Microbial Functional Diversity in Facilities Cultivation Soils of Nitrate Accumulation

Available online at www.sciencedirect.com

Procedia Environmental Sciences 8 (2011) 1064–1071 Procedia Environmental Sciences 13 (2012) 1037 – 1044

Procedia Environmental Sciences www.elsevier.com/locate/procedia

The 18th Biennial Conference of International Society for Ecological Modelling

Microbial Functional Diversity in Facilities Cultivation Soils of Nitrate Accumulation H.Y. Huanga,c, P. Zhoua,b, W.W. Shib, Q.L. Liua,b, N. Wangb, H.W Fenga,Y.E. Zhia,b b

a School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dong Chuan Rd, Shanghai 200240, China Key Laboratory of Urban Agriculture (South) of Ministry of Agriculture, Shanghai Jiao Tong University, 800 Dong Chuan Rd, Shanghai 200240, China c Bor. S. Luh Food Safety Research Center, Shanghai Jiao Tong University, 800 Dong Chuan Rd, Shanghai 200240, China

Abstract There are increasing concerns over facilities cultivation soils of nitrate accumulation in China. Nitrate pollution in secondary salinization soil is regarded as having potential effects on soil microbial communities. Our study was conducted to evaluate effects of secondary salinization on soil microbial functional diversity with the BIOLOG method. The results showed that Average well-color development (AWCD) values declined with the rising of nitrate concentrations to some extent. The results also exhibited that the accumulation of nitrate in soil decreased the carbon sources utilization rates and the microbial species diversity indices. It indicated that nitrate has significantly negative effects on the sole-carbon-source metabolic ability of soil microbial communities. The cluster analysis intuitively demonstrated the distance and relationship between each sample: soil samples with high nitrate content were more close to each other, while soil samples with low were more similar in distances. The principal component analysis (PCA) result further validated that nitrate was inversely correlated to microbial carbon sources utilization intensity and microbial diversity. The four carbon substrates, Carbohydrates, Miscellaneous, Amino acids and Polymers, could reflect most of the information about carbon sources utilization. Microorganisms preferred these four carbon substrates were more vulnerable to nitrate. Thus, these four carbon substrates could be one of the prioritized microbe carbon sources in soil bioremediation. © byby Elsevier B.V.Ltd. Selection and/or peer-review under responsibility of School of Environment, © 2011 2011Published Published Elsevier Beijing Normal University. Keywords: BIOLOG; Nitrate contaminated soil; Secondary salinization; Microbial diversity



Corresponding author. Tel.: +86-21-34206922; fax: +86-21-34205762. E-mail address: [email protected]

1878-0296 © 2011 Published by Elsevier B.V. Selection and/or peer-review under responsibility of School of Environment, Beijing Normal University. doi:10.1016/j.proenv.2012.01.097

H.Y.etHuang et al./ Procedia Environmental (2011) 1064–1071 H.Y. Huang al. / Procedia Environmental Sciences Sciences 13 (2012)8 1037 – 1044

