbial community structure and function in some soils. While our studies confirm the results of Colman et al. (2013), it appears that the influence of SNP on soil microbiota likely depends on soil types, as well as concentrations and time of exposure. Acknowledgments This work was supported by a grant from the Graduate School, Kasetsart University and Center for Advanced Studies in Agriculture and Food, KU Institute for Advanced Studies, Kasetsart University, and The Commission on Higher Education.
References Benn TM, Westerhoff P (2008) Nanoparticle silver released into water from commercially available sock fabrics. Environ Sci Technol 42:4133–4139 Borneman J, Triplett EW (1997) Molecular microbial diversity in soils from Eastern Amazonia: evidence for unusual microorganisms and population shifts associated with deforestation. Appl Environ Microbiol 63:2647–2653 Colman BP, Arnaout CL, Anciaux S, Gunsch CK, Hochella MF Jr, Kim B, Lowry GV, McGill BM, Reinsch BC, Richardson CJ, Unrine JM, Wright JP, Yin L, Bernhardt ES (2013) Low concentrations of silver nanoparticles in biosolids cause adverse ecosystem responses under realistic field scenario. PLoS ONE 88(2):e57189 Gee GW, Bauder JW (1986) Particle size analysis. In: Klute A (ed) Method of soil analysis part 1 physical and mineralogical methods, 2nd edn. American Society Agronomy, Madison, pp 399–404 Han DW, Lee MS, Lee MH, Uzawa M, Park JC (2005) The use of silvercoated ceramic beads for sterilization of Sphingomonas sp. in drinking mineral water. World J Microbiol Biotechnol 21:921–924 Ha¨nsch M, Emmerling C (2010) Effects of silver nanoparticles on the microbiota and enzyme activity in soil. J Plant Nutr Soil Sci 173:554–558 Hopkins DW (2008) Carbon mineralization. In: Carter MR, Gregorich EG (eds) Soil sampling and methods of analysis, 2nd edn. CRC Press, Boca Raton, pp 589–598 Keeney DR, Nelson DW (1982) Nitrogen-inorganic forms. In: Page AL, Miller RH, Keeney DR (eds) Methods of soil chemical analysis, part 2: chemical and microbiological properties, 2nd edn. ASA and SSSA, Madison, pp 642–698 Kim JS, Kuk E, Yu KN, Kim JH, Park SJ, Lee HJ, Kim SH, Park YK, Park YH, Hwang CY, Kim YK, Lee YS, Jeong DH, Cho MH (2007) Antimicrobial effects of silver nanoparticles. Nanomedicine 3:95–101 Lee HY, Park HK, Lee YM, Kim K, Park SB (2007) A practical procedure for producing silver nanocoated fabric and its antibacterial evaluation for biomedical applications. Chem Commun 28:2959–2961 Medina C, Santos-Martinez MJ, Radomski A, Corrigan OI, Radomski MW (2007) Nanoparticles: pharmacological and toxicological significance. Br J Pharmacol 150:552–558 Morones JR, Elechiguerra JL, Camacho A, Holt K, Kouri JB, Ramirez JT, Yacaman MJ (2005) The bactericidal effect of silver nanoparticles. Nanotechnology 16:2346–2353
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World J Microbiol Biotechnol Nelson DW, Sommers LE (1982) Total carbon, organic carbon and organic matter. In: Page AL, Miller RH, Keeney DR (eds) Methods of soil chemical analysis, part 2: chemical and microbiological properties, 2nd edn. ASA and SSSA, Madison, pp 570–572 Ratte HT (1999) Bioaccumulation and toxicity of silver compounds: a review. Environ Toxicol Chem 18:89–108
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Soil Survey Staff (1992) Soil survey laboratory methods manual. Soil survey investigations report No. 42 (version 2.0). USDA-SCS. U.S. Gov. Print. Office, Washington Vigneshwaran N, Kathe AA, Varadarajan PV, Nachane RP, Balasubramanya RH (2007) Functional finishing of cotton fabrics using silver nanoparticles. J Nanosci Nanotechnol 7:1893–1897
SHORT COMMUNICATION
Shift of bacterial community structure in two Thai soil series affected by silver nanoparticles using ARISA Wariya Chunjaturas • John A. Ferguson • Wutthida Rattanapichai • Michael J. Sadowsky Kannika Sajjaphan
•
Received: 5 August 2013 / Accepted: 19 October 2013 Ó Springer Science+Business Media Dordrecht 2014
Abstract In this study we examined the influence of silver nanoparticles (SNP) on the bacterial community and microbial processes in two soils from Thailand, a Ayutthaya (Ay) and Kamphaengsaen soil series (Ks). Results of this analysis revealed that SNP did not affect to pH, electrical conductivity, cation exchange capacity, and organic matter in both the Ay and Ks series. Automated ribosomal intergenic spacer analysis (ARISA) analysis profiles showed that bacterial community decreased with increasing SNP concentration. Pearson’s correlation coefficient and multidimensional scaling analyses indicated that the effects of SNP on the bacterial community structure depended more on soil types than SNP application rates and incubation periods. Additionally, the results showed that SNP application rates affected on amount of CO2 emissions, while SNP application rates had no effect on N mineralization in both soil types. This study is the first investigation of the effects of SNP on bacterial community using ARISA analysis. Our results might be useful to evaluate the risk associated with the applications of SNP for consumer products and agricultural practices.
