Zinc oxide nanoparticle exposure triggers different gene expression patterns in maize shoots and roots

Environmental Pollution 229 (2017) 479e488

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Zinc oxide nanoparticle exposure triggers different gene expression patterns in maize shoots and roots* Hongwei Xun 1, Xintong Ma 1, Jing Chen, Zhongzhou Yang, Bao Liu, Xiang Gao, Guo Li, Jiamiao Yu, Li Wang**, Jinsong Pang* Key Laboratory of Molecular Epigenetics of MOE, School of Life Sciences, Northeast Normal University, Changchun 130024, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 December 2016 Received in revised form 17 May 2017 Accepted 24 May 2017

The potential impacts of environmentally accumulated zinc oxide nanoparticles (nZnOs) on plant growth have not been well studied. A transcriptome profile analysis of maize exposed to nZnOs showed that the genes in the shoots and roots responded differently. Although the number of differentially expressed genes (DEGs) in the roots was greater than that in the shoots, the number of up- or down-regulated genes in both the shoots and roots was similar. The enrichment of gene ontology (GO) terms was also significantly different in the shoots and roots. The “nitrogen compound metabolism” and “cellular component” terms were specifically and highly up-regulated in the nZnO-exposed roots, whereas the categories “cellular metabolic process”, “primary metabolic process” and “secondary metabolic process” were down-regulated in the exposed roots only. Our results revealed the DEG response patterns in maize shoots and roots after nZnO exposure. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Maize seedling Zinc oxide nanoparticle Transcriptome Differentially expressed genes Gene ontology annotation

1. Introduction Nanoparticles (NPs) are engineered structures with a diameter of less than 100 nanometers (nm) (Mukhopadhyay, 2014), and they can be manufactured via inorganic synthesis or by exploiting living organisms (Prasad et al., 2016). Because of their unique physicochemical properties, NPs have been widely used for industry and agriculture, which has led to an increasing number of products and processes (Ma et al., 2010). There are many applications for NPs in different regions, including NPs used for industrial (Aziz et al., 2015; Schmid et al., 2010), human health (Aziz et al., 2016; Ito et al., 2005), agricultural (Mishra et al., 2014; Sekhon, 2014), and food science processes (Saxena, 2009). However, the unique properties of NPs leads to interactions with biological systems via chemical, catalytic, mechanical and surface effects (Klaine et al., 2008). Released NPs may then cycle through different environmental compartments and eventually contaminate living organisms (Phillips et al., 2012). Nano-sized materials have been shown

*

This paper has been recommended for acceptance by Baoshan Xing. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (L. Wang), [email protected] (J. Pang). 1 These authors contributed equally to the work. http://dx.doi.org/10.1016/j.envpol.2017.05.066 0269-7491/© 2017 Elsevier Ltd. All rights reserved.

to enter living organisms and spread to organs and tissues (Van Aken, 2015), and small-sized TiO2 NPs have been shown to penetrate through the plant cell wall and have been observed in cortical cells (Du et al., 2011). The interactions between Fe3O4 NPs and plants revealed that iron oxide particles could be taken up by Pisum sativum L. and Lepidium sativum L. (Bystrzejewska-Piotrowska et al., 2012). However, the safety issues related to engineered NP usage € rster, 2010). are a subject of increasing concern (Oberdo Both positive and negative effects of NPs on plants have been reported, and the key factors governing these effects include the nanomaterial type, particle size, specific surface area, and the plant species in question (Tripathi et al., 2017a; Zhu et al., 2012). Moreover, an array of NPs, such as carbon, silver, zinc oxide, copper oxide, aluminum oxide, fullerene, silicon dioxide, titanium oxide and cerium oxide, have been studied in different crops (Atha et al., 2012; De La Torre-Roche et al., 2012; De La Torre-Roche et al., 2013; Zhu et al., 2012). Carbon nanotubes have been shown to facilitate water uptake and penetrate the seed coat, thereby accelerating tomato germination (Khodakovskaya et al., 2009), whereas TiO2 NPs have been shown to negatively affect water transport and transpiration in corn plants (Asli and Neumann, 2009). Metal-based NPs exhibit toxicity associated with the metal constituents and likely originate from reactive oxygen species (ROS) generation and the release of toxic metals, which impacts the cell membrane (Su et al., 2015). For example, ZnO NPs (nZnOs) have been found to

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significantly retard the root growth of all tested species (Lactuca sativa L., Zea mays L., Cucumis sativus L., Brassica napus L., Raphanus sativus L., and Lolium perenne L.), whereas none of the species were affected by multi-walled nanotubes (MWNTs) (Lin and Xing, 2007). Gene expression analyses represent a powerful approach for understanding the molecular mechanisms underlying the responses in plants exposed to nanomaterials. Many genes involved in stress response pathways have been found to be differentially expressed in response to nano-Ag (Kaveh et al., 2013), and the genes encoding metallothionein in wheat (Triticum aestivum L.) were up-regulated (Dimkpa et al., 2013). In a study of the effects of nano-sized zinc oxide (ZnO), fullerene soot (FS) or titanium dioxide NP exposure on gene expression, the genes responsive to both abiotic and biotic stimuli, cell organization and biogenesis were down-regulated in Arabidopsis thaliana (L.) Heynh (Landa et al., 2012). Bulk ZnO has been widely used as an additive for numerous materials and products. ZnO NP is among the most commonly used NPs in many applications, including personal care, pigments, ceramics, batteries, paints, foods, and semiconductors (Du et al., 2011). The increasing use of nZnOs has raised concerns about the synthesized material form. Additional nZnOs enter the environment with the wide industrial usage of this NP, and it then accumulates in soil. The inappropriate use of materials considered innocuous, such as asbestos, has been shown to be harmful to human health (Hillerdal and Henderson, 1997). Agricultural applications of nZnOs have also been proposed (Sabir et al., 2014), although the effect of these NPs on crop germination and development has not been adequately studied. The diverse effects of nZnOs on plants were previously documented (Landa et al., 2012), and nZnOs were found to damage both the cortical and root epidermal cells of L. perenne (ryegrass) and dramatically reduced the root growth (Lin and Xing, 2008). A study to determine the effect of nZnOs on Brassica nigra L. found that seed germination and seedling and explant growth were adversely affected and the antioxidant activities and non-enzymatic antioxidant contents were increased (Zafar et al., 2016). A recent study concluded that the concentration of released metal ions was not high enough to cause phytotoxic effects in plants (Yang et al., 2015). To further investigate the effects of nZnO exposure on the early growth of maize and the underlying molecular functions, we conducted comparative transcriptomic analyses on nZnO-exposed maize seedlings. We examined the impact of nZnOs on maize seedlings at the phenotypic and molecular levels. Shortened roots and differential gene expression patterns were observed in both the shoots and roots.