1038

1065

1. Introduction Facility culturing soil polluted by nitrate has become a serious problem in many countries, including China. There are increasing concerns over nitrate contamination in facility culturing soils since the 1970s. Nitrate pollution in secondary salinization soil is regarded as having potential effects on soil microbial communities. In facility culturing agriculture, chemical-intensive farming is the conventional method to promote the productivity in a short period and leads to the accumulation of salinity in soils, among which the secondary salinization of soil is the predominant problem. It could limit the growth of crops, constrain agricultural productivity, and lead to the abandonment of agricultural soils. Nitrate accumulation in soils could be hazardous to environment in particular to the human health through food chain eventually [1,2]. Soil microorganism plays pivotal roles in soil ecosystem，it is a key driving force of soil formation, evolution, and soil fertility, and the most active component as well. The stability and function of soil ecosystem depends on the cycling of nutrients by the soil microbial communities. Soil microbial community structural and functional diversity are thought to be the most potential and sensitive indicator for soil ecosystem evaluation [3-5]. Soil secondary salinization is regarded as having potential effects on soil microbial diversity. Recently extensive researches have been focused on the relationship between the microbial community diversity and the processes occurring in the soil, and how they respond to environmental changes [6,7]. Various methods were used to research soil microbial diversity, such as the traditional plate counts of cultivation method, the molecular method, the phospholipids fatty acids analysis method and the BIOLOG method [8-10]. Molecular techniques based on DNA can provide information about gene diversity, thus have overcome the limitations of culture-based method but required high precision and great challenge. Phospholipid fatty acid (PLFA) analysis has been used as a culture-independent method of assessing the structure of soil microbial communities and determining gross changes that accompany soil disturbances. But the method cannot be used to characterize microorganisms to species. In addition, all of these methods can't obtain the overall activity and metabolic function information, the BIOLOG method is a compensation for these shortages. The BIOLOG microplate technique, a new method for microbial community functional diversity analysis, could supply the community level physiological profiles (CLPPs) based on the ability of microorganisms to oxidize different carbon substrates, thus could be applied to study the impact of nitrate accumulation of facility culturing soils. Compared with other environmental microbial community research methods, the BIOLOG method possess the following advantages : (1) high sensitivity and resolution; (2) acquirement of the microbial communities’ metabolic features fingerprint; (3) keeping the original metabolic features without bacteria isolated and cultured; (4) a rapid and efficient test[11]. This paper employs BIOLOG method to study the effects of secondary salinization on soil microbial functional diversity, and to provide theoretical basis for the microbial remediation of secondary salinization soils. 2. Materials and methods 2.1. Reagent and materials BIOLOG-EcoplateTM was from Biolog Inc., Hayward, CA, USA, Microplate Reader was from BioTek, Gene Company Limited, Constant Temperature Incubator (Model MIR-153) was from SANYO Electric Co.Ltd, Japan, AutoAnalyzer 3 (Model SEAL XY-2, BRAN+LUEBBE) was from A United Dominion Company.

1066

H.Y. Huang etEnvironmental al./ Procedia Environmental Sciences H.Y. Huang et al. / Procedia Sciences 13 (2012) 10378–(2011) 1044 1064–1071

1039

2.2. Site description and soil sampling Six soil samples (0-5cm), named S1-1, S2-1, S3-1, S4-1, S5-1and S6-1, were collected in October, 2010, from Nanhui District (N31.04690°, E121.78843°), Shanghai, China. Apart from control sample Table 1. Physical and chemical properties of soil sample (S6-1), the rest soils were rich in nitrate. Each sample was sieved through 2 mm sieve, and divided into two groups. One group was used for BIOLOG analysis immediately, and the other was weighed and oven-dried at 105°C to constant weight for the chemical properties analysis (table 1). The chemical properties were measured following routine methods (Agrochemistry Commission and Soil Science Society of China (ACSSSC), Society of China (ACSSSC), 1983). Table 1. Physical and chemical properties of soil sample

5.02 ±0.06

EC (ms cm-1) 6.47±0.15

Organic matter (g kg-1) 43.90±2.64

Nitrate N (mg kg-1) 1763±10.82

S2-1

7.14±0.20

3.31±0.11

36.27±1.73

453±10.12

S3-1

6.99±0.12

1.52±0.10

44.99±2.1

116±4.36

S4-1

5.70±0.09

6.21±0.08

71.67±2.22

1279±24.27

S5-1

6.43±0.12

5.47±0.18

35.58±1.56

781±10.97

S6-1

7.01±0.04

0.45±0.08

25.64±0.08

45±2.65

Ssoil samples

pH

S1-1

2.3. Plate preparation BIOLOG-EcoplateTM system was used to evaluate the CLPPs of above six soil samples. After soil samples were incubated at 25 °C for 24 hours, 10 g of soil sample was taken into 100 ml NaCl solution (0.85mol/l), then shaking for 30minutes at 250 rpm. The above soil suspension was tenfold diluted till 103 , and then transferred 150 µl of the soil dilution into each of 96 wells on the Eco-microplates, each soil sample was done in triplicate. Finally, the plates were incubated at 25 °C in dark environment. The optical density (OD) at 590nm in each well was recorded at regular 24 h intervals. The OD values under 0.06 were set to zero. 2.4. Data analysis The activity of microbial metabolism is often described as average well-color development (AWCD), the following formula is the computational method of AWCD: AWCD=∑ [(C-R)]/31

