Effects of cultivation of OsrHSA transgenic rice on functional diversity of microbial communities in the soil rhizosphere

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Effects of cultivation of OsrHSA transgenic rice on

functional diversity of microbial communities in the

soil rhizosphere

Xiaobing Zhang

a,b

, Xujing Wang

a,

, Qiaoling Tang

a

, Ning Li

c

, Peilei Liu

c

, Yufeng Dong

a

,

Weimin Pang

a

, Jiangtao Yang

a

, Zhixing Wang

a,

aBiotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China bBiology Institute, Hebei Academy of Sciences, Shijiazhuang 050051, China

cDevelopment Center of Science and Technology, Ministry of Agriculture, Beijing 100122, China

A R T I C L E I N F O

A B S T R A C T

Article history:

Received 11 August 2014 Received in revised form 13 November 2014 Accepted 4 January 2015 Available online 13 January 2015

With the widespread cultivation of transgenic crops, there is increasing concern about unintended effects of these crops on soil environmental quality. In this study, we used the Biolog method and ELISA to evaluate the possible effects of OsrHSA transgenic rice on soil microbial utilization of carbon substrates under field conditions. There were no significant differences in average well-color development (AWCD) values, Shannon–Wiener diversity index (H), Simpson dominance indices (D) and Shannon–Wiener evenness indices (E) of microbial communities in rhizosphere soils at eight samplings between OsrHSA transgenic rice and its non-transgenic counterpart. The main carbon sources utilized by soil microbes were carbohydrates, carboxylic acids, amino acids and polymers. The types, capacities and patterns of carbon source utilization by microbial communities in rhizosphere soils were similar throughout the detection period. We detected no OsrHSA protein in the roots of OsrHSA transgenic rice. We concluded that OsrHSA transgenic rice and the rHSA protein it produced did not alter the functional diversity of microbial communities in the rhizosphere.

© 2015 Production and hosting by Elsevier B.V. on behalf of Crop Science Society of China and Institute of Crop Science, CAAS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Biolog method ELISA Soil microbes Functional diversity

1. Introduction

The development of transgenic crops has brought huge economic benefits along with concerns of ecological safety, such as possible effects on soil organisms. Numerous studies have been carried out to investigate the effects of transgenic crops on soil microbial communities [1–3]. Most results indicate that transgenic Bt crops have not caused significant effects on soil microbes compared to conventional crops

[4–10]. OsrHSA transgenic rice is transformed with the human serum albumin gene that produces a soluble medic-inal globular protein[11]that has attracted attention regard-ing its environmental safety. However, few studies have examined the effects of OsrHSA transgenic rice on soil microorganisms. The aim of this study was to evaluate the effect of OsrHSA transgenic rice on soil microbial functions under field conditions and to provide a scientific basis for evaluating the safety of OsrHSA transgenic rice in China.

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⁎ Corresponding authors.

E-mail addresses:xujingwang0514@126.com(X. Wang),wangcotton@126.com(Z. Wang).

Peer review under responsibility of Crop Science Society of China and Institute of Crop Science, CAAS. http://dx.doi.org/10.1016/j.cj.2014.11.001

2214-5141/© 2015 Production and hosting by Elsevier B.V. on behalf of Crop Science Society of China and Institute of Crop Science, CAAS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A v a i l a b l e o n l i n e a t w w w . s c i e n c e d i r e c t . c o m

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Materials

and

methods

Sampling and pretreatment study carried out using OsrHSA transgenic rice and its sgenic parent cultivar TP309 as a control, was d a t a n experimenta l farm located in Xiantao, China (30 °N, 113 °E). The soil type was paddy (pH 7.8, N 2.24 g k g − 1 , total P 0.61 g k g − 1 , total K 18.2 g k g − 1 ; matter 28.4 g k g − 1 ). All treatments were set up in a ly randomize d design with three replica tes. Soil samples collected from five points in an S-shaped across each field were taken from depths of approxi -10 cm, along with as much associated root material as Sampling occurred at the tillering, jointing, milk and stages, and at 83, 204, 261, and 321 days after the rice harveste d. The soils were placed into plastic bags, which placed in ice cube boxes and immediately taken to the y, where they were stored in a refrigerator at 4 °C and ntly used for determi nation of soil microbial . Biolog cultivation and statistical analysis rice rhizosphere (root syst em) w a s sh ake n vigorou sly to e lar g e soil p ar tic les an d the rhi zosp h ere soi lw a s co ll ec te d 1 – The standard curve of protein determina tion using BCA method. 2 – T h e st a n da rd cu rv e o f p ro te in de te rm in a tio n b y E L IS A .