2. Materials and methods 2.1. Plant material and nanoparticle exposure Maize seeds of the inbred line B73 were washed with tap water for 5 min, surface sterilized with 75% ethanol for 2 min, and rinsed with sterile distilled water for 2 min, and each step was repeated 3 times. Five biological replicates were performed for both the nZnOexposure and mock groups, and 10 seeds were included in each replication. The sterilized seeds were kept on sterile filter paper in 90-mm Petri dish, which contained 5 ml of 500 mg L
1 nZnOs (Sigma-Aldrich, Cat. #677450, Saint Louis, MO, USA; assay >97%, particle size <50 nm (BET), surface area >10.8 m2/g) resuspended in double distilled water (ddH2O) (NP exposure group) or ddH2O only (mock group). The seeds were incubated at 25  C in the dark for 5 days for germination.

2.2. Morphological observations and trait measurements After 5 days of germination, the germination rates, lengths and fresh weights of the shoots and primary roots of individual plants were measured. Data processes were evaluated via a one-way ANOVA in Excel. The significance of the differences between groups was examined by a one-way ANOVA, with the significance threshold set at p < 0.05. 2.3. Sampling and total RNA isolation The shoots and primary roots of each nZnO-exposure group or mock group replicate were collected separately. Total RNA was extracted with TRIZOL® reagent (Life Technologies Corporation, Cat. #15596-026, Carlsbad, CA, USA) and treated with DNase I (New England Biolabs, Cat. #M0303S, Ipswich, MA, USA). Five micrograms of total RNA from each of the 2 replicates of the same organ (shoot or root)/treatment (nZnO exposure or mock) were pooled (only 4 out of 5 technical replicate samples in the same groups were used), for a total of 8 sequencing samples: 2 root nZnO samples (roots from seedlings grown in 500 mg L
1 nZnO), 2 root mock samples (roots from seedlings grown in ddH2O), 2 shoot nZnO samples (shoots from seedlings grown in 500 mg L
1 nZnO), and 2 shoot mock samples (shoots from seedlings grown in ddH2O). 2.4. RNA-sequencing and sequence data process The RNA samples were sequenced with an Illumina Genome Analyzer (HiSeq™ 2500, San Diego, CA, USA). The raw data were processed with the FastQC tool (http://www.bioinformatics.bbsrc. ac.uk/projects/fastqc/) to remove low-quality and contaminated sequences. The clean data were aligned to the B73 reference genome (http://www.phytozome.net/maize) with TopHat software (Trapnell et al., 2009). 2.5. Differential gene expression analysis and validation The cufflinks package (Trapnell et al., 2010) was used for the RPKM (Reads Per Kilobase of exon model per Million mapped reads) and differentially expressed gene (DEG) analyses with default parameters. Venn diagrams were generated to display the number of DEGs in the different organs and treatments using the web-based Venny tool (http://bioinfogp.cnb.csic.es/tools/venny/). The Pheatmap package (https://cran.r-project.org/ package¼pheatmap) was used to draw a heatmap of the organspecific DEGs to show changes in their expression pattern. To validate the DEGs and their expression levels identified via the RNA-Seq analysis, 3 DEGs were randomly selected from each of the following 3 change types: shoot specific, root specific and shared by both shoots and roots. The expression level changes were tested via quantitative real-time reverse transcription PCR (qRTPCR). A StepOnePlus® instrument (Applied Biosystem, Foster City, CA, USA) and Takara SYBR® Premix Ex Taq™ II reagent (Takara Biotechnology, Cat.# RR820A, Dalian, Liaoning, China) were used. The same batch of RNA samples (3 technical replicates for each 2 biological replicates of the same type of organ/treatment) for RNA sequencing were used as templates for reverse transcription, and the generated cDNAs were used as quantitative real-time PCR templates. Each 25 ml reaction consisted of 2 ml of the template, 12.5 ml of 2 SYBR Premix, 2.5 ml (25 nM) of each primer and 5.5 ml of ddH2O. The reactions were subjected to an initial denaturation step of 95  C for 5 min, followed by followed by 40 cycles of 95  C for 30 s, 55  C for 15 s and 72  C for 30 s. The amplification efficiency and specificity of each PCR reaction were verified by visualizing the melting curve and running the PCR product on 2% agarose gels. The

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Fig. 1. Morphology and plant measurements. A. Mock seedlings; B. nZnO-exposed seedlings; C. Shoot and root lengths of the mock and nZnO-exposed seedlings (Y axis unit: cm); D. Shoot and root weights of the mock and nZnO-exposed seedlings (Y axis unit: g). Notes: bars in Figure A and B: 1 cm ** significant differences in the root length between the mock and nZnO-exposed shoots in Fig. C.

Table 1 Summary of RNA-Seq data. Sequencing samples

Paired-end reads

Reads mapping rate

Clean clusters

Clean bases

Shoot mock Shoot nZnO Root mock Root nZnO

12484616 10900944 13416881 15023004

86.2% 85.1% 88.0% 86.5%

33,595,524 34,425,129 33,639,316 49,445,230

8,398,881,000 8,606,282,250 8,409,829,000 12,361,307,500

DEG fold change was correlated between the RNA-Seq and qRT-PCR results using the Excel t-test function (paired two-sample mean analysis).

2.6. Gene ontology annotation The gene ontology annotation (GOA) analyses of the DEGs between the root exposure and root mock groups and the shoot exposure and shoot mock groups were conducted via the AgriGO (Du et al., 2010) (http://bioinfo.cau.edu.cn/agriGO/) web-based Zea mays AGPv3.30 database and singular enrichment analysis (SEA) tool. The enrichment of terms in the different processes were further analyzed (p < 0.01, FDR < 0.05).

3. Results 3.1. nZnO induced maize seedling phenotypic changes The phenotypes of the nZnO-exposed and mock maize seedlings showed significant differences only in the root length (Fig. 1A and B). The primary root average lengths of the nZnO-exposed seedlings were significantly shorter than those of the mock treatments (Fig. 1C) (p < 0.01). The weight of the shoots and primary roots and the length of the shoots of the nZnO-exposed and mock seedling groups showed no significant differences (p > 0.05) (Fig. 1D). Although differences were observed in the germination rates of the nZnO-exposed and mock groups (at 92% and 100%, respectively), they were not significant (0.10 > p > 0.05).