W. Chunjaturas W. Rattanapichai Department of Soil Science, Kasetsart University, Cha-Tuchak, Bangkok, Thailand J. A. Ferguson M. J. Sadowsky Department of Soil, Water and Climate, and BioTechnology Institute, University of Minnesota, St. Paul, MN, USA K. Sajjaphan (&) Department of Soil Science and Center for Advanced Studies in Agriculture and Food, KU Institute for Advanced Studies, Kasetsart University, 50 Phahon Yothin Rd., Cha-Tuchak, Bangkok 10900, Thailand e-mail: [email protected]
Keywords Silver nanoparticles Bacterial community structure Thai soil Microbial activity
Introduction A growing interest in nanotechnology has led to an increased worldwide production and application of nanoparticles. Nanoparticles are incorporated into various categories of consumer products, including cosmetics, textiles, electronics and medicines (Medina et al. 2007). Silver nanoparticles (SNP) are potent and broad-spectrum antibacterial agents with activity against diverse species of gram-positive and gram-negative bacteria (Kim et al. 2007). They show good antibacterial activity due to their large surface area to volume ratio, which provides better contact with microorganisms (Morones et al. 2005). SNPs have also been applied to clothes, socks and laboratory gowns, as well as in medical products, such as surgical gowns and dressing bandages, where they are claimed to have the ability to inhibit bacterial growth (Lee et al. 2007; Vigneshwaran et al. 2007). A possible pathway of SNP movement into soils is via wet or dry deposition, as well as from the application of organic wastes to agricultural fields, such as sewage-sludge or the use of plant growth–promoting sprays. The antimicrobial spectrum of silver is extensive, and Ag has also been reported to be effective against a variety of viruses (Han et al. 2005). Silver ions also have fungicidal and algicidal properties (Ratte 1999). Nanosilver is highly toxic to several strains of bacteria, including Vibrio cholerae (the bacterium causing cholera), E. coli and, most significantly, methicillin-resistant Staphylococcus aureus, which is responsible for infections that resist treatment by conventional antibiotics. Nanosilver at a
123
World J Microbiol Biotechnol
concentration as low as 0.14 lg/ml has been reported to be toxic to several species of nitrifying bacteria, which play an important role in the environment by converting ammonia in the soil into a nitrogen form that can be used by plants. Nitrifying bacteria are also used by sewage treatment plants to convert raw sewage into less harmful products. The toxicity of nanosilver to these microorganisms has raised concerns that release of nanosilver to the environment may disrupt the operation of sewage treatment plants, as well as natural processes in the ecosystem that support plant life (Benn and Westerhoff 2008). However, there is little research concerning the influence of SNP on microorganisms in soil. Most studies investigated the impacts on microorganisms in vitro and thus, there is a great necessity to explore the probable effects of SNPs on microorganisms in even more complex environmental systems like soils. The objectives of the present study were to investigate the effects of SNP on bacterial community structure and some chemical properties on two soils from Thailand.