Table 1– Functional indexes of soil microbial communities.

Sampling stage# AWCD Diversity index (H) Dominance index (D) Evenness index (E)

TP309 Transgenic rice TP309 Transgenic rice TP309 Transgenic rice TP309 Transgenic rice

Tillering stage 0.780 ± 0.253# ab 0.761 ± 0.271#ab 4.224 ± 0.135#a 4.233 ± 0.148#abc 0.983 ± 0.004#b 0.982 ± 0.004#ab 0.946 ± 0.029#a 0.959 ± 0.025#a Jointing stage 1.230 ± 0.032# c 1.359 ± 0.096#c 4.388 ± 0.052#a 4.446 ± 0.018#c 0.987 ± 0.001#b 0.988 ± 0.000#b 0.983 ± 0.013#bc 0.988 ± 0.004#a Milk stage 1.040 ± 0.057# bc 1.066 ± 0.143#bc 4.294 ± 0.022#a 4.343 ± 0.019bbc 0.985 ± 0.001#b 0.986 ± 0.001#b 0.959 ± 0.006#ab 0.969 ± 0.002#a Maturity sage 0.996 ± 0.133# bc 1.167 ± 0.035#bc 4.342 ± 0.060#a 4.419 ± 0.025#bc 0.986 ± 0.001#b 0.987 ± 0.000#b 0.970 ± 0.006#ab 0.981 ± 0.008#a 83 days# 0.908 ± 0.051# bc 0.854 ± 0.041#ab 4.212 ± 0.049#a 4.204 ± 0.037#ab 0.984 ± 0.001#b 0.983 ± 0.000#ab 0.941 ± 0.008#a 0.953 ± 0.007#a 204 days 1.156 ± 0.232# bc 1.058 ± 0.157#bc 4.303 ± 0.117#a 4.310 ± 0.050#bc 0.985 ± 0.002#b 0.985 ± 0.001#b 0.963 ± 0.021#ab 0.981 ± 0.013#a 261 days 0.429 ± 0.061# a 0.515 ± 0.254#a 4.211 ± 0.143#a 4.062 ± 0.162#a 0.968 ± 0.006#a 0.963 ± 0.023#a 1.015 ± 0.044#c 0.998 ± 0.073#a 321 days 1.112 ± 0.180# bc 1.116 ± 0.184#bc 4.344 ± 0.051#a 4.394 ± 0.027#bc 0.985 ± 0.002#b 0.986 ± 0.001#b 0.981 ± 0.018#ab 0.982 ± 0.008#a # Sampling time after the rice harvest: 83, 204, 261, and 321 days. All values represent mean ± SD of triplicate samples. Different letters indicate significant difference (P < 0.01). Superscripts represent

variations between varieties; subscripts represent variations between growth stages.

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from the root surfaces. Fresh 2 g soil samples were placed in 20 mL of sterile physiological saline solution, sealed with sealing film and shaken. The soil suspensions were stepwise diluted to 1000 times and added to Biolog Eco microplates (Biolog Co., CITY, USA). The inoculated microplates were incubated at 28 °C. Color development in the wells was measured as the absorbance value with a microplate reader (Bio-Tek Instruments Inc., USA) after 24, 48, 72, 96, 120, 144, and 168 h.

Following inspection of the data the absorbance values at 72 h were subjected to statistical analysis. An overall micro-bial activity indicator was estimated by average well-color development (AWCD) value, which reflected the capacity of carbon source utilization and metabolic activity of soil microbes. It was calculated as follows:

AWCD ¼ ∑ C–Rð Þ=n ð1Þ

where C indicates the absorbance values of media of each well; R indicates the absorbance values of the control well; n

indicates the number of carbon source types in the Biolog Eco microplate.