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Fig. 2. Differentially expressed genes between different organs and treatments A. Number of DEGs between the different organs (shoot and root) and treatments (mock and nZnO exposed) and the number of DEGs shared in the different organs or treatments. R500-R-M: DEGs in the roots between the nZnO-exposed and mock seedlings; S-500-S-M: DEGs in the shoots between the nZnO-exposed and mock seedlings; S-M-R-M: DEGs between the mock shoots and roots; S-500-R-500: DEGs between the nZnO-exposed shoots and roots. B. Organ-specific nZnO-exposure-induced DEGs. The shoot- or root-specific DEGs were subtracted from the total DEGs, and only the number of organ-specific nZnO-induced DEGs is shown in this picture. R-UP: number of root-specific nZnO-exposure-induced up-regulated genes; S-UP: number of shoot-specific nZnO-exposure-induced up-regulated genes; RDOWN: number of root-specific nZnO-exposure-induced down-regulated genes; S-DOWN: number of shoot-specific nZnO-exposure-induced down-regulated genes. C. Changes in the gene expression patterns of eight nZnO-exposure-induced organ-specific DEGs. I. S-N R-U; II. S-D R-U; III. S-D R-N; IV. S-N R-D; V. S-D R-D; VI. S-U R-N; VII. S-U RD; VIII. S-U R-U.S-U: up-regulated in the shoot; S-N: not changed in the shoot; S-D: downregulated in the shoot; R-U: up-regulated in the root; R-N: up-regulated in the root; R-D: down-regulated in the root. * Tab indicates the direction and fold difference of the expression change.

3.2. RNA-seq data analysis and differentially expressed gene (DEG) identification The transcriptomes of the samples were obtained after processing and assembly. The quantity and quality of the sequencing data are shown in Table 1. The average length of a single read was 125 bp. The usable sequence data of each type of sample were unbiased, and enough reads were obtained to perform expression analyses. There were 26789 genes identified in the roots, with 26697 and 26717 genes expressed in the mock and nZnO-exposed root groups, respectively, and 26625 genes expressed in both samples. There were 26529 genes identified in the shoots, with 26464 and 26426 genes expressed in the mock and nZnO-exposed shoot groups, respectively, and 26361 genes expressed in both samples (a full list of DEGs is available upon request). A comparison of the gene expression in different organs for the same type of treatment (mock or nZnO) showed that 6148, 2384 and 4219 DEGs occurred in the mock shoot and nZnO-exposed shoot groups and both groups simultaneously, respectively, and these DEGs were organ specific between the shoots and roots. A comparison of the different treatments in the same organ showed

that 275, 168 and 41 DGEs occurred in the root, shoot and the root and shoot simultaneously, respectively. These 484 DEGs were indeed differentially expressed after the shoots and roots were exposed to nZnO (Fig. 2A). The number of nZnO-exposure-induced DEGs after subtracting the common DEGs that showed the same change direction in both the mock and nZnO-exposure groups is shown in Fig. 2B. The full list of nZnO-induced DEGs and their expression patterns is shown in Table S1. We observed 154 and 121 root-specific DEGs that were up- and down-regulated, respectively; 80 and 88 shoot-specific DEGs that were up- and down-regulated, respectively; and 19 and 15 DEGs shared between the roots and shoots that were upand down-regulated, respectively. However, 5 DEGs were upregulated in the shoot but down-regulated in the root and 2 DEGs were down-regulated in the shoot but up-regulated in the roots (the list of nZnO induced organ-specific DEGs and their expression change patterns can be found in Table S1). The organspecific nZnO-induced gene expression change patterns are shown in Fig. 2C, and the gene expression levels could be classified into 8 types: I. not changed in the shoots but up-regulated in the roots (S-N R-U); II. down-regulated in the shoots but up-regulated in the roots (S-D R-U); III. down-regulated in the shoots but not

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Table 2 Quantitative real-time RT-PCR validation of the RNA-Seq DEGs. Organ

Gene ID

Shoot

Shared Shoot Root Shoot Root Shoot Root Root

Primers

GRMZM5G891187 Fwd: CTGGCGTGATGATCGTTTAGT (58  C) Rev: CTTGTTCTTGGTTGCCAGATTG (60  C) GRMZM2G350793 Fwd: TACAGCTACGGCATTGTTCTC (56  C) Rev: CAGTGCTTCCTCGACGTATTT (57  C) GRMZM2G098167 Fwd: CTGGTGAAGCTGTGCATTTG (57  C) Rev: GACTGTTATTCCTGGGCATGTA (57  C) GRMZM2G126900 Fwd: GACGATGAGGACAAGGAGAAC (56  C) Rev: CGGGAAGTACTTGTCGATTAGG (58  C) GRMZM2G082959 Fwd: GATCTCATCCGCGACACTAAC (57  C) Rev: AGGTAGAAGGAGTGGCTCATG (56  C) GRMZM5G841343 Fwd: AGAACACCGGCAAGAAGTAGC (59  C) Rev: AGACAACGCCCACGAAATC (58  C) GRMZM2G302245 Fwd: CTGGTGGTCGTCATACATTAGC (57  C) Rev: GGGAGAAGAAACAGTTCGGATT (59  C) GRMZM2G145041 Fwd: GAGCTGGAGATGATACACAAGAA (57  C) Rev: GGAAAGAATGGAACGGAAGAA (58  C) GRMZM2G153536 Fwd: AAGGTTGAGGAACGCCTGG (60  C) Rev: CTCGCCCTGGTACGTGACTG (61  C)

Product size (bp)