(1)

Shannon-Weaver index (H) is often used to describe the richness (the number of oxidize substrates) of microbial metabolism response. Shannon-Weaver index (H) is calculated as follows. H=-∑ [pi×㏑ pi],

(2)

pi= ODi/(∑ODi)

(3)

H.Y. Huang et al./ Procedia Environmental Sciences 8 (2011) 1064–1071 H.Y. Huang et al. / Procedia Environmental Sciences 13 (2012) 1037 – 1044

1040

1067

Simpson index(D) is used to describe the dominance of response. D=1-∑ (pi×pi)

(4)

Mclntosh index(U) is used to describe the evenness of response, and can reflect the conformance of microbial activity U= ( Pi2 ) Where C is the optical density of reaction well, R is the optical density of control well, pi is the ratio of the OD on each well to the sum of the OD on whole wells. The OD values of soil samples at 72 h was used to calculated H, D and U, since at this incubation time the highest rate of microbial growth was observed [12,13]. All statistical analysis was performed with SPSS, version 15.0. 3. Results 3.1. The characteristic of microbial carbon sources utilization The activity of microbial metabolism is usually described as average well-color development (AWCD). AWCD indicates the utilization intensity of sole carbon source by microorganisms, thus can reflect the activity of soil microorganisms [14]. Although the initial AWCD values of the six soil samples display a negligible difference, the overall utilization rates showed an escalating trend and great variation with the increasing of cultivation time (Fig. 1). The carbon sources utilization rate of S6-1 (the control) was significantly higher than other five, while S1-1 with the highest content of nitrate (Table 1) showed the lowest carbon sources utilization rate (Fig. 1). In general, the AWCD value showed a contrary trend with the increasing concentration of nitrate, that is the AWCD value is S6-1>S3-1>S2-1>S5-1>S4-1>S1-1, whereas the nitrate concentrations of soil samples is S1-1>S4-1>S5-1>S2-1>S3-1>S6-1.

Fig. 1. Average well-color development (AWCD) of six soil samples

(5)

1068

H.Y. Huang et al./ Procedia Environmental Sciences 8 (2011) 1064–1071 H.Y. Huang et al. / Procedia Environmental Sciences 13 (2012) 1037 – 1044

1041

Fig. 2. Diversity indices of microbial carbon sources utilization(a) Mclntosh index (U), (b) (c) Shannon-Weaver index (H), (c) Simpson index (D) The different letter on the top of the column showed as tatistically significant difference between the treatments at P = 0.05 level using one-way ANOVA.

3.2. Diversity indices of Microbial carbon sources utilization After incubated for 72hours, three diversity indices, Shannon-Weaver index (H), Simpson index (D) and Mclntosh index (U), were used to describe the diversity of microbe carbon sources utilization. It was quite remarkable that the H, D and U of S6-1 were at the highest level compared to the other five samples (Fig. 2). One-way ANOVA results showed that S6-1 had a significant correlation with soil nitrate concentration. The more serious salinization in soil, the lower richness, evenness and dominance soil microorganisms showed. 3.3. Principal component analysis and cluster analysis of soil microbial community In order to explore the effects of soil secondary salinization on microbial communities, further investigation needed to be done. The 31 sole carbon sources of Eco-microplate were divided into six categories: carbohydrates, amides, amino acids, polymers, carboxylic acids, and miscellaneous respectively, according to their chemical structure. The principal component and hierarchical cluster were analyzed on the basis of the six soil samples application rates of the six categories carbon sources (dates of AWCD of 144 h). We evaluated the similarity of the six soil samples based on AWCD values using the hierarchical cluster analysis method. The distance and relationship between each sample showed intuitively similar classification (Fig. 3). S1-1 were more close to S4-1 and S5-1(all of the three have high level of salinity) in carbon sources utilization, S6-1(the control) was close to S2-1 and S3-1(low level of salinity), which further indicated that salinity was relevant to the intensity of microbial metabolism. The principal component analysis (PCA) showed the first and the second principal component (PC1 and (PC2) accounted 47.46% and 25.57% of data variance respectively, 2 principal component factors explained 73.03% of total variance. Garland etc reported that samples located in different space were relevant to the ability of carbon substrates utilization [15]. S6-1, S2-1 and S3-1 were mainly distributed in

