Enhanced Green Fluorescent Protein Transgenic Expression In

Jul 5, 2013 - Department of Laboratory Animal Science, Third Military Medical ... University of Chinese Academy of Sciences, Beijing 100049, P. R. Chi...
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Enhanced Green Fluorescent Protein Transgenic Expression In Vivo Is Not Biologically Inert Hongde Li,†,‡ Hong Wei,§ Yong Wang,§ Huiru Tang,† and Yulan Wang*,†,∥ †

Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China § Department of Laboratory Animal Science, Third Military Medical University, Chongqing 400038, P. R. China ‡ University of Chinese Academy of Sciences, Beijing 100049, P. R. China ∥ Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, P. R. China S Supporting Information *

ABSTRACT: Enhanced green fluorescent protein (EGFP) is a widely used biological reporter. However, the effects of EGFP expression in vivo are still unclear. To investigate the effects of EGFP transgenic expression in vivo, we employed an NMR-based metabonomics method to analyze the metabonome of EGFP transgenic mice. The results show that the metabonomes of urine, liver, and kidney of the EGFP transgenic mice are different from their wild-type counterparts. The EGFP mice expressed high levels of urinary 3-ureidopropionate, which is due to the downregulated transcriptional level of β-ureidopropionase. The expression of EGFP in vivo also affects other metabolic pathways, including nucleic acid metabolism, energy utilization, and amino acids catabolism. These findings indicate that EGFP transgenic expression is not as inert as has been considered. Our investigation provides a holistic view on the effect of EGFP expression in vivo, which is useful when EGFP is employed as a functional biological indicator. Our work also highlights the potential of a metabonomics strategy in studying the association between molecular phenotypes and gene function. KEYWORDS: 3-ureidopropionate, EGFP, metabolism, metabonomics, transgene

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INTRODUCTION Green fluorescent protein (GFP) is a 26.9 kDa protein isolated from the jellyfish Aequorea victoria and exhibits green fluorescence when exposed to blue light. It is widely used as a biological reporter to identify tissue and cells with a target gene expression.1−5 Enhanced green fluorescent protein (EGFP) is a derivative of GFP with two point mutations at S65T and F64L. The two point mutations make the fluorescence of EGFP stronger compared to the normal GFP.6 Therefore, EGFP is considered more suitable for expression studies in mammalian cells as a coexpression marker. Presently, no obvious detrimental effects of EGFP have been reported in vitro.7−9 Most studies have also suggested that EGFP is biologically inert in vivo, with no evidence of toxicity.10,11 However, Huang et al. found that transgenic expression of EGFP could induce cardiomyopathy.12 Stripecke et al. also found that the BM185 pre-B leukemia cells with EGFP expression inhibited tumor development in BALB/c mice.13 Therefore, the biological effect of EGFP expression in vivo is still debatable. Previous studies have mainly focused on the morphological changes and were based on the conventional hypothesis-driven strategies. Here, we investigate holistic © 2013 American Chemical Society

metabolic alterations associated with EGFP transgenic expression in vivo to re-enforce the effects of EGFP. A metabonomics technique could provide an effective approach to evaluate the global biological effects associated with EGFP transgenic expression at the molecular level. Metabonomics is a holistic systems approach capable of capturing the metabolic responses of a living system to external stimuli.14,15 Small molecular metabolites in complex biological samples are measured by nuclear magnetic resonance (NMR) or mass spectrometry, and with the assistance of multivariate data analysis such as orthogonal projection to latent structure discriminant analysis (OPLS-DA),16 the important differences in metabolic profiles can be identified. The detectable small molecular metabolites can involve many different metabolic pathways; therefore, metabonomic analyses can provide a wider vision of the metabolic changes. In recent years, the metabonomics approach has been successfully applied in many research fields, such as toxicology,17−19 nutritional research,20−22 gene function23 and host−microbe interacReceived: June 15, 2013 Published: July 5, 2013 3801