Annotation

Average fold change (log2) RNASeq

qRTPCR

115

Walls Are Thin 1

1.20

1.72

105

Leucine-rich repeat protein kinase family protein


2.51


2.35

104

HSP20-like chaperones superfamily protein

1.99

3.46

135

Myo-inositol oxygenase 1

2.17 1.82

1.45 1.91

104

Carboxyesterase 17


1.74
3.40


4.05
2.92

117

Uncharacterized protein family SERF

3.16

3.31

113

Regulator of chromosome condensation (RCC1) family protein

3.07 2.15

2.92 1.04

105

Homeodomain-like superfamily protein

1.95

1.36

114

Branched-chain aminotransferase 3

1.25

2.25

changed in the roots (S-D R-N); IV. not changed in the shoots but down-regulated in the roots (S-N R-D); V. down-regulated in both shoots and roots (S-D R-D); VI. up-regulated in the shoots but not changed in the roots (S-U R-N); VII. up-regulated in the shoots but down-regulated in the roots (S-U R-D); and VIII. up-regulated in both shoots and roots (S-U R-U). The annotation of the DEGs is shown in Table S2. The expression levels of certain genes were significantly changed (larger than 4 fold) by nZnO exposure, and 49 upregulated and 26 down-regulated DEGs were found in the roots, whereas 9 up-regulated and 25 down-regulated DEGs were found in the shoots (Table S1). Validation of RNA-Seq was performed by running qRT-PCR with 9 randomly chosen organ-specific DEGs, the genes IDs and their function annotations were shown in Table 2. The qRT-PCR melting curves showed typical specific amplification results, and one unique band of the correct size was visualized for each PCR product (data not shown). This evidence supported the fidelity of the PCR data. The changes in expression detected via qRT-PCR and the original RNA-Seq data were shown in Table 2. The fold change differences were not significant (p > 0.05); thus, these results validated the transcriptome data. 3.3. Gene ontology annotation (GOA) of the nZnO-induced organspecific DEGs To investigate the organ-specific nZnO-exposure-induced DEGs, the biological functions of the DEGs were further explored using AgriGO. A total of 375 of 484 DEGs (Table S1) were annotated and enriched in 76 GO terms. Although the GO-annotated DEGs changes in different directions were similar in the shoots (73 and 74 were up- and down-regulated, respectively) and roots (118 and 110 were up- and down-regulated, respectively), the processes involved were different, with the up- and down-regulated DEGs in the shoots annotated in 7 and 14 terms, respectively, and the up- and

down-regulated DEGs in the roots annotated in 35 and 25 terms, respectively (Fig. 3; the shoot- and root-specific enriched GO terms are listed in Table S3). The GO annotation categories and their organ specificities are shown in Table 4. Most of the terms were either root or shoot specific. In the significantly enriched root-specific processes, all terms in the categories “nitrogen compound metabolism” (8), “nutrient reservoir” (1) and “small molecule metabolism” (4) were specifically up-regulated in the nZnO-exposed roots. In the former 2 categories, the change in the expression level of most DEGs was greater than 4 fold. The categories “cellular metabolic process,” “primary metabolic process” and “secondary metabolic process” were only down-regulated in the exposed roots. These changes indicated the root response to direct contact with nZnOs. Enriched shoot-specific categories were not identified in the GO analysis. In both the roots and shoots, all terms from the categories “ion binding” (8 roots and 3 shoots) and “cellular component” (2 roots and 1 shoot) were up-regulated, and the DEGs for most terms were significantly changed. In the “oxidation reduction/antioxidant activity” category, changes were observed in the roots, although only 2 terms (GO:0016684 and GO:0004601) were enriched, and different change directions were observed for different DEGs for the other 3 terms (GO:0055114, GO:0016491 and GO:0016705). A similar observation was found in the shoots, in which “unfolded protein binding” was up-regulated but “carbohydrate binding” was down-regulated. In certain categories, including “catalytic activity”, “transport”, “response to stimulus” and “cell part,” more complicated gene expression change patterns were found. Common or unique terms varied according to the up- and/or down-regulation of the DEGs in the roots and shoots. These changes indicated elaborated homeostasis regulation of the same processes in response to nZnO exposure. In a further GO enrichment analysis of the highly changed (larger than 4 fold) DEGs, 35 of 49 root-specific nZnO up-regulated DEGS and 19 of 26 root-specific down-regulated DEGs were

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Fig. 3. GO annotation of the biological processes of the organ-specific nZnO-exposure-induced differentially expressed genes. A. Root up-regulated process; B. Root down-regulated process; C. Shoot up-regulated process; D. Shoot down-regulated process.

annotated and categorized into to 17 and 6 terms, respectively (Table 3) (in Table 4, the change directions are marked in bold). These root-specific highly up-regulated DEGs were mainly enriched in the “nitrogen compound metabolic,” “ion binding” and “extracellular region” processes. Among the 6 highly down-regulated terms, GO:0050896 (response to stress; p-value ¼ 0.0023, FDR ¼ 0.042) was the only one enriched in both analyses, whereas the other 5 terms were only slightly enriched in transport processes (GO:0006810, GO:0022857 and GO:0005215), and they showed pvalues ranging 0.00051e0.005 and the FDR values ranging from 0.011 to 0.042. The 9 shoot-specific highly up-regulated DEGs were slightly enriched in GO:0006950 (p-value ¼ 0.0007, FDR ¼ 0.01); however, none of the 25 shoot-specific highly down-regulated DEGs were enriched in any GO category. 4. Discussion The effects of nZnOs, conjugated bulk ZnO, or even dissolved Zn

ions on the exposed plants have not been well studied, and studies have reported different results in different systems. A study comparing nZnOs and free Zn ions using a microbial live cell reporter assay system showed distinct gene expression patterns, suggesting that these two Zn forms caused toxic reactions via different pathways (Su et al., 2015). In an ecotoxicity study of the effects of nZnOs, bulk ZnO, and ZnCl2 on wheat, radish and vetch, the different types of ZnO showed similar adverse effects in the tested plants, which indicated that the ZnO ion dissolution was similar in these plants; nevertheless, ZnCl2 showed the highest mez et al., 2015). Lee et al. reported that the Zn toxicity (García-Go ions dissolved from nZnOs were limited and had minor effects compared with the Zn ions dissolved from ZnCl2 at the same concentration. Moreover, the nZnOs primarily accounted for the changes observed after ZnO exposure. Hence, we believe that the gene expression changes observed in the present study were primarily caused by the impact of nZnOs in maize seedlings. Our results revealed that the nZnO treatment significantly

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Table 3 DEGs and enriched GO terms in the shoot and root. Organ Change

Input Annotated Enriched GO terms (>2 fold) DEGs DEGs

Enriched GO terms (>4 fold)

Shoot

Up

104

73

GO:0006950.