The Shannon–Wiener diversity index (H), Simpson domi-nance index (D) and Shannon–Wiener evenness index (E) were used to assess the richness, dominating population and evenness of soil microorganisms, respectively. These indices were calculated using the formulae:

H ¼ −∑ Pð i  ln PiÞ ð2Þ

D ¼ 1 –∑ Pi2 ð3Þ

where Piindicates the ratio of the relative absorbance value in the ith well to the total relative absorbance values in the entire microplate.

E ¼ H=Hmax ¼ H= ln S ð4Þ

where H indicates the Shannon–Wiener diversity index and S indicates the number of wells with color variation.

Table 2– Loading factors of principle components of 31 types of carbon sources.

Code Type of carbon source TP309 Transgenic rice

PC1 PC2 PC1 PC2

A2 β-methyl-D-glucoside (carbohydrate) 0.820 −0.334 0.898 −0.092

A3 D-galactonic acidγ-lactone (carboxylic acid) 0.739 0.025 0.862 0.096

A4 L-arginine (amino acid) 0.725 0.371 0.852 −0.016

B1 Pyruvic acid methyl ester (others) 0.243 0.207 0.613 −0.455

B2 D-xylose (carbohydrate) 0.574 −0.203 0.672 −0.083

B3 D-galacturonic acid (carboxylic acid) 0.830 0.223 0.686 0.035

B4 L-asparagine (amino acid) 0.794 0.297 0.599 −0.087

C1 Tween 40 (polymer) 0.896 0.013 0.677 −0.468

C2 i-erythritol (carbohydrate) 0.758 −0.213 0.714 0.077

C3 2-hydroxy benzoic acid (carboxylic acid) 0.667 −0.126 0.504 0.388

C4 L-phenylalanine (amino acid) 0.379 −0.442 0.770 0.221

D1 Tween 80 (polymer) 0.616 −0.176 0.583 −0.496

D2 D-mannitol (carbohydrate) 0.797 0.229 0.814 –0.200

D3 4-hydroxy benzoic acid (carboxylic acid) 0.836 0.238 0.752 −0.192

D4 L-serine (amino acid) 0.861 0.275 0.860 −0.212

E1 α-cyclodextrin (polymer) 0.466 0.072 0.714 0.241

E2 N-acetyl-D-glucosamine (carbohydrate) 0.927 −0.105 0.909 −0.116

E3 γ-hydroxybutyric acid (carboxylic acid) 0.011 0.498 0.470 0.545

E4 L-threonine (amino acid) 0.430 −0.297 0.767 −0.001

F1 Glycogen (polymer) 0.896 −0.192 0.888 0.025

F2 D-glucosaminic acid (carboxylic acid) 0.497 0.449 0.469 0.515

F3 Itaconic acid (carboxylic acid) 0.579 −0.178 0.518 −0.093

F4 Glycyl-L-glutamic acid (amino acid) 0.663 −0.111 0.706 0.285

G1 D-cellobiose (carbohydrate) 0.860 −0.102 0.858 0.010

G2 Glucose-L-phosphate (others) 0.817 −0.309 0.793 −0.250

G3 α-ketobutyric acid (carboxylic acid) 0.568 0.247 0.667 0.101

G4 Phenylethylamine (amine) 0.637 0.277 0.722 0.157

H1 α-D-lactose (carbohydrate) 0.718 −0.355 0.851 0.058

H2 D,L-α-glycerol phosphate (others) 0.649 −0.439 0.652 −0.389

H3 D-malic acid (carboxylic acid) 0.557 0.534 0.713 0.442

H4 Putrescine (amine) 0.498 0.202 0.674 0.308

Table 3– Score of principal variables affecting PC1 and PC2.

Sample Principle component Carbohydrate Carboxylic acid Amino acid Polymer Others Amine

TP309 PC1 5.454 5.284 3.852 2.874 1.709 1.135 PC2 −1.083 1.910 0.093 −0.283 −0.541 0.479 Transgenic rice PC1 5.716 5.641 4.554 2.862 2.058 1.396 PC2 −0.346 1.837 0.190 −0.698 −1.094 0.465

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Variance analysis was performed using SPSS (SPSS for Windows, Version 16.0, USA).