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tions.24−26 Global metabonomics analysis with the aid of targeted investigation has been demonstrated to provide metabolic alterations with more detail and great reliability.27 For example, determining the transcriptional levels of key enzymes, using real time PCR (RT-PCR), in the metabolic pathways offers insights into the changes of enzymes involved in the production of specific metabolites, and this can facilitate the analysis of metabolic changes with greater depth. In this study, we took a combinational approach of metabonomics analysis and RT-PCR analysis of an enzyme of interest as directed by the metabonomics analysis. We first employed an NMR-based metabonomics method to analyze the metabonomes of the biofluids, liver and kidney of EGFP transgenic mice. We then validated our metabonomic result by using RT-PCR to determine the transcriptional level of key enzymes that were related to the changed metabolic pathway. Our research demonstrates the benefit of the combined analytical approach, which is applicable to investigate effects of genetic modification. The information provided here sheds light on the metabolic effect of EGFP expression in vivo, which should be taken into consideration when EGFP is used as a biological reporter.

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water/methanol solution (1:2), and this procedure was repeated twice. All of the supernatants were mixed together and lyophilized after removing methanol. The powder was dissolved in 600 μL of 0.1 M buffer (pH = 7.4, 50% D2O, K2HPO4/NaH2PO4 = 4:1, 0.001% TSP and 0.1% NaN3), and centrifuged at 16000× g at 4 °C for 10 min; 550 μL of supernatant was transferred into 5 mm NMR tube for NMR detection. NMR Spectroscopy

One dimensional (1D) 1H NMR spectra of urine, liver and kidney extracts were acquired at 298 K on a Bruker AVIII 600 MHz NMR spectrometer with an inverse detection cryogenic probe (Bruker Biospin, Germany). The first increment of NOESY pulse sequence with continuous wave irradiation on water peak during recycle delay and mixing time for water suppression was employed. Recycle delay of 2 s and mixing time of 100 ms were set. The 90° pulse length was adjusted to 10 μs approximately and 64 scans were collected into 32 k data points for each spectrum with the spectral width of 20 ppm. To assist the assignment, two-dimensional (2D) NMR spectra including 1H−1H COSY, 1H−1H TOCSY, 1H Jresolved, 1H−13C HSQC and 1H−13C HMBC for typical samples were acquired and processed with the similar parameters as described previously.29

MATERIALS AND METHODS

Chemicals

Sodium 3-(trimethylsilyl) [2,2,3,3-2H4] propionate (TSP) was obtained from Cambridge Isotope Laboratories (Miami, FL). D2O (99.9% in D) was purchased from Sigma-Aldrich Inc. (St. Louis, MO). NaH2PO4·2H2O and K2HPO4·3H2O were purchased from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, China).

Multivariate Data Analysis

The free induction decays (FID) were multiplied by an exponential function with a line-width factor of 1 Hz before Fourier transformation. The phase and baseline corrections were performed manually and the spectrum was calibrated using the peak of TSP at δ 0.0 with software Topspin 3.0 (Bruker Biospin). The spectral region from δ 0.5 to δ 9.5 was integrated into bins with the width of δ 0.004 employing AMIX package (v3.8, Bruker Biospin). The ranges (δ 4.51−5.17 and δ 5.48−6.00) for water and urea peaks in urine spectra were removed and each bin area was normalized to the total area of the respective spectrum. For the liver and kidney extraction, the integral range between δ 4.6−5.15 was discarded due to imperfect water peak suppression. The area of each bin was normalized to the weight of liver or kidney tissues used for the extraction. Multivariate data analysis was performed with the software SIMCA-P+ (v12.0, Umetrics, Sweden). The model was constructed using the orthogonal projection to latent structure-discriminant analysis (OPLS-DA) with Pareto variance (Par) scaling and validated using a 7-fold cross validation method. The parameters generated from the OPLSDA model were R2X, the total variation being explained by the model, and Q2, representing the predictability of the model. The models were further validated by an additional validation method, variance analysis of the cross-validated residuals (CVANOVA) (P < 0.05).30 To assist the interpretation of the loadings generated from the models, the loadings were first back-transformed and plotted with color-coded OPLS-DA coefficients in MATLAB 7.1 using an in-house script.31 The color code stands for the absolute value of Pearson correlation coefficients (|r|), which indicates the weight of each variable contributing to the differentiation between groups. Here, the value of |r| greater than 0.514 was considered to be important (n = 14, P < 0.05).