Down

105

74

Up

175

118

Down

141

110

Root

19 Shared Shoot and Root Up 15 Shoot and Root Down

14

8

GO:0006950, GO:0005506, GO:0005618, GO:0030246, GO:0016021, GO:0050896, GO:0006519, GO:0043436, GO:0019752, GO:0030145, GO:0020037, GO:0046914, GO:0050896, GO:0016114, GO:0006720, GO:0046906, GO:0043167, GO:0044260, GO:0010467,

GO:0006979, GO:0050896, GO:0006457, GO:0051082, GO:0020037, GO:0046906, GO:0016705, GO:0030312, GO:0005737, GO:0005886, GO:0005576. GO:0004674, GO:0004672, GO:0022857, GO:0022891, GO:0031224. GO:0006950, GO:0006576, GO:0009308, GO:0044106, GO:0042221, GO:0010035, GO:0009309, GO:0034641, GO:0055114, GO:0006811, GO:0006979, GO:0006082, GO:0042180, GO:0044271, GO:0006575, GO:0030001, GO:0045735, GO:0016765, GO:0043169, GO:0046872, GO:0046906, GO:0016491, GO:0043167, GO:0005506, GO:0016684, GO:0004601, GO:0048046, GO:0005576. GO:0006950, GO:0042221, GO:0055114, GO:0019748, GO:0006721, GO:0009628, GO:0008299, GO:0019438, GO:0010033, GO:0006725, GO:0016491, GO:0020037, GO:0016705, GO:0005506, GO:0043169, GO:0003824, GO:0046872, GO:0005773, GO:0044437, GO:0005774. GO:0044238, GO:0044237, GO:0043170, GO:0009987, GO:0006950, GO:0050896, GO:0008152, GO:0044267.

e

e GO:0006576, GO:0034641, GO:0030145, GO:0046914, GO:0005576.

GO:0006575, GO:0044106, GO:0045735, GO:0043167,

GO:0032787, GO:0044271, GO:0009308, GO:0006519, GO:0016765, GO:0043169, GO:0046872, GO:0048046,

GO:0006810, GO:0050896, GO:0051234, GO:0051179, GO:0022857, GO:0005215.

e

e

*GO terms in italics indicate the newly discovered highly changed enriched terms that were not previously identified from the 2-fold changes in the analysis of the genes.

changed the expression levels of certain genes in the maize seedlings, and different response patterns were observed between the roots and shoots. Most of the DEGs were only found in one type of organ. More up-regulated than down-regulated genes were found in the exposed roots and seedlings at 260 and 231, and 175 and 141, respectively. These findings were inconsistent with that of previous reports in which the overall down-regulation of genes was considered a common reaction of plants against stresses from different types of nanomaterials (García-S anchez et al., 2015; Kaveh et al., 2013). A study of Arabidopsis plants exposed to nZnO revealed 660 and 826 up- and down-regulated genes, respectively (Landa et al., 2012), whereas only 484 DEGs were identified in this research. The lower number of observed DEGs and the absence of photosynthesis-related DEGs, which are common in NP exposure experiments, were justified because the germinating maize seedlings in our study were in an early growth stage and had not been exposed to light (Hong et al., 2005; Wang et al., 2015). The gene expression changes in response to nZnO exposure might have an early up-regulation period in response to acute stress as observed in the present study and phytotoxicity as demonstrated in many previous reports that performed long-term treatments. The most significantly changed genes in the shoots were GRMZM5G841343 (log2 ratio ¼ 3.16) and GRMZM2G000035 (log2 ratio ¼
3.60), whereas the most significantly changed genes in the roots were AC233955.1_FG003 (log2 ratio ¼ 4.25) and GRMZM2G361217 (log2 ratio ¼
9.57). AC233955.1_FG003 and 3 other highly up-regulated homologous genes were annotated as nicotianamine synthase 4 (NAS4). NAS can regulate ion transport and distribution (Inoue et al., 2003). In maize, ZmNAS genes respond to heavy metal ions (Ni, Fe, Cu, Mn, Zn, and Cd), suggesting that highly enhanced transport processes occur in nZnO-exposed maize roots. The gene GRMZM2G361217 is weakly similar to the DNA-binding family protein, its biological functions as well as many other shoot- or root-specific DEGs that showed greater than 4-fold changes were not clarified. The functions of the observed DEGs were highly diverse, and the DEGs in certain gene families were mostly specific to the different organs or treatments. The “peroxidase superfamily” was the most up-regulated gene family in the

shoots, with 4 identified DEGs (GRMZM2G133475, GRMZM2G410175, GRMZM2G144648, and GRMZM2G320269). The up-regulation of these genes were indicative of a counter response to nZnO-triggered oxidative stress in the shoot. In metal oxide NPtreated plants, the superoxide family proteins are the most commonly found stress marker (Chen et al., 2015; Tripathi et al., 2017b), and their identification in the present study strongly suggested intensive oxidative stress posed by nZnO on the maize shoot. Of the down-regulated genes in the shoots, the greatest changes were observed in the “leucine-rich repeat kinase” (LRRK) family, with 3 DEGs (GRMZM2G350793, GRMZM2G027958, and GRMZM2G073884). The members of this family are involved in multiple functions, including biotic and abiotic stress defense (Lee et al., 2004), development regulation (Shpak et al., 2004) and hormone response (Halter et al., 2014). Among the root-specific upregulated DEGs, the most significant changes were observed in the “RmlC-like cupins superfamily protein” genes, with 11 DEGs (GRMZM2G030772, GRMZM2G049930, GRMZM2G071390, GRMZM2G072965, GRMZM2G087111, GRMZM2G093076, GRMZM2G093622, GRMZM2G149714, GRMZM2G157298, GRMZM2G170829, and GRMZM2G170857) out of 154 DEGs annotated in this family. The biological function of these genes includes “nitrogen metabolism and transport,” suggesting that the shortening of the nZnO-exposed shoots was caused by the up-regulation of these genes and the subsequently enhanced nitrogen metabolism efficiency. We noted that 5 DEGs (GRMZM2G025832, GRMZM2G014395, GRMZM2G076936, GRMZM2G069722, and GRMZM2G161472) from the “Cytochrome P450 superfamily” were down-regulated in the roots, indicating that CYP540 was the family with the most down-regulated genes. More than 400 Cytochrome P450 genes are observed in the maize genome, and they have extremely diverse functions, including the catalysis of structural macromolecules, biosynthesis and catabolism of hormones, and serve as signaling molecules (Bak et al., 2011). Interestingly, another 4 “Cytochrome P450” DEGs (GRMZM2G164036, GRMZM2G470442, GRMZM2G093286, and GRMZM2G135536) were up-regulated in the exposed roots. These bi-directional changes strongly suggest that nZnO is phytotoxic and cytochrome P450 is involved in

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Table 4 Significantly enriched GO terms. Function

GO term

Description

Class DEGs

Reference (P Root (P value) value)