Materials and methods Soil collection and preparation Two agricultural soils from Nakhonpathom Province, Thailand were selected for this study. Surface soil samples (0–10 cm) from a Ayutthaya series (Ay) clay soil and a Kamphaengsaen series (Ks) sandy loam soil were air-dried and passed through a 2 mm sieve for further study. Soil microcosms set up Aliquots (150 g dry weight basis) of nonsterilized soil were adjusted to 8 % moisture, placed into jam jars, and maintained at this moisture during the experiments. Each soil microcosm was amended with different concentrations of SNPs (0, 50, 100, 250 and 500 lg/g) with three replicates per treatment. All samples were incubated at room temperature. Soil chemical properties, bacterial community structure and soil microbial processes were examined at 0, 2, 4 and 8 weeks. Soil properties and chemical analysis Soil pH was determined using a 1:1 ratio of soil to deionized water. Electrical conductivity (EC) was determined using a 1:5 ratio of soil to deionized water. Textural classification was measured using the pipette method (Gee and Bauder 1986). Organic matter content was determined by wet oxidation and titration using the Walkley and Black method (Nelson and Sommers 1982).
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The cation exchange capacity (CEC) was determined with 1 M NH4OAc solution buffered at pH 7 (Soil Survey Laboratory Staff 1992). The basic cations (Ca, Mg, K and Na) in the NH4OAc solution were determined by atomic absorption spectrophotometry (AAS). Exchangeable Al was extracted by 1 M KCl and the Al in the solution was determined by AAS. Analysis of the ARISA bacterial community profiles At each time point, bacterial community structure was analysed by using the polymerase chain reaction and automated ribosomal intergenic spacer analysis (PCR– ARISA). Total bacterial genomic DNA was extracted from soil samples using the Power Soil DNA kits (MoBio laboratory, CA., USA). PCR–ARISA was performed following the method of Borneman and Triplett (1997) with slight modifications. Reaction mixtures contained PCR buffer (Promega), 2.5 mM MgCl2, 500 lg of bovine serum albumin per ml, 200 lM of each dNTP, 400 lM of each primer, 2.5 U of Taq polymerase, and approximately 350 ng of template DNA, in a final volume of 50 ll. The primers were 1406f, 50 TGYACACACCGCCCGT 30 (universal, 16S rRNA gene), and 23Sr, 50 GGGTT BCCCCATTCRG 30 (bacterial-specific, 23S rRNA gene). Primer 1406f was 50 end labeled with the phosphoramidite dye 5-FAM. Reaction mixtures were held at 94 °C for 2 min, followed by 30 cycles of amplification at 94 °C for 15 s, 55 °C for 15 s, and 72 °C for 45 s, and a final extension of 72 °C for 2 min. PCR products, along with 1 ll of an internal size standard were added to 20 ll of deionized formamide, and the mixture was denatured at 95 °C for 5 min, followed by 2 min on ice. Sample fragments were then discriminated by using the ABI 310 genetic analyzer (Perkin-Elmer), in which DNA was electrophoresed in a capillary tube filled with electrophoresis polymer rather than in a polyacrylamide gel. The samples were run under standard ABI 310 denaturing electrophoresis conditions for 1 h each, with the POP-4 polymer, and the data were analyzed by using the GeneScan 3.1 software program (Perkin-Elmer). The program output was a series of peaks (an electropherogram). The sizes were estimated by comparison to fragments in the internal size standard. The performances of two Rhodamine X-labeled internal size standards, the GeneScan-2500 size standard (Perkin-Elmer) and a custom 200–2,000 bp standard (Bioventures, Inc.), were compared for the sizing of large fragments (up to 1,200 bp). In addition, the GeneScan software calculated the fluorescence contained in each peak, which was proportional to the quantity of DNA in the fragment. The relative amount of each fragment in the PCR product was estimated as the ratio between the fluorescence (peak area) of the fragment of
World J Microbiol Biotechnol
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Fig. 1 Dendrogram showing ARISA profiles of bacterial communities from Ay and Ks series amended with different concentration of SNP
interest and the total fluorescence of all fragments in the profile. The community profiles were normalized and analyzed by using BioNumerics v.3.5 software (Applied Maths, Sint-Martens-Latem, Belgium). Community profile similarities were calculated by using Pearson’s product-moment correlation coefficient, with 1 % optimization. Results were analysed with accounting for the covariance structure by using the multidimensional scaling (MDS) and multivariate analysis of variance (MANOVA),
forms of discriminant analysis, subroutines of Bionumerics software. Soil microbial activity analysis Soil CO2 respiration and N mineralization were used to determine soil microbial activity. Soil microcosms were set-up as described above. The CO2 trap contained 10 ml of 1 M NaOH. At each sampling time, the jam jars were opened, all of the
123
Second discriminant
Second discriminant
World J Microbiol Biotechnol
0 ppm 50 ppm 100 ppm 250 ppm 500 ppm
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First discriminant
(A) Second discriminant
Second discriminant
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(B) (B) Fig. 2 Multivariate analysis of variance (MANOVA) ARISA profiles of bacterial communities from Ay series showing effects of SNP concentrations (a) and incubation periods (b)
Fig. 3 Multivariate analysis of variance (MANOVA) ARISA profiles of bacterial communities from Ks series showing effects of SNP concentrations (a) and incubation periods (b)
Results and discussion NaOH in the trap and 150 g of soil were removed. The amounts of CO2 evolved and trapped in the 1 M NaOH were determined as described by Hopkins (2008). The mineralization of N during soil microcosm incubations was examined by measuring accumulations of NO3–N and extractable NH4–N in 10 g aliquots of soil using the method described by Keeney and Nelson (1982).
