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Metabolite profiling of a Zn-accumulating rice mutant Yin Wang, Sha Mei, Zhixue Wang, Zhoulei Jiang, zhangshicang Zhu, Jingwen Ding, Dianxing Wu, and Xiao-Li Shu J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 25 Apr 2017 Downloaded from http://pubs.acs.org on April 26, 2017

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Journal of Agricultural and Food Chemistry

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Metabolite profiling of a Zn-accumulating rice mutant

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Yin Wang, Sha Mei#, Zhixue Wang, Zhoulei Jiang, Zhangshicang Zhu, Jingwen Ding, Dianxing

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Wu, Xiaoli Shu*

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State Key Laboratory of Rice Biology and Key Laboratory of the Ministry of Agriculture for the

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Nuclear-Agricultural Sciences, Department of Applied Biosciences, Institute of Nuclear

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Agricultural Sciences, Zhejiang University, Hangzhou 310029, China

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# equal contribution as the first author

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*Corresponding author

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Email: [email protected]

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ACS Paragon Plus Environment


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Abstract：Breeding crops with high Zn-density is an effective way to alleviate human dietary Zn

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deficiencies. We characterized a mutant Lilizhi (LLZ) accumulating at least 35% higher Zn

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concentration in grain than wild-type in hydroponics experiments. The mutant stored less Zn

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contents in the roots and transported more Zn to the grain. Metabolite profiling demonstrated that

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with high Zn treatment, the content of proline, asparagine, citric acid and malic acid were

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enhanced in both LLZ and the WT, which were thought to be involved in Zn transport in rice.

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Furthermore, the contents of cysteine, allothreonine, alanine, tyrosine, homoserine, β-alanine and

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nicotianamine that required for the production of many metal-binding proteins were specifically

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increased in LLZ. LLZ had higher capability of amino acid biosynthesis and metal cation

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transportation. The current research extends our understanding on the physiological mechanisms

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of Zn uploading into grain, and provides references for further Zn biofortification breeding in rice.

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Keywords：：Zinc (Zn); Grain, Amino acid; Nicotianamine; Rice; Biofortification

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Introduction

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Zinc is an essential micronutrient for human health, which plays critical roles in diverse

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biochemical processes, including catalyzing enzymes, facilitating protein folding and regulating

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gene expression1. The Zn deficiency-induced malnutrition constitutes a major public health

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problem in the world, affecting more than 25% of the world’s population2. The serious Zn

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deficiency is a leading risk factor for disability and death, especially for infants, children and

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pregnant women in developing countries where the cereal-based foods with low concentrations of

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Zn represent the main diet component3. Thus, breeding new crop cultivars with high grain-Zn

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concentrations will be an important strategy to alleviate human Zn deficiency.

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Rice is one of the most important cereals worldwide, providing energy and micronutrients for

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at least 3 billion people. Unfortunately, rice grain is especially low in Zn content. Among 939

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rice cultivars analyzed4, Zn concentration in brown rice ranged from 15.9 to 58.4 mg/kg with a

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mean of 25.4 mg/kg. Due to the narrow range of Zn level in existing germplasm collections, rice

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seems to be more difficult to make substantial increases in grain Zn content compared to maize

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and wheat5. Owing to this disadvantageous effect, various efforts have been made to exploit novel

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Zn-dense germplasm in rice. Jeng et al.6 developed three high Zn-density mutant lines from the

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rice cultivar IR64, which accumulated considerably higher levels of Zn in their grains (26.58,

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28.95 and 26.16 mg kg-1, respectively) than the wild type (16.00 mg kg-1). Some transgenic rice

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lines, which were generated by over-expression of OsNAS genes, could also increase grain Zn

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concentration7-12. However, none of the above studies has effectively increased the level of Zn

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content in rice grain to meet the nutritional demand.