Nitrogen compound metabolic process

GO:0009309 GO:0009308 GO:0006576 GO:0044271 GO:0034641 GO:0044106 GO:0006519

amine biosynthetic process amine metabolic process cellular biogenic amine metabolic process cellular nitrogen compound biosynthetic process cellular nitrogen compound metabolic process cellular amine metabolic process cellular amino acid and derivative metabolic process cellular amino acid derivative metabolic process protein folding organic acid metabolic process cellular ketone metabolic process oxoacid metabolic process carboxylic acid metabolic process aromatic compound biosynthetic process cellular aromatic compound metabolic process isoprenoid biosynthetic process isoprenoid metabolic process terpenoid biosynthetic process terpenoid metabolic process secondary metabolic process

BP BP BP BP BP BP BP

7 12 (5) 5 (5) 8 (5) 12 (6) 11 (5) 12 (5)

259 640 79 427 797 554 706

Up (0.00022) a Up (4.30E-05 a Up (4.10E-05 a Up (8.50E-04 a Up (3.20E-04 a Up (5.50E-05 a Up (1.10E-04

BP BP BP BP BP BP BP BP BP BP BP BP BP

6 (5) 5 13 13 13 13 6 7 5 5 5 5 9

296 261 989 1008 987 987 201 359 110 130 71 93 302

a

BP

20/23

1931

MF

20/23

2055

GO:0016705 oxidoreductase activity, acting on paired donors MF

7/10

539

GO:0016684 oxidoreductase activity, acting on peroxide as acceptor GO:0004601 peroxidase activity GO:0016765 transferase activity, transferring alkyl or aryl (other than methyl) groups GO:0003824 catalytic activity GO:0004672 protein kinase activity GO:0004674 protein serine/threonine kinase activity GO:0006811 ion transport GO:0030001 metal ion transport GO:0022891 substrate-specific transmembrane transporter activity GO:0022857 transmembrane transporter activity GO:0006979 response to oxidative stress GO:0050896 response to stimulus

MF

5

216

Up/Down (6.20E-06/4.80E04) Up/Down (1.00E-03/1.70E05) Up/Down (8.70E-04/1.20E04) Up (3.40E-03)

MF MF

5 5 (5)

216 126

Up (3.40E-03) a Up (3.30E-04 (8.80E-07))

MF MF MF BP BP MF

65 13 11 10 6 8

11519 1661 1139 620 267 758

Down (1.50E-03)

MF BP BP

951 391 3551

GO:0006950 response to stress

BP

GO:0042221 response to chemical stimulus

BP

9 8/8 37/39 (8) 23 27/28 20 (6) 22/25

GO:0010035 GO:0010033 GO:0009628 GO:0045735 GO:0030145 GO:0043167

BP BP BP MF MF MF

645 1030 1397 100 70 4136

GO:0006575 GO:0006457 Small molecule metabolic GO:0006082 process GO:0042180 GO:0043436 GO:0019752 Cellular metabolic process GO:0019438 GO:0006725 Primary metabolic process GO:0008299 GO:0006720 GO:0016114 GO:0006721 Secondary metabolic GO:0019748 process Oxidation reduction/ GO:0055114 oxidation reduction Antioxidant activity GO:0016491 oxidoreductase activity

Catalytic activity

Transport

Response to stimulus

Nutrient reservoir Ion binding

Carbohydrate/protein binding Cellular component

Cell part

response to inorganic substance response to organic substance response to abiotic stimulus nutrient reservoir activity manganese ion binding ion binding

2243 2052

GO:0043169 cation binding

MF

GO:0046872 metal ion binding

MF

GO:0046914 transition metal ion binding GO:0005506 iron ion binding

MF MF

11 13 17 12 (11) 12 (11) 32 (15)/ 30 32 (15)/ 30 32 (15)/ 30 27 (13) 10/11 9

GO:0020037 heme binding

MF

9/10 8

575

GO:0046906 GO:0030246 GO:0051082 GO:0005576

tetrapyrrole binding carbohydrate binding unfolded protein binding extracellular region

MF MF MF CC

591 220 153 575

GO:0048046 GO:0005618 GO:0005737 GO:0030312

apoplast cell wall cytoplasm external encapsulating structure

CC CC CC CC

9/10 8 6 5 18 (11)/ 7 17 (12) 9 32 9

4135 4124 3109 712

376 659 6663 680

Shoot (P value)

(1.60E-03)) (9.60E-08)) (2.70E-04)) (6.60E-04)) (8.70E-04)) (2.50E-03))

Up (1.60E-03 (3.20E-05)) Up (9.30E-04)

Up (6.40E-04) Up (7.60E-04) Up (6.30E-04) Up (6.30E-04) Down (2.60E-04) Down (9.60E-04) Down (1.30E-04) Down (2.80E-04) Down (1.80E-05) Down (6.20E-05) Down (7.30E-06)

Down (8.30E-04) Down (4.00e-04) Up (6.10E-04) Up (1.60E-03) Down (1.50E-03)

Up (4.90E-04) Up/aDown ((7.50E-07/9.10E09 (2.30E-03)) Up/Down (2.70E-06/1.80E07) Up/Down (1.50E-04/1.50E06) Up (2.00E-04) Down (4.70E-04) Down (9.30E-05) a Up (1.30E-13 (2.10E-18)) a Up (2.80E-15 (5.40E-20)) a Up/Down (1.50E0-03 (1.50E-04)/1.90E-03) a Up/Down (1.50E0-03 (1.50E-04)/1.90E-03) a Up/Down (1.40E-03 (1.50E04)/1.80E-03) a Up (7.20E-04 (1.20E-04)) Up/Down (1.70E-03/2.50E04) Up/Down (1.40E-03/1.90E04) Up/Down (0.017/0.00024)

a

Up (2.10E-10 (1.80E-10))

a

Up (2.80E-12 (2.20E-12))

Down (1.60E-03) Up (1.60E-05) Up (7.90E-05) b Up (2.80E-06 (7.00E-04))

Up (1.90E-04) Up (2.30E-04) Up (0.00028) Down (4.60E-05) Up (8.50E-05) Up (0.0013)

Up (1.10E-04) Up (6.20E-04) Up (1.30E-04)

H. Xun et al. / Environmental Pollution 229 (2017) 479e488

487

Table 4 (continued ) Function

a b

GO term

Description

Class DEGs

Reference (P Root (P value) value)

Shoot (P value)

GO:0005886 GO:0016021 GO:0031224 GO:0005774 GO:0044437 GO:0005773

plasma membrane integral to membrane intrinsic to membrane vacuolar membrane vacuolar part vacuole

CC CC C CC CC CC

2404 1723 1790 387 392 867

Up (9.60E-04) Down (1.20E-03) Down (1.60E-03)

16 13 13 8 8 15

Down (2.80E-04) Down (3.10E-04) Down (4.80E-06)