H.Y. Huang et al./ Procedia Environmental Sciences 8 (2011) 1064–1071 H.Y. Huang et al. / Procedia Environmental Sciences 13 (2012) 1037 – 1044

1042

Fig. 3. Cluster analysis of soils with different nitrate concentration (based on AWCD of 144h)

1069

Fig. 4. Principal components analysis on soil microbial communities Conclusion

the positive direction, whereas the high salinity samples S4-1, S5-1 and S6-1 were mainly distributed in the negative direction in PC1. The six soil samples presented an obvious metabolism variance in PC1. The six soil samples distributed relatively centralized in PC2 except for S5-1(Fig. 4). The four carbon sources: Carbohydrates, Miscellaneous, Amino acids and Polymers were significantly correlated with PC1 and PC2 (R>0.80), and could reflect most of the information about carbon sources utilization. 4. Discussion Our study indicated that secondary salinization of soil had prominent effects on microbial community. Firstly, the CLPP of soil microbial was relevant to the concentration of nitrate. Secondly, nitrate decreased the richness, evenness and dominance of microorganism. Also, the principal component analysis and cluster analysis demonstrated differences among different degree of nitrate accumulated soils. The hierarchical cluster analysis also demonstrated that nitrate contents did correlated with the AWCD values (represent metabolic activity), but not simple inversely correlated. The four carbon sources (Carbohydrates, Miscellaneous, Amino acids and Polymers) could reflect most of the information about carbon sources utilization. Microorganisms preferred these four carbon substrates were more vulnerable to nitrate. Thus, these four carbon substrates could be one of the prioritized microbe carbon sources in soil bioremediation. The results may partly due to that excessive nitrate changed the soil environment suitable for microorganism growth, thus constrained the activity of soil microbe. Our previous study illustrated that secondary salinization inhibited activities of soil enzymes and soil respiration, and our results were consistent with it. However, microbial communities are extremely complex group in the complicated and volatile soil environment; and many physical factors, chemical factors and anthropogenic factors can influence it (pH, temperature, soil organic matter, human management practices etc). Thus, to obtain an accurate theoretical basis of secondary salinization soils, a large number of soil samples should be taken and a lasting study should be done in future research. The BIOLOG method provides CLPPs of bacterial or fungal community’s ability to use specific carbon sources [16]. It is a fast, convenient and relatively economic way to study microbial communities, the large amount of date it produce can also help us give more comprehensive and thorough analysis. On the

1070

H.Y. Huang et al. / Procedia Sciences 13 (2012) 10378–(2011) 1044 1064–1071 H.Y. Huang etEnvironmental al./ Procedia Environmental Sciences

1043

other hand, the BIOLOG method also has a series of limitations: it bases on those organisms capable of utilizing available carbon sources, reflects only a small fraction of microorganisms in soil ecosystem. What’s more, it favors fast growing organisms, represents potential metabolic diversity not in situ diversity and sensitive to inoculums density [17]. Different extraction procedures, the optimum inoculums cell density, time and temperature of incubation of microplates, and interference of soil particles can generate some differences in metabolic profiles for a signal sample [18] .Nonetheless, it is widely used in the field of environmental microbiology, and is a useful and valuable tool especially when used in conjunction with other methods. However, mechanism discussing and other factors affecting still need further study to evaluate effects of nitrate contaminated soil. 5. Conclusion Microorganisms are vital to ecological cycle and stabilization of soil ecosystem, so our study is significant for the research of secondary salinization soil. We found that secondary salinization has prominently adverse effect on the soil microbial community. Furthermore, nitrate accumulation inhibited microbial activity. Secondary salinization degree correlates with the AWCD values (represent metabolic activity). The four carbon sources (Carbohydrates, Miscellaneous, Amino acids and Polymers) could reflect most of the information about carbon sources utilization. Microorganisms preferred these four carbon substrates were more vulnerable to nitrate. These four carbon substrates could be one of the prioritized microbe carbon sources in the bioremediation of secondary salinization soil.