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Due to alkaline pH and high orthophosphate concentration in the phloem, Zn is thought to be

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transported as complexes with ligands via the phloem while not as free ions13-15. In recent years,

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advances in high-throughput metabolomics analysis have assisted in studying the multiple

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physiological processes of Zn homeostasis. A variety of Zn-binding ligands have been identified in

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plants,

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metallothioneins16-24. Although a lot of metabolites have been identified and they play diverse

including

organic

acids,

amino

acids,

phytochelatins,

nicotianamine

and

3



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roles in Zn transport, a broad understanding of the metabolic changes in Zn uploading into

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developing rice grain remains unclear.

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In this study, a rice mutant, designated as LLZ, which accumulated a large amount of Zn in

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grains were characterized. We compared the accumulation and distribution of Zn between plant

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tissues under three Zn conditions in the mutant and wild-type, also we analyzed the metabolic

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changes in the developing grains of both varieties responding to different Zn treatment. The

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primary objective of this work is to provide novel metabolic evidence regarding Zn uploading into

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grain and to assist in elucidating the types of mutation resulting in the enhancement of Zn. The

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results revealed may lay a foundation for elucidation of the molecular mechanisms associated with

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Zn transport in rice and digging out genes associated to Zn accumulation in the future and further

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to develop rice varieties with high Zn concentrations in grain.

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Materials and Methods

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Plant materials and growth conditions

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The rice mutant, Lilizhi (LLZ), was derived from Oryza sativa ssp. Japonica variety

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Dongbeixiang (DBX) with irradiated by 250 Gy gamma rays (M11 generation). The seeds of both

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LLZ and WT were surface-sterilized by 3% H2O2 for 20 min and rinsed thoroughly in deionized

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water, then soaked in deionized water for 48 hours at 30°C. The seeds were transferred to the

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germination boxes in a growth chamber under photo flux density of 400 µmol m-2 s-1, light/dark

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period of 16/8 h, day/night temperature of 30/25 °C, and relative humidity of 75-85%. After

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supplied with only deionized water for the first two weeks, the seedlings were transplanted into

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48-well plastic buckets (35 L) and concentration of nutrient solution was increased gradually, from

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quarter of the final concentration for the first three days to half concentration for the next four

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days, then to the final concentration from the second week. The composition of nutrient solution

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was the same as that used by Wu et al. 25 except the Zn concentration varied: 1.5 mmol/L NH4NO3,

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1.0 mmol/L CaCl2, 1.6 mmol/L MgSO4, 1.0 mmol/L, K2SO4, 0.3 mmol/L KH2PO4, 0.05 µmol/L

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H3BO3, 5.0 µmol/L MnSO4, 0.2 µmol/L CuSO4, 0.5 µmol/LZnSO4, 0.05 µmol/L (NH4)6Mo7O24

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and 20 µmol/L Na2FeEDTA. The pH of the hydroponic solution was adjusted to 6.0 using 1 M

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NaOH or HCl. Hydroponics experiments were carried out in a greenhouse facility of Zhejiang 4


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University with natural light and temperature.

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Treatments and sampling

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The plants were exposed to three levels of Zn concentration (0.5, 5 and 50 µmol/L). Each

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treatment was triplet. Whole plant was harvested after seed maturation and separated into grain,

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glume, rachis, flag leaf, lower stems and leaves, and roots. Lower stems and leaves were collected

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from the basal leaves and stem internodes in rice. All samples were washed with deionized water

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to remove superficial nutrient solution. Roots and flag leaves were submerged in a 1.0 L bath

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containing 1.0 mM LaCl3 and 0.05 mM CaCl2 for 10 min in order to remove apoplastic Zn. Then

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all tissues were first dried at 105 °C for 30 min and finally dried to constant mass at 70 °C. The

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dry weight of all tissues was recorded. Dry materials of plant tissues were grounded in the agate

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mortar, passed through a 60 mesh and stored for elemental analysis. For metabolite profiling

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analysis, the grain-tissues of LLZ and WT were sampled for 0.5 and 50µmol/L Zn treatment at 15

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days after flowering (DAF) according to Wu et al. 25 that Zn concentration of rice grains reached

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the highest level at the stage. Six biological replicates were performed for each sample. All

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samples were fresh-frozen in liquid nitrogen and stored at -80 °C till metabolite extraction.