Indicating enriched root-specific GO terms with greater than a four-fold change. Indicating enriched shoot-specific GO terms with greater than a four-fold change.

homeostasis in early maize roots. The effects of NPs have been demonstrated on different plants, such as Arabidopsis (Mukhopadhyay, 2014), tobacco (Ma et al., 2010), rice (Yang et al., 2015) and maize (Van Aken, 2015). Different plant responses were observed under NP exposure, which might reflect complex plant and NP interactions. Although nZnO (400 mg/L) significantly hindered the germination of Arabidopsis (Lee et al., 2010), our study reported different results, which might have been related to the larger genome size of maize and the increased number of abiotic stress tolerance genes to counteract environment factors. Studies of higher plants exposed to NPs have demonstrated that smaller particles (<40 nm) can be absorbed and transmitted through the vascular system, whereas larger particles aggregate in the root tissue and clog the conductive system (Asli and Neumann, 2009; Geislerlee et al., 2012; Ma et al., 2010). TiO2 NPs inhibited maize leaf growth (Asli and Neumann, 2009), which might have been caused by the clogging of root cell pores with TiO2 NPs with a diameter of 3 nm. However, shortened roots were observed in nZnO-exposed maize seedlings, and adverse effects on leaf growth were not observed, and these changes were related to the characteristics of nZnOs instead of Zn ions (Lin and Xing, 2007; Su et al., 2015). Similar effects were observed in our study, and nZnOs with a diameter of 50 nm were too large to block the maize root cell wall pore, which was only 6.6 nm in diameter (Asli and Neumann, 2009). Adverse effects were not observed in our study via the RNA-sequencing analysis, and water-stress or oxygen-deficiency related gene expression changes were not observed. The oversized nZnOs did not prevent water from entering the roots and did not disrupt the water supply to the shoots; thus, these particles only affected the growth of the roots. Significant transcriptional repression of phosphate metabolism genes has been observed and widely studied (García-S anchez et al., 2015; Misson et al., 2005), and it is likely a response to NP exposure-induced phosphate starvation. The inhibition of primary root growth in A. thaliana was observed because of the plant's adaptation to phosphate deficiency, which promotes topsoil ret et al., 2011). In the present study, foraging (Abel, 2011; Pe phosphate metabolism genes were not up-regulated in any organ, although 2 “phosphate transporter” genes (GRMZM2G009045 and GRMZM2G326707) were down-regulated in the nZnO-exposed roots and shoots, respectively. This unexpected finding might have been caused by the shorter duration of the nZnO treatment, which may have been insufficient to trigger the response of phosphorus starvation genes. Similar to most plants, maize usually changes its root morphology in response to environment nitrogen availability, and it increases its root length and biomass in nitrogen deficient conditions or shortens its roots in nitrogen sufficient conditions (Bonifas and Lindquist, 2009; Liu et al., 2009). In the present study, all “nitrogen compound metabolic processes” were exclusively

highly up-regulated in the nZnO-exposed roots, which were significantly shortened. Zn applications have been reported to increase the utilization of nitrogen (Barrameda-Medina et al., 2017). We speculated that the exposure to nZnO triggered the maize root nitrogen sensor and indicated an abundant nitrogen supply; thus, the nitrogen metabolism genes were up-regulated, which in turn shortened the root length. 5. Conclusions The gene expression changes between the nZnO-exposed and mock maize treatments were assessed via RNA-Seq and comparison analyses. The results showed that most of the DEGs and annotated GO terms were shoot or root specific, although a relatively small number of terms were shared between the shoots and roots. Compared with a number of previous studies in which genes were predominantly down-regulated in NP-exposed organisms, our results indicated that more organ-specific up-regulated genes than down-regulated genes were identified in the roots. The GO analysis further revealed the enriched DEG patterns between the shoots and roots. The roots exclusively and highly up-regulated genes associated with the terms “nitrogen compound metabolism,” “nutrient reservoir,” as well as “small molecule metabolic process”, which strongly suggests a relationship between enhanced nitrogen absorbing abilities and shortened roots. Our findings suggested that ZnO nanoparticles could impose their affections to exposed plants by altering genes expression levels in certain pathways in an organ specific manner. The molecular mechanisms of plants to nanoparticles exposure could be both plant species and chemical composition dependent. Acknowledgements This work was supported by the International Science & Technology Cooperation Program of China (2014DFA31740), National Transgenic Maize Project (2014ZX0300305B) and Natural Science Fund of Jilin Province (20150101083JC). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envpol.2017.05.066. References Abel, S., 2011. Phosphate sensing in root development. Curr. Opin. Plant Biol. 14, 303e309. Asli, S., Neumann, P.M., 2009. Colloidal suspensions of clay or titanium dioxide nanoparticles can inhibit leaf growth and transpiration via physical effects on root water transport. Plant Cell Environ. 32, 577e584. Atha, D.H., Wang, H., Petersen, E.J., Cleveland, D., Holbrook, R.D., Jaruga, P., Dizdaroglu, M., Xing, B., Nelson, B.C., 2012. Copper oxide nanoparticle mediated DNA damage in terrestrial plant models. Enviro. Sci. Technol. 46, 1819e1827. Aziz, N., Faraz, M., Pandey, R., Shakir, M., Fatma, T., Varma, A., Barman, I., Prasad, R.,