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Effect of SNP on some soil chemical properties The soil samples had the following characteristics. The Ay series soil had a clay texture with 7.02 % sand, 34.58 % silt, and 58.40 % clay; 1.85 % organic matter; pH of 4.88, EC of 2.35 dSm-1; and CEC of 13.39 cmol kg-1. In
World J Microbiol Biotechnol
contrast, the Ks series had a sandy loam texture with 54.99 % sand, 44.17 % silt, and 0.84 % clay; 5.76 % organic matter; pH of 6.84; EC of 1.45 dSm-1; and CEC of 30.96 cmol kg-1. The investigation of the effect of SNP on soil properties indicated that different concentrations of SNP (50, 100, 250, and 500 lg/g soil) and incubation periods of 0, 2, 4 and 8 weeks did not affect pH, EC, CEC, and organic matter in both the Ay and Ks soil series (data not shown). Similar results were previously reported by Ha¨nsch and Emmerling (2010). They found that no treatment effects were found for pH and soil organic carbon. Effect of SNP application on bacterial community structure The results from ARISA profiles showed the effects of different SNP concentrations and incubation times on bacterial community structure. The analysis of bacterial amplicon distributions from SNP concentrations and incubation periods showed that bacterial community structure decreased with increasing SNP concentration and increasing incubation period in both the Ay and Ks series soils. The Pearson correlation coefficient analysis (Fig. 1) and MDS analysis (data not shown) showed that the effects of SNP on bacterial community structure depended on soil type more than the SNP concentration and incubation period. Multivariate analysis of variance (MANOVA) was performed to determine if SNP concentrations and incubation periods were significant variables affecting bacterial community structure. For the Ay series soil, the results indicated that community composition did not cluster by concentration (Fig. 2a) or incubation period (Fig. 2b), except for the control samples. In contrast, for the Ks series soil, the results showed that microbial constituents of soils clustered by both SNP concentrations (Fig. 3a) and incubation periods (Fig. 3b). Effect of SNP application on soil microbial activity The results of soil respiration illustrated that the amount of CO2 emissions decreased with increasing SNP application rates and incubation periods in both soil types (data not show). Moreover, the results showed that SNP application rate affected the amount of CO2 emissions. Results of N mineralization studies distinctly showed that the available nitrogen in the ammonium (NH4?) form in the Ay series soil decreased steadily from initial amount to the second week, and then leveled off until week 8 (data not shown). In contrast, the nitrate–N (NO3-) in the Ay series increased steadily from the initial amount to the second week, and then leveled off until week 8. The ammonium form in the Ks soil slightly decreased and its
trend was downward from the beginning to week 8, while the nitrate form slightly increased to week 4 and then decreased to week 8 (data not shown). However, the results indicated that the amount of SNP application rates had no effect on N mineralization in both soil types. Taken together, our results indicate that SNP concentrations and incubation times can alter microbial community structure and function in some soils. While our studies confirm the results of Colman et al. (2013), it appears that the influence of SNP on soil microbiota likely depends on soil types, as well as concentrations and time of exposure. Acknowledgments This work was supported by a grant from the Graduate School, Kasetsart University and Center for Advanced Studies in Agriculture and Food, KU Institute for Advanced Studies, Kasetsart University, and The Commission on Higher Education.
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