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Elemental analysis

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The Zn contents of plant tissues were measured as described by Sasaki et al.26. Samples of

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100±0.01 mg dried flour were hydrolyzed with concentrated HNO3 (60%) at 140 °C. The Zn

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concentration in the digest solution was determined by inductively coupled plasma-mass

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spectrometry (ICP-MS 7700X; Agilent Technologies). The key parameters for ICP-MS were

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plasma RF-power 1500 W, carrier gas flow rate 0.9 L/min, makeup gas flow rate 0.17 L/min,

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sample depth 8 mm, nebulizer pump seed 0.1 rps and spray chamber temperature 2°C. Each

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sample was triplet and Student’s t-test was conducted with SPSS 20.0 (IMB Corp. Chicago).

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Metabolite extraction and metabolite profiling analysis

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Metabolites were extracted and derivatized according to Lisec et al.

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. Briefly, frozen

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grain-tissues samples (50 mg) were homogenized in 700 µL of 100% methanol containing 30 µL

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of adonitol (2 mg/mL in water) as internal standard. After centrifugation at 11000 g for 15 min, the

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supernatants were transferred to a fresh 2 mL glass vial. Afterwards, 375 µL of chloroform and 5



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750 µL H2O were added to the samples. After centrifugation at 2200 g for 15 min, 150 µL of upper

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phases were taken and dried in a vacuum concentrator without heating. Then, 40 µL of

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methoxyaminhydrochloride (20 mg/mL in pyridine) was added to the dried samples and shaken

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for 2 h at 37°C. Afterwards, 70 µL of the MSTFA mix (1 ml＋20 µL FAMEs) was added to the

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sample and incubated for 30 min at 37°C. The derivatized samples were then analyzed by a gas

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chromatograph system coupled with a time-of-flight mass spectrometer (Pegasus HT GC-TOF-MS;

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LECO). The program of temperature-rise was followed by initial temperature 80 °C for 1 min,

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10 °C min−1 rate up to 295 °C, then 295 °C for 6 min. The mass spectrometry data were acquired

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in full-scan mode with the m/z range of 33-600 at a rate of 20 spectra per second after a solvent

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delay of 300 s. The mixed aliquots of all prepared sample extracts were included into the

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measurement for quality control. The raw peaks exacting, the data baselines filtering, peak

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alignment and deconvolution analysis were carried out by Chroma TOF software of LECO

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Corporation. Peaks were identified and annotated by comparison with retention time indices (RIs)

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and mass spectra from the LECO/Fiehn metabolomics library databases. The total mass of signal

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integration area was normalized on the basis of the fresh weight of the sample and the added

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amount of an internal standard (adonitol).

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Statistical analysis and metabolic pathway construction

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The normalized data involving the peak number, sample name, and normalized peak areas

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was fed to SIMCA14 software package for multivariate statistical analyses comprising of principal

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component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis

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(OPLS-DA). Data was log transformed prior to PCA and OPLS-DA. Differences in the metabolite

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levels were detected using the Limma package in R software platform

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metabolite showed a significant change (p≤0.05) at a threshold of VIP values greater than 1.0, it

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was defined as the specific metabolite. Benjamini and Hochberg correction method was also used

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to account for multiple testing29. The false discovery rate (FDR) for each p-value threshold was

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estimated by calculating q-values, based on the P-values derived from Limma analysis. Metabolic

28

. Once the level of a

6


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pathway was constructed according to KEGG metabolic database (http://www.genome.jp/kegg/).

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Results

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Plant growth and Zn accumulation

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The mutant LLZ exhibited a high Zn density in grain, and the Zn concentration in brown rice

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of LLZ reached to 52.7 mg/kg in the fields, but only 24.4 mg/kg in WT (Table S1). For comparison

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of Zn uptake and accumulation in the two genotypes, the mutant LLZ and WT were grown in

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nutrient solution. When the plants exposed to three Zn concentrations (0.5, 5 and 50 µM), both of

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them grew healthy and show no symptom of Zn toxicity. Biomass productions of both LLZ and

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WT were not significantly affected by enhanced Zn in solution (Table 1). While the biomass of

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shoot and whole-plant of WT was larger than that of the mutant in each hydroponic experiment,

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but there was no significant difference in root biomass between two varieties (Table 1).