Animal Experiments and Sample Collection

An animal experiment was carried out according to the National Guidelines for Experimental Animal Welfare (MOST, P. R. China, 2006) in a SPF facility certified by the Hubei Provincial Office of Science and Technology (SYXKE 2009− 0051). A total of 14 female lentiviral vector-mediated EGFP transgenic mice and 14 female FVB/N mice (wild-type as control) were provided by Department of Laboratory Animal Science, Third Military Medical University (Chongqing, China). All of the mice were fed freely with standard food and water. All of the animals were sacrificed at the age of 8 weeks by neck dislocation. The urine and blood plasma of each mouse were collected before the sacrifice. Livers and kidneys were collected immediately after sacrifice. All of the samples were snap frozen in liquid nitrogen and stored at −80 °C until NMR analysis. Since no difference in the metabolic profiles of blood plasma was observed between EGFP mice and the wildtype, no further description of plasma is given. Sample Preparation

A total of 100 μL urine sample was mixed with 450 μL of 10% D2O and 55 μL of 1.5 M buffer (pH = 7.4, K2HPO4/NaH2PO4 = 4:1, 0.05% TSP and 0.1% NaN3),28 centrifuged (16000× g, 4 °C) for 10 min; 550 μL of supernatant was transferred into a 5 mm NMR tube for NMR analysis. For the liver and kidney samples, 50 mg of tissue was excised from the same site of each sample, weighed and homogenized with 0.6 mL of cold water/ methanol solution (1:2) using a tissuelyser (QIAGEN, Hilden, Germany) at 20 Hz for 90 s. The supernatant was obtained after centrifugation at 10000× g at 4 °C for 10 min. The remaining pellets were extracted again with 0.6 mL of cold 3802

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Figure 1. Discriminations of metabolic profiles of urine between EGFP mice and wild-type mice. Typical 600 MHz 1H NMR spectra of urine samples from (A) wild-type and (B) EGFP mice; (C) OPLS-DA score plot (left) and coefficient plot (right) showing the discrimination between 1H NMR spectra of urine from EGFP (T) and wild-type (W) mice. (|r| cutoff value is 0.514, n = 14, P < 0.05; CV-ANOVA: P = 1.71 × 10−7). Keys: 1, 2oxoglutarate; 2, 2-oxoisocaproate; 3, 2-oxoisovalerate; 4, 3-hydroxybutyrate; 5, 3-methyl-2-oxovalerate; 6, 3-ureidopropionate; 7, 4-cresol glucuronide; 8, acetate; 9, alanine; 10, allantoic acid; 11, aspartate; 12, choline; 13, cis-aconitate; 14, citrate; 15, creatine; 16, creatinine; 17, dimethylamine; 18, ethanolamine; 19, formate; 20, fumarate; 21, glucose; 22, glutamate; 23, glutamine; 24, glycerophosphocholine; 25, glycine; 26, glycogen; 27, guanidinoacetate; 28, guanine; 29, hippurate; 30, histidine; 31, hypoxanthine; 32, indoxyl sulfate; 33, inosine; 34, inosine 5′monophosphate (IMP); 35, isoleucine; 36, lactate; 37, leucine; 38, lysine; 39, malonate; 40, mannose; 41, methionine; 42, methylamine; 43, myoinositol; 44, nicotinamide; 45, phenylacetate; 46, phenylacetylglycine; 47, phenylalanine; 48, phosphocholine; 49, scyllo-inositol; 50, succinate; 51, taurine; 52, trimethylamine; 53, trimethylamine N-oxide; 54, tyrosine; 55, uracil; 56, uridine; 57, uridine 5′-monophosphate; 58, valine; 59, xanthine; 60, U1. For details of chemical shifts and multiplies, refer to Supporting Information Table S1.

Figure 2. Discriminations of metabolic profiles of kidney between EGFP mice and wild-type mice. Typical 600 MHz 1H NMR spectra of kidney aqueous extracts from (A) wild-type and (B) EGFP mice, and the region (δ 5.0−9.0) has been vertically expanded ×16; (C) OPLS-DA score plot (left) and coefficient plot (right) showing the discrimination between 1H NMR spectra of kidney aqueous extracts from EGFP (T) and wild-type (W) mice. (|r| cutoff value is 0.514, n = 14, P < 0.05; CV-ANOVA: P = 0.003). For identification of the peak numbers, refer to keys in Table 1 and Figure 1.