Acknowledgements This work was supported by the National Natural Science Foundation of China (31071860), Special Fund for Agro-scientific Research in the Public Interest of China (200903056), Science Foundation of Shanghai (08DZ1900404), Science Foundation of Chongming of Shanghai (10DZ1960103), the National High Technology Research and Development Program (“863” Program) of China (2007AA10Z441).

References [1] Wang ZH. Li SX. Effects of N forms and rates on vegetable growth and nitrate accumulation. Pedosphere 2003;13:309–16. [2] Yang XY, Wang XF, Wei M, Yang FJ, Shi QH. Changes of Nitrate Reductase Activity in Cucumber Seedlings in Response to Nitrate Stress. Agr Sci Chin 2010;9:216–22. [3] Fuhrman Jed A. Microbial community structure and its functional implications. Nature 2009;27:193–9. [4] Mouillot D, Villeger S, Scherer-Lorenzen M, Mason NWH. Functional Structure of Biological Communities Predicts Ecosystem Multifunctionality. Plos One 2011;6:1–9. [5] Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al.. Ecology-Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 2001;294:804–8. [6] Orlando J, Chavez M, Bravo L, Guevara R, Caru M. Effect of Colletia hystrix (Clos), a pioneer actinorhizal plant from the Chilean matorral, on the genetic and potential metabolic diversity of the soil bacterial community. Soil Biol Biochem 2007;39: 276976. [7] Jiang W, Wang JJ, Tang JS, Hou F, Lu YT. Soil bacterial functional diversity as influenced by cadmium. Environ Earth 2010;59:1717–22.

1044

H.Y. Huang al. / Procedia Environmental Sciences Sciences 13 (2012)8 1037 – 1044 H.Y.etHuang et al./ Procedia Environmental (2011) 1064–1071

1071

[8] Schutter ME, Sandeno JM, Dick RP. Seasonal, soil type, and alternative management influences on microbial communities of vegetable cropping systems. Biol Fertil Soils 2001;34:397–410. [9] Kelly JJ, Tate RL. Use of BIOLOG for the analysis of microbial communities from zinc-contaminate soils. J Environ Qual 1998;27:601–8. [10] Preston-Mafham J, Boddy L, Randerson PF. Analysis of microbial community functional diversity using sole-carbonsource utilisation profiles - a critique. FEMS Microbiol Ecol 2002;42:1–14. [11] Jennifer L. Kirka, Lee A. Methods of studying soil microbial diversity. Journal of Microbiological Methods 2004;58: 169– 88. [12] Garland J L. Analytical approaches to the characterization of sample of microbial communities using patterns of potential C source utilization. Soil Biol Biochem, 1996;28:213–21. [13] Haack SK, Garchow H, Klug MJ, Forney LJ. Analysis of factors affecting the accuracy, reproducibility and interpretation of microbial community carbon source utilization patterns. App Environ Microbiol 1995;61:1458–68. [14] Zabinski CA, Gannon JE. Effects of recreational impacts on soil microbial communities. Environ Manage 1997;21:233–8. [15] Garland JL, Mills AL. Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source-utilization. Appl Environ Microb 1991;57:2351–9. [16] Zak J C. Functional diversity of microbial communities:A quantitative approach. Soil Biol Biochem 1994;26:1101–8. [17] Classen AT, Boyle SI, Haskins KE, Overby ST, Hart SC. Community-level physiological profiles of bacteria and fungi:plate type and incubation temperature influences on contrasting soils. FEMS Microbiol Ecol 2003;44:319–28. [18] Raphael Calbrix, Karine Laval, Sylvie Barray. Analysis of the potential functional diversity of the bacterial community in soil: a reproducible procedure using soli-carbon-source utilization profiles. Eur J Soil Biol 2005;41:11–20.