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With elevated Zn supply, the Zn concentrations increased whether in whole plant or different

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tissues in both LLZ and WT (Table 1 and Fig 1). Although there was no difference in Zn

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concentration of whole-plant between LLZ and WT under all three treatments (Table 1), the Zn

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concentrations in different plant tissues between LLZ and WT varied greatly (Fig 1). Consistent

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with the previous field data, the average Zn levels of brown rice in LLZ were 1.63-fold, 1.65-fold

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and 1.35-fold, higher than those in WT under 0.5, 5 and 50 µM Zn conditions respectively (Fig 1).

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In companied with the Zn accumulation in grains, the average Zn concentration in glume of LLZ

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was 31.1% higher than that in WT under three Zn conditions (Fig 1). However, the LLZ and WT

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did not show significant difference in the Zn accumulation of flag leaf and rachis; Furthermore,

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the mutant LLZ accumulated 34.0% and 34.8% lower Zn concentrations in root and lower stem

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respectively than those of WT on average of the three Zn conditions (Fig 1). The Zn

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concentrations of lower leaves between LLZ and WT differed significantly when exposed to 0.5

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and 5 µM Zn treatments, whereas it showed a similar accumulation pattern in both varieties under

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50 µM Zn condition (Fig 1). Thus, the mutant displayed lower concentrations of Zn in roots and

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higher concentrations of Zn in shoots, the shoot to root ratio of Zn concentration (S/R) in LLZ was

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at least 1.2-fold greater than that in WT under each Zn treatment (Table 1). 7



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Comparison of metabolite levels between LLZ and WT

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To reveal physiological mechanisms of Zn uploading into rice seed, the metabolite profiles in

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the grains of LLZ and WT subjected to 0.5 and 50 µM Zn treatments were investigated. With the

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grains at 15 DAF, 136 metabolites were identified according to the reporting guidelines by Fernie

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et al.30 among a total of 695 intensive signals (Table S2). These metabolites included amino acids,

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organic acids, carbohydrates, lipids and other secondary metabolites. The raw GC-TOF-MS

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metabolomics data were available at the MetaboLights Web site (accession MTBLS448).

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Principal component analysis (PCA) revealed obvious metabolic differentiation between

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samples within treatments and genotypes (Fig. 2). The first two principle components can explain

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46.8% of the total variance. The two genotypes can be separated by the PC1 which represented

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28.1% of the total detected metabolite variance and the samples with different Zn treatment was

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clustered along PC2. Among the corresponding loadings, nicotianamine was found to be one of

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the major contributors for the separation along PC1, organic acids and amino acids, such as citric

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acid, glutamine and cystine were the dominate metabolites contributors to PC2 (Table S3).

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To determine the specific different metabolomes between varieties, limma package was

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further used to dissect the levels of metabolites between the grains of two varieties under 0.5 and

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50 µM Zn concentrations respectively. The results indicated that there were 29 and 62 metabolites

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with a significant change between LLZ and WT with the 0.5 µM and 50 µM Zn treatment,

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respectively (Table S4 and Table S5). And 24 differential metabolites were consistently present in

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two treatments (Table 2), among which 17 metabolites with higher contents in LLZ and 7

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metabolites with lower contents in LLZ when compared to WT (Table 2). The consistently

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accumulated metabolites in LLZ were majorly amino acids and organic acids, including glycine,

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glutamic acid, asparagine, tryptophan, glutamine, glutaconic acid and pimelic acid, the contents of

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nicotianamine in LLZ with 0.5 and 50 µM Zn treatment were respectively 1.3-fold and 1.9-fold

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higher than those in the WT (Table 2), whereas the amounts of 3 carbohydrates (gentiobiose,

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allose and glucose-6-phosphate) in the mutant were significantly decreased than that in the WT

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(Table 2).