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Figure 3. Discriminations of metabolic profiles of liver between EGFP mice and wild-type mice. Typical 600 MHz 1H NMR spectra of liver aqueous extracts from (A) wild-type and (B) EGFP mice. The region (δ 5.7−9.0) has been vertically expanded ×8. (C) OPLS-DA score plot (left) and coefficient plot (right) showing the discrimination between 1H NMR spectra of liver aqueous extracts from EGFP (T) and wild-type (W) mice. (|r| cutoff value is 0.514, n = 14, P < 0.05; CV-ANOVA: P = 3.21 × 10−6). For identification of the peak numbers, refer to keys in Table 1 and Figure 1.

Table 1. Correlation Coefficients for Metabolites with Marked Differences between the EGFP Transgenic and Wild-type Micea urine

kidney

liver

metabolite (key)

δ (ppm)

R2X = 0.45; Q2 = 0.83

R2X = 0.58; Q2 = 0.52

R2X = 0.70; Q2 = 0.71

3-ureidopropionate (6) phenylacetate (45) guanidinoacetate (27) malonate (39) ethanolamine (18) inosine (33) fumarate (20) TMAO (53) scyllo-inositol (49) hypoxanthine (31) nicotinamide (44) UMP (57) alanine (9) glycogen (26) glucose (21) mannose (40)

3.30 7.47 3.80 3.11 3.14 8.24 6.52 3.28 3.35 8.19 8.71 6.01 1.48 5.42 5.25 5.19

+0.92 −0.67 −0.82 −0.64 −0.77 −0.59 +0.54 −0.55 −0.67 −0.68 −0.79 −0.81

−0.65

+0.66 −0.80 −0.74 −0.84

a

Positive and negative signs indicate the level of metabolite is higher (+) or lower (−) in the EGFP mice compared with the wild-type control; the keys of the metabolites are the same as Supporting Information Table S1 and Figure 1.

Quantitative RT-PCR

TGCGT-3′, reverse 5′-CCACAGGATTCCATACCCAAGA3′.

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Total RNA was isolated using Trizol (Invitrogen) and cDNA was synthesized using First Strand cDNA Synthesis kit (TOYOBO, Japan). Quantitative RT-PCR was performed on a SLAN Fluorescent Quantitation PCR Detection System (Shanghai Hongshi Medical Technology Co., Ltd., China) using TOYOBO THUNDERBIRD SYBR qPCR Mix. The primers were synthesized by Invitrogen Biotechnology Co., Ltd. Gene expression data were normalized to β-actin. The following primers were used: Upb1 forward 5′-TCACCTGTGCTCTCAACCGT-3′, reverse 5′-ATACATCTCAAGTCGTCCCGTC-3′; β-actin forward 5′-CTGAGAGGGAAATCG-

RESULTS

The Differences of the Metabolic Profiles

High-resolution 1D 1H NMR spectra from urine and aqueous tissue extracts provide abundant signals from metabolites presented in major metabolic pathways. For each biological sample, the typical spectra from a wild-type control mouse and an EGFP mouse are displayed in Figures 1A,B, 2A,B, and 3A,B. The assignment shown in Supporting Information Table S1 is facilitated by published data24,32 and confirmed with a range of 3804