2.3. Extraction and quantification of OsrHSA protein

Three grams of roots, stems, leaves and spikes (fresh weight equivalent) were ground. Aliquots of approximately 400 mg of crushed tissue samples were separately mixed with 1.0 mL protein extraction reagent (Plant Protein Extraction Kit, Beijing Cowin Bioscience Co., Ltd., China). The suspensions were centrifuged, and the clarified supernatants were trans-ferred to new tubes.

Total soluble protein concentration (Fig. 1) was measured with a BCA Protein Assay Kit (Beijing Cowin Bioscience Co., Ltd., China) and calculated as follows:

y ¼ 0:2192x þ 0:0103: ð5Þ

OsrHSA protein (Fig. 2) was quantified using a Human Albumin ELISA Quantitation Set (Bethyl Laboratories, Inc., USA) and calculated as:

y ¼ exp 1:166 – 17:810= x þ 5:197½ ð Þ: ð6Þ

3. Results and discussion

The Biolog system is an effective approach that is commonly employed to assess spatial and temporal changes in microbial communities. Higher AWCD values indicate higher metabolic activity by microbial communities. Higher H, D, and E suggested higher functional diversity of soil microbes and more uniform distribution of individuals among species. As shown inTable 1 differences in the AWCD, H, D, and E of microbial communities between OsrHSA transgenic rice and its non-transgenic rice TP309 counterpart were not significant at any observation point.

The 31 carbon source substrates on the microplate were divided into six categories: carbohydrates, amino acids, carbox-ylic acids, polymers, amines and metabolism products. The initial loading factor reflects the correlation coefficient between princi-pal components and carbon utilization. As shown inTable 2, 25 carbon sources used by soil microbes on the transgenic rice had relatively high correlation with PC1, 20 substrates used by soil microbes on TP309 had relatively high correlation with PC1. According to the scores of variables affecting PC1 and PC2 (Table 3), the main carbon sources utilized by soil microbes on OsrHSA transgenic and non-transgenic rice are carbohydrates, carboxylic acids, amino acids and polymers.

The principle behind principal component extraction is to extract the first m principal components with corresponding eigenvalues greater than one. Based on this concept, a total of seven principal components were extracted, with a cumulative contribution rate of 86.82%. Specifically, the contribution rate of PC1 was 65.07%, accounting for the greatest proportion, and the contribution rate of PC2 was 7.00%. Consequently, PC1 as the horizontal axis and PC2 as the vertical axis were employed to construct a principal component analysis diagram of carbon utilization, and the scores of the two principal components were used as the coordinates (Fig. 3). As shown inFig. 3, On the

PC1 axis, both OsrHSA transgenic rice and its non-transgenic counterpart were distributed in the positive range for all eight stages. On the PC2 axis, they were distributed in the positive direction as well. These results demonstrate similar capacities and patterns of carbon source utilization by microbial commu-nities on both genotypes.

There are numerous factors that affect evaluations of the safety of transgenic plants, such as temperature, soil type, exogenous chemicals and plant species. Liu et al.[12]and Lee et al. [13] reported differences associated with the plant growth stage. We determined that significant variations were associated with different sampling times; these might be due to seasonal or temperature fluctuations.

We quantified OsrHSA protein using ELISA, which allowed a threshold detection of 6.25 ng mL−1. OsrHSA protein was detected only in the spikes of transgenic rice at the milk and maturity stages (Fig. 4). Although a strong endosperm-specific promoter and its signal peptide were used in the transgenic line OsrHSA protein was expressed only in the endosperm[11]. In this study, OsrHSA protein was not detected in transgenic rice roots. Moreover, protein released into soil cannot accumulate Fig. 3– Principal component analysis of carbon utilization by soil microbial communities in different treatment groups.

Fig. 4– OsrHSA and total protein in rice. All values refer to fresh tissue weight and represent means ±SD of triplicate samples. * Indicates“below detection limit”.

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[9]. Therefore, OsrHSA protein had no effect on the functional diversity of soil microorganisms.

Acknowledgments

This work was supported by the Major Project of China on New Varieties of GMO Cultivation (2012ZX08011-003 and 2014ZX08011-004B). We thank Prof. Daichang Yang (College of Life Sciences, Wuhan University, China) for providing test specimens.

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References

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