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Metabolic changes of LLZ and WT in response to high Zn treatment 8


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To get a comprehensive view for metabolites changes in response to high Zn treatment, we

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compared the metabolomes of samples with the treatment of 50 µM Zn concentrations to those

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treated with 0.5 µM Zn. The analysis revealed that the influence of high Zn treatment on

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metabolism was highly dependent on genotypes. There were 52 and 42 metabolites with a

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significant change in mutant LLZ and WT, respectively. Among which, 27 metabolites showed

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identical responses to high Zn treatment in both two genotypes (Fig 3).Among these identical

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metabolites, 5 amino acids (proline, glycine, asparagine, isoleucine and glutamic acid) 8 organic

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acids such as threonic acid, pimelic acid, glyceric acid, α-ketoglutaric acid, citric acid, aconitic

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acid, malic acid and fumaric acid, and 1 carbohydrates (2-deoxy-D-glucose) showed significant

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increment, and 3 amino acids (glutamine, tryptophan and cystine), 3 organic acids (glyceric

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acid-3-phosphate, pyruvic acid and succinic acid), and 4 carbohydrates (fructose-6-phosphate,

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glucose-6-phosphate, fructose-2,6-bisphosphate and glucose-1-phosphate) were significantly

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down-accumulated in two genotypes(Fig 3).

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Compared with WT, there were 25 metabolites showing variety-specific responses to high Zn

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treatment in LLZ (Fig 3). Organic acid and amino acid metabolism appeared to be affected by high

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Zn treatment in LLZ, resulting in significantly accumulation of 7 amino acids and 7 organic acids,

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including valine, alanine, tyrosine, tartronic acid and glycolic acid, and down-regulating of

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β-glutamic acid, 3-hydroxypropionic acid and 2,2-dimethylsuccinic acid. In addition, the level of

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allose was significantly increased by 1.1-fold in response to high Zn treatment in LLZ (Fig 3). The

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significant increase of ethanolamine content was also observed in high Zn treated LLZ, indicating

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that amine biosynthesis was dramatically enhanced (Fig 3). It is noteworthy that nicotianamine

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and 5-methylthioadenosine were also dramatically enhanced in LLZ, which were biosynthesized

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from S-adenosyl methionine (Fig 3).

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Meantime, the contents of 2-ketobutyric acid and cysteine exhibited different responses to

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high Zn treatment between LLZ and WT, which were significantly down-regulated in WT but

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up-accumulated in the mutant LLZ (Fig 3). Xylose, which was one of the important carbohydrates

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in plant, increased by 0.5-fold in the WT but decreased by 0.6-fold in the mutant (Fig 3).

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Discussion 9



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Tissue-specific accumulation of Zn in LLZ

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In this study, we characterized a novel Zn-dense mutant LLZ, which showed high efficiency

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of Zn enrichment in grain. Impa et al. 31 proposed that continued root uptake can fully account for

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seed Zn allocation under Zn-sufficient conditions. Zn concentration in the whole-plant reflected

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the absorptive capacity of root uptake32. However, the whole-plant Zn concentrations between the

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mutant LLZ and WT did not differ significantly at any Zn levels (Fig 1), indicating they have

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similar absorptive capacity of root uptake.

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Wu et al. 25 found that large amounts of the Zn in rice seeds was accumulated through

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remobilization from sources tissues rather than continued root uptake during grain filling. Zinc in

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the grains may be actively supplied via the phloem after mobilization from the blades of the flag

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and upper leaves33. The remobilization of Zn from vegetable tissues to grain was major source of

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grain Zn loading under Zn-deficient conditions31. The mutant LLZ accumulated less Zn in root and

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the lower stem while more Zn in upper shoot tissues, especially in glumes and grains, presented

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different root-to-shoot partitioning of Zn from WT (Fig 1), characterized as a higher ratio of

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shoot/root Zn concentration (Table 1). If the great accumulation of Zn in LLZ grain was majorly

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transported from the upper leaves as previous reported34, 35, the Zn concentration in the upper leaf

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tissues of the LLZ would be lower than that in WT. However, there was no significant different in

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the Zn accumulation of flag leaf and rachis between LLZ and WT (Fig 1). That revealed the

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mutant LLZ transported more Zn to upper shoot tissues from root and had high efficiency in

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root-to-shoot Zn transporting which may mainly contribute to the higher Zn accumulation of grain

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in LLZ.