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2D NMR spectra. A total of 59 metabolites involved in the metabolism of amino acids, carbohydrates and nucleotides are identified. 1D 1H NMR spectra of urine are dominated by taurine, trimethylamine-N-oxide (TMAO) and organic acids such as acetate, lactate, citrate and hippurate. The NMR spectra of kidney extracts are mainly consisted of resonance peaks from lactate, taurine, choline, inosine and a range of amino acids. Glycogen, glucose and amino acids resonance signals are the dominating metabolites in the 1D 1H NMR spectra of liver. The differences in the metabolic profiles were obtained by cross-validated OPLS-DA comparisons between wild-type and EGFP mice and displayed in the cross-validated scores plots and color-coded correlation coefficient loadings plots (C in Figures 1, 2 and 3). Here, the resonance peaks pointing upward and colored red indicates an increase in the levels of the metabolites in the EGFP transgenic mice whereas peaks pointing downward and colored red indicate a decrease. The statistical parameters of all the models and the main metabolic characteristics in EGFP group are summarized in Table 1. The cross-validated score plots and permutation test plots using the projection to latent structure-discriminant analysis (PLS-DA) are also given in Supporting Information Figures S1−3. The urinary profiles of EGFP transgenic mice are characterized by significant high levels of 3-ureidopropionate (3-UP) and low levels of guanidinoacetate and phenylacetate compared to the wild-type control (CV-ANOVA: P = 1.71 × 10−7, Figure 1C and Table 1). The metabolic profiles of the kidney from EGFP transgenic mice and the wild-type control are also different (CV-ANOVA: P = 0.003). Here the model generated from the profiles of kidney is on the boarder-line for the levels of significance (p < 0.05), however, considering the changes also observed in urine, the model is valid. The EGFP induced changes in kidney include decreased levels of ethanolamine, TMAO, scyllo-inositol, nicotinamide, uridine 5′monophosphate (UMP), inosine, hypoxanthine and malonate as well as the increased levels of fumarate in EGFP transgenic mice (Figure 2C and Table 1). The metabolic profiles of the liver obtained from EGFP transgenic mice significantly differ from those of the wild-type (CV-ANOVA: P = 3.21 × 10−6), which are manifested by decreased levels of inosine, glucose, glycogen and mannose as well as increased levels of alanine (Figure 3C and Table 1).

Figure 4. Transcriptional levels of gene Upb1 and blood glucose levels. (A) mRNA of β-ureidopropionase isolated from triplicate livers of the wild-type (W) and EGFP mice (T), determined by RT-PCR, normalized to β-actin (mean ± SD, t-test, P < 0.005). (B) Blood glucose levels determined by 1H NMR spectrum (mean ± SD, n = 14, t-test, P > 0.05).

Evidence of the Disturbance of Nucleic Acids Metabolism and Inflammatory Immune Response

3-Ureidopropionate, a catabolic intermediate of pyrimidine, is markedly increased in the urine of EGFP mice (Figure 1C and Table 1), and the correlation coefficient for 3-ureidopropionate is the most significant among all the detectable metabolites shown in the coefficient plots. Elevation of urinary 3ureidopropionate was found in mice infected with parasites in previous studies,33,34 and high levels of 3-ureidopropionate has also been reported in a patient with a β-ureidopropionase deficiency.35 To test whether the increased levels of 3ureidopropionate was caused by the down-regulation of βureidopropionase, which is an enzyme catalyzing the reaction from 3-ureidopropionate to β-alanine, the transcriptional level of β-ureidopropionase was determined by quantitative RTPCR. The result shows that β-ureidopropionase is downregulated in EGFP mice significantly (Figure 4A). Therefore, the accumulation of urinary 3-ureidopropionate is most probably caused by the decreased expression of β-ureidopropionase in EGFP mice. The decreased levels of inosine, uridine 5′-monophosphate (UMP) and hypoxanthine are found in the kidney obtained from EGFP mice compared to the wild-type mice (Figure 2C and Table 1). The low levels of inosine are also found in the liver of EGFP mice (Figure 3C and Table 1). Inosine can convert to hypoxanthine and vice versa in the metabolic pathway. Inosine is an important component of tRNA, and plays a crucial role in the proper translation of the genetic code in wobble base pairs.36,37 These observations suggest that there is a disturbance of nucleic acid metabolism and the protein translation. The previous research has shown that inosine has antiinflammatory effects and play an important role in host defense.38 Meanwhile, mannose is markedly decreased in the liver of EGFP mice; mannose is an important component of glycoprotein and mannose-binding lectin, which have crucial functions in anti-inflammation and innate immunity.39,40 The alternations of these metabolites suggest that EGFP expression in vivo could induce an immune response, which has been shown in previous investigation.13,41

The Transcriptional Level of β-Ureidopropionase

The levels of 3-ureidopropionate in the urinary profile are increased markedly among all the detectable metabolites shown in the coefficient plot. To confirm our results further, we measured the transcriptional level of β-ureidopropionase, which converts 3-ureidopropionate into β-alanine, in liver using RTPCR method. The down-regulated transcriptional level of the β-ureidopropionase is presented in the EGFP mice (Figure 4A).