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Metabolic changes induced by high Zn treatment

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Metabolites can reflect the responses of plant to certain environment. Excessive Zn stress

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caused similar changes of metabolic pathways in LLZ and WT. The glycolysis appeared to be

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inhibited

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glucose-6-phosphate, 3-phosphoglycerate and pyruvic acid in both varieties (Fig 3). What’s more,

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the TCA cycle, associated with glycolysis pathway, was also be affected under high Zn condition

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in both varieties, shown as higher contents of citric acid, aconitic acid, fumaric acid and malic acid

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(Fig 3). That suggested Zn might affect the energy production system of rice. Some organic acids

by

Zn

stress,

exhibited

as

decreased

contents

of

fructose-6-phosphate,

10


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involved in TCA cycle, like citric acid and malic acid, were considered as chelator and associated

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with the transportation of zinc/cadmium in Thlaspi caerulescens, Arabidopsis halleri, and Triticum

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aestivuml 21, 22, 36. Citric acid and malic acid increased significantly in two varieties under high Zn

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treatment (Fig 3), being favorable for binding partners of zinc ion into xylem and facilitating the

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long-distance transport of Zn.

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Synthesis of some specific amino acids was significantly enhanced when plant were exposed

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to toxic metals. Amino acids, including proline, isoleucine, glutamine, glycine, and asparagine,

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were significantly increased in two varieties with high Zn exposure (Fig 3). Proline has been

280

shown to increase rapidly in response to heavy metal exposure16,

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previously considered as one of candidate ligands for heavy metals during xylem transport40, 41.

282

That the large amounts of proline and asparagine accumulated in response to high Zn treatment

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was agreement with previous observations (Fig 3). However, the contents of some amino acids,

284

such as serine, threonine, methionine and aspartic acid, were not significantly affected in both

285

varieties by high Zn stress (Fig 3). That might because the buffering effects through the

286

modulation of down-stream amino acids.

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Varieties dependent metabolic changes induced by high Zn treatment

37-39

; while asparagine was

288

The differential responses of some metabolic biomarkers to high Zn treatment between LLZ

289

and WT indicated that the influences of Zn on metabolites varied between genotypes. LLZ

290

exhibited more active amino acid biosynthesis in response to high Zn stress. Amino acids were the

291

precursors and constituents of proteins. The specific accumulated amino acids, like cysteine,

292

allothreonine, alanine, tyrosine, valine, homoserine and β-alanine in LLZ (Fig 3), were necessary

293

for the production of many metal-binding proteins which participated in the transportation of

294

metal cations, including phytochelatins and metallothioneins18, 42-47. Nicotianamine was thought to

295

be another key ligand involved in long- and short-distance transport of metal cations19. The

296

dramatic enhancement of nicotianamine in grains of LLZ irrespective of Zn treatments might

297

increase the mobility of Zn as the chemical forms of Zn in phloem (Table 2 and Fig 3). Genetic

298

regulation of nicotianamine synthases (NAS), which is responsible for biosynthesis of

299

nicotianamine, could enhance Zn concentration greatly. The Zn concentrations in seed of

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transgenic tobacco lines constitutively expressing a barley NAS gene increased 1.8-fold48. For rice, 11



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constitutive over-expression of genes encoding nicotianamine synthase also led to elevated

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nicotianamine levels, greater total content of Zn in grain and better plant tolerance to Zn

303

deficiency7-10. Furthermore, the endosperm of grains from transgenic rice over-expressing NAS

304

genes contained more bio-available Zn9. In reverse, suppression of NAS genes in Arabidopsis

305

halleri rendered a concomitant decrease in root-to-shoot translocation of Zn49, 50. Therefore, the

306

significant higher content of nicotianamine and amino acids in LLZ may contribute to the higher

307

Zn accumulation in LLZ.

308

Enriching Zn in grain is an important goal of micronutrient biofortification in rice. The

309

transportations of zinc from root to shoot and further to grain are the results of interplays among

310

not only different membrane transporters but also the mobility of zinc binding to ligands within

311

intercellular and organs. LLZ accumulated significant higher concentration of Zn in grains due to

312

improving root-to-shoot transport of Zn. Based the different metabolic profiles between the LLZ

313

and WT under normal and high Zn conditions, we proposed the enhancement of metal chelators

314

such as nicotianamine in LLZ was favor in accumulating Zn in grains. The characterization of a

315

novel high Zn accumulation rice mutant LLZ would help for further exploring the physiological

316

mechanisms of Zn uploading into grain, dig out the related genes combined other omics data, and

317

then provide molecular basis for Zn biofortification breeding in rice.

318 319

Acknowledgements

320

The authors gratefully acknowledge the financial support for this work from Zhejiang

321

Provincial Department of Science and Technology (2016YDF101801, 0406) and HarvestPlus

322

Program (2014H8327.ZHU).

323 324 325

Supporting Information Available: Additional Tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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708-723.

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Page 20 of 26

Table 1 Growth and Zn accumulation in LLZ and WT under 0.5, 5 and 50 µM Zn conditions. 5µM

0.5µM LLZ Dry weight (g/plant)

Zn concentration (µg/g)

Whole plant Shoot Root Whole plant Shoot Root S/R

WT

LLZ

5µM WT

LLZ

WT

3.08±0.63**

4.23±0.26

2.89±0.87*

4.23±0.26

2.94±0.36**

4.09±0.39

2.50±0.57** 0.58±0.10

3.72±0.24 0.51±0.04

2.40±0.61** 0.49±0.12

3.72±0.24 0.51±0.04

2.44±0.32** 0.50±0.06

3.54±0.29 0.55±0.08

58.38±2.59

61.15±3.78

84.81±2.59

61.15±3.78

240.86±18.33

248.64±14.05

61.28±1.74 54.37±2.07** 1.13

59.22±2.94 69.81±1.06 0.85

85.77±2.21** 77.3±1.08** 1.11

59.22±2.94 69.81±1.06 0.85

232.86±9.20 320.95±14.40** 0.73

229.06±6.37 442.96±7.63 0.52

466

* and ** indicated there were significant differences between LLZ and WT under the same Zn condition at 0.05 and 0.01 level respectively. The values given are means±SE

467

(n=6).

468 469

20


Page 21 of 26

470


Table 2 Significant and consistent different metabolites between LLZ and WT under 0.5 and 50 µM Zn condition.

471

0.5 µM

50 µM

Name WT

LLZ

VIP

P-value

Q-value

Log2(LLZ/WT)

WT

LLZ

VIP

P-value

Q-value

Log2(LLZ/WT)