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DISCUSSION In the current study, lentiviral vector-mediated EGFP transgenic mice are chosen to investigate the metabolic impact of EGFP transgenic expression in vivo. We analyze the metabonome of EGFP transgenic mice employing an NMRbased metabonomic method in combination with multivariate data analysis. The results clearly show that the metabolic profiles of EGFP mice and their wild-type are significant different, and the major systemic effects of EGFP on mouse metabolism are summarized in Figure 5.

Evidence of the Promotion of Glycogenolysis in Liver and Tricarboxylic Acid (TCA) Cycle in Kidney

The decreased levels of glycogen and glucose are found in the liver of EGFP mice, however, the levels of lactate and the 3805

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Figure 5. Summary of the major systemic effects of EGFP on mouse metabolism. Note: (↑) and (↓) indicated increased and decreased levels compared with the wild-type respectively. PEP, phosphoenolpyruvate; Upb1, β-ureidopropionase; PK, Pyruvate kinase; 3-UP, 3-ureidopropionate; SDH, succinate dehydrogenase; MBL, mannose-binding lectin.

intermediate products of the TCA cycle do not change significantly (Figure 3C). In addition, higher levels of alanine were noted in the liver of EGFP mice. The elevation of alanine in the liver has been shown to inhibit the activity of pyruvate kinase (PK) to suppress the glycolytic pathway.42,43 Hence, the current results suggest the promotion of glycogenolysis and the suppression of glycolysis pathway in liver to maintain the stable levels of blood glucose (Figure 4B) in the EGFP mice. Malonate, as a competitive inhibitor of succinate dehydrogenase (SDH),44,45 decreases significantly in the kidney of EGFP mice (Figure 2C and Table 1), which suggests high activity of succinate dehydrogenase, an enzyme catalyzing the reaction from succinate to fumarate. High activity of succinate dehydrogenase is consistent with the increased levels of fumarate in the kidney of EGFP mice. In addition, niacinamide, also known as vitamin B3, is decreased in the kidney of EGFP mice. Niacinamide is the component of the coenzyme nicotinamide adenine dinucleotide (NAD) participating in the TCA cycle. These results indicate that TCA cycle is promoted in the kidney of EGFP mice.

In summary, we have demonstrated that mice with lentiviral vector-mediated EGFP transgenic expression are metabolically different from their wild-type counterparts. These differences include variations in nucleic acid and amino acid metabolism. In particular, we discover down-regulated expression of βureidopropionase in the liver, which leads to the accumulation of urinary 3-ureidopropionate. The EGFP expression in vivo has an impact on the inflammatory immune response of EGFP transgenic mice. The present study provides new insights into the effect of EGFP expression in vivo, suggesting that EGFP expression is not as inert as believed and care should be taken when EGFP is employed as a functional biological indicator. Our work has also highlighted the application of the metabonomics strategy in studying the association between molecular phenotypes and gene function.

Evidence of the Suppression of the Amino Acid Catabolism and Renal Function

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ASSOCIATED CONTENT

S Supporting Information *

Figures S1−S3 and Table S1. This material is available free of charge via the Internet at http://pubs.acs.org.

Both phenylacetate and guanidinoacetate are decreased markedly in the urine of EGFP mice (Figure 1C). Phenylacetate is an intermediate of the metabolism of phenylalanine and most of the amino acids can be metabolized into guanidinoacetate through the urea cycle.46 Therefore, the decreased levels of phenylacetate and guanidinoacetate suggest a suppression of the catabolism of amino acids. In addition, the levels of TMAO and scyllo-inositol, known as organic osmolytes in the cell,47 are decreased with concurrent decreased levels of ethanolamine in the kidney of EGFP mice. Ethanolamine is a component of phospholipids, which are major components of biological membranes.48 These observations imply that EGFP transgenic expression in mice could lead to a disturbed function of the kidney to some degree.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Telephone: +86-2787197143. Fax: +86-27-87199291. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge financial supports from the Ministry of Science and Technology of China (2010CB912501, 2009CB118804 and 2012CB934004) and the National Natural Science Foundation of China (20921004 and 21175149). 3806

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