Amides

malonamide

0.004373

0.006878

1.452720

0.007570

0.037835

0.653303

0.003789

0.006815

1.515390

0.000005

0.000042

0.846681

Amines

putrescine

0.318642

0.195162

1.426440

0.009328

0.044069

-0.707266

0.390817

0.230128

1.413020

0.000291

0.000766

-0.764060

Amino acids

asparagine

0.495972

0.893163

1.638140

0.000448

0.004588

0.848665

0.664333

1.612813

1.515320

0.000005

0.000042

1.279601

citrulline

0.016312

0.040486

1.715440

0.000043

0.000728

1.311509

0.019727

0.053422

1.405370

0.000353

0.000884

1.437237

glutamic acid

0.059774

0.084402

1.652220

0.000322

0.003676

0.497775

0.075415

0.139960

1.431950

0.000189

0.000622

0.892101

glutamine

0.335262

0.566383

1.570330

0.001693

0.013640

0.756490

0.167295

0.262897

1.118920

0.020567

0.021236

0.652109

glycine

0.208921

0.424430

1.717470

0.000043

0.000728

1.022565

0.310111

0.706367

1.522220

0.000004

0.000042

1.187634

lysine

0.068137

0.141847

1.633770

0.000469

0.004588

1.057831

0.079481

0.135788

1.124250

0.020116

0.021236

0.772665

N-acetyl-L-leucine

0.149754

0.057122

1.744570

0.000016

0.000545

-1.390487

0.124004

0.068730

1.293260

0.003427

0.006127

-0.851382

ornithine

0.017881

0.058502

1.777600

0.000001

0.000102

1.710030

0.028683

0.063331

1.535400

0.000001

0.000030

1.142738

tryptophan

0.018536

0.048955

1.606420

0.000876

0.007998

1.401125

0.009145

0.028123

1.448430

0.000113

0.000425

1.620631

0.000411

0.002321

1.784880

0.000001

0.000102

2.496635

0.000596

0.002957

1.516660

0.000005

0.000042

2.309910

allose

0.006561

0.002036

1.726670

0.000033

0.000728

-1.688114

0.006672

0.004416

1.214080

0.008488

0.010901

-0.595473

gentiobiose

0.001811

0.000471

1.752060

0.000016

0.000545

-1.943553

0.001524

0.000478

1.490380

0.000036

0.000189

-1.672321

glucose-6-phosphate

0.438056

0.284239

1.516020

0.003669

0.022964

-0.624011

0.288442

0.210643

1.264330

0.004901

0.007821

-0.453482

sorbitol

0.014946

0.028098

1.515660

0.003669

0.022964

0.910694

0.015113

0.029493

1.335300

0.001693

0.003428

0.964588

Imines

nicotianamine

0.001263

0.003169

1.677260

0.000176

0.002195

1.327817

0.001448

0.005399

1.495980

0.000016

0.000100

1.898905

Lipids

1-monopalmitin

0.000743

0.001280

1.545200

0.003688

0.022964

0.784933

0.001040

0.001513

1.207910

0.012399

0.015184

0.541373

Nucleosides

flavin adenine dinucleotide

0.153576

0.332123

1.558430

0.002017

0.015351

1.112766

0.131737

0.220004

1.026060

0.040507

0.034968

0.739863

Organic acids

2,4-diaminobutyric acid

0.014540

0.039276

1.727370

0.000033

0.000728

1.433589

0.023460

0.046971

1.378910

0.000715

0.001711

1.001562

3-hydroxybutyric acid

0.001747

0.000065

1.497250

0.004493

0.025647

-4.747391

0.001905

0.001493

1.129870

0.011533

0.014460

-0.351935

glutaconic acid

0.000242

0.000465

1.689920

0.005303

0.029062

0.942371

0.000285

0.000898

1.537720

0.000012

0.000087

1.657844

pimelic acid

0.006566

0.012152

1.677470

0.000176

0.002195

0.888071

0.008016

0.021560

1.559120

0.000000

0.000000

1.427481

succinic acid

0.336809

0.217613

1.673370

0.000176

0.002195

-0.630168

0.250803

0.142045

1.418110

0.000260

0.000719

-0.820208

Carbohydrates 2-deoxy-D-glucose

21



Page 22 of 26

472

Figure captions

473

Fig 1. The Zn accumulation of different plant tissues under 0.5, 5 and 50 µM Zn conditions. The results are presented as means±SE. The values

474

followed by asterisks are statistically different from the wild type according to a Student’s t-test (n = 6; *P < 0.05; **P < 0.01).

475

Fig 2. Principal component analysis (PCA) of metabolic profiles in grains of LLZ and WT under 0.5 and 50 µM Zn conditions (six biological

476

replicates). Circle and triangle respectively denote metabolomes of LLZ and WT under 0.5µM Zn condition, while inverted triangle and box correspond

477

to LLZ and WT under 50 µM Zn conditions.

478

Fig 3. Metabolic changes of 15 DFA grains in LLZ and WT after 50 µM Zn treatment and the pathway they participated. Box and circle respectively

479

denote significantly changed metabolites of WT and LLZ (VIP>1, P

Metabolite Profiling of a Zinc-Accumulating Rice ... - ACS Publications

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