488

H. Xun et al. / Environmental Pollution 229 (2017) 479e488

2015. Facile algae-derived route to biogenic silver nanoparticles: synthesis, antibacterial, and photocatalytic properties. Langmuir 31, 11605e11612. Aziz, N., Pandey, R., Barman, I., Prasad, R., 2016. Leveraging the attributes of mucor hiemalis-derived silver nanoparticles for a synergistic broad-spectrum antimicrobial platform. Front. Microbiol. 7, 1984. € fer, R., Paquette, S., WerckBak, S., Beisson, F., Bishop, G., Hamberger, B., Ho Reichhart, D., 2011. Cytochromes p450, the Arabidopsis Book. American Society of Plant Biologists, p. e0144. Barrameda-Medina, Y., Lentini, M., Esposito, S., Ruiz, J.M., Blasco, B., 2017. Zn-biofortification enhanced nitrogen metabolism and photorespiration process in green leafy vegetable Lactuca sativa L. J. Sci. Food Agric. 97, 1828e1836. Bonifas, K.D., Lindquist, J.L., 2009. Effects of nitrogen supply on the root morphology of corn and velvetleaf. J. Plant Nutr. 32, 1371e1382. Bystrzejewska-Piotrowska, G., Asztemborska, M., Steborowski, R., PolkowskaMotrenko, H., Danko, B., Ryniewicz, J., 2012. Application of neutron activation for investigation of fe3o4 nanoparticles accumulation by plants. Nukleonika 57, 427e430. Chen, J., Liu, X., Wang, C., Yin, S.S., Li, X.L., Hu, W.J., Simon, M., Shen, Z.J., Xiao, Q., Chu, C.C., Peng, X.X., Zheng, H.L., 2015. Nitric oxide ameliorates zinc oxide nanoparticles-induced phytotoxicity in rice seedlings. J. Hazard. Mater 297, 173e182. De La Torre-Roche, R., Hawthorne, J., Deng, Y., Xing, B., Cai, W., Newman, L.A., Wang, C., Ma, X., White, J.C., 2012. Fullerene-enhanced accumulation of p,p'-dde in agricultural crop species. Environ. Sci. Technol. 46, 9315e9323. De La Torre-Roche, R., Hawthorne, J., Musante, C., Xing, B., Newman, L.A., Ma, X., White, J.C., 2013. Impact of ag nanoparticle exposure on p,p'-dde bioaccumulation by Cucurbita pepo (zucchini) and Glycine max (soybean). Environ. Sci. Technol. 47, 718e725. Dimkpa, C.O., McLean, J.E., Martineau, N., Britt, D.W., Haverkamp, R., Anderson, A.J., 2013. Silver nanoparticles disrupt wheat (Triticum aestivum L.) growth in a sand matrix. Environ. Sci. Technol. 47, 1082e1090. Du, W., Sun, Y., Ji, R., Zhu, J., Wu, J., Guo, H., 2011. TiO2 and ZnO nanoparticles negatively affect wheat growth and soil enzyme activities in agricultural soil. J. Environ. Monit. 13, 822e828. Du, Z., Zhou, X., Ling, Y., Zhang, Z.H., Su, Z., 2010. Agrigo: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 38, W64eW70.  mez, C., Babin, M., Obrador, A., Alvarez, ndez, M.D., 2015. García-Go J.M., Ferna Integrating ecotoxicity and chemical approaches to compare the effects of ZnO nanoparticles, ZnO bulk, and ZnCl2 on plants and microorganisms in a natural soil. Environ. Sci. Pollut. R. 22, 16803e16813. nchez, S., Bernales, I., Cristobal, S., 2015. Early response to nanoparticles in García-Sa the Arabidopsis transcriptome compromises plant defence and root-hair development through salicylic acid signalling. BMC Genomics 16, 341. Geislerlee, J., Wang, Q., Yao, Y., Zhang, W., Geisler, M., Li, K., Huang, Y., Chen, Y., Kolmakov, A., Ma, X., 2012. Phytotoxicity, accumulation and transport of silver nanoparticles by arabidopsis thaliana. Nanotoxicology 7, 323e337. Halter, T., Imkampe, J., Mazzotta, S., Wierzba, M., Postel, S., Bücherl, C., Kiefer, C., Stahl, M., Chinchilla, D., Wang, X., 2014. The leucine-rich repeat receptor kinase bir2 is a negative regulator of bak1 in plant immunity. Curr. Biol. 24, 134e143. Hillerdal, G., Henderson, D.W., 1997. Asbestos, asbestosis, pleural plaques and lung cancer. Scand. J. Work Env. Hea 23, 93e103. Hong, F., Zhou, J., Liu, C., Yang, F., Wu, C., Zheng, L., Yang, P., 2005. Effect of nano-TiO2 on photochemical reaction of chloroplasts of spinach. Biol. Trace Elem. Res. 105, 269e279. Inoue, H., Higuchi, K., Takahashi, M., Nakanishi, H., Mori, S., Nishizawa, N.K., 2003. Three rice nicotianamine synthase genes, osnas1, osnas2, and osnas3 are expressed in cells involved in long-distance transport of iron and differentially regulated by iron. Plant J. 36, 366e381. Ito, A., Shinkai, M., Honda, H., Kobayashi, T., 2005. Medical application of functionalized magnetic nanoparticles. J. Biosci. Bioeng. 100, 1e11. Kaveh, R., Li, Y.S., Ranjbar, S., Tehrani, R., Brueck, C.L., Van Aken, B., 2013. Changes in Arabidopsis thaliana gene expression in response to silver nanoparticles and silver ions. Enviro. Sci. Technol. 47, 10637e10644. Khodakovskaya, M., Dervishi, E., Mahmood, M., Xu, Y., Li, Z., Watanabe, F., Biris, A.S., 2009. Carbon nanotubes are able to penetrate plant seed coat and dramatically affect seed germination and plant growth. ACS Nano 3, 3221e3227. Klaine, S.J., Alvarez, P.J.J., Batley, G.E., Fernandes, T.F., Handy, R.D., Lyon, D.Y., Mahendra, S., McLaughlin, M.J., Lead, J.R., 2008. Nanomaterials in the environment: behavior, fate, bioavailability, and effects. Environ. Toxicol. Chem. 27, 1825e1851. Landa, P., Vankova, R., Andrlova, J., Hodek, J., Marsik, P., Storchova, H., White, J.C., Vanek, T., 2012. Nanoparticle-specific changes in Arabidopsis thaliana gene expression after exposure to ZnO, TiO2, and fullerene soot. J. Hazard. Mater 241e242, 55e62. Lee, C.W., Mahendra, S., Zodrow, K., Li, D., Tsai, Y.C., Braam, J., Alvarez, P.J.J., 2010. Developmental phytotoxicity of metal oxide nanoparticles to Arabidopsis thaliana. Environ. Toxicol. Chem. 29, 669e675. Lee, S.C., Kim, J.Y., Kim, S.H., Kim, S.J., Lee, K., Han, S.K., Choi, H.S., Jeong, D.H., An, G., Kim, S.R., 2004. Trapping and characterization of cold-responsive genes from T-

DNA tagging lines in rice. Plant Sci. 166, 69e79. Lin, D., Xing, B., 2007. Phytotoxicity of nanoparticles: inhibition of seed germination and root growth. Environ. Pollut. 150, 243e250. Lin, D., Xing, B., 2008. Root uptake and phytotoxicity of ZnO nanoparticles. Environ. Sci. Technol. 42, 5580e5585. Liu, J., Chen, F., Olokhnuud, C., Glass, A.D.M., Tong, Y., Zhang, F., Mi, G., 2009. Root size and nitrogen-uptake activity in two maize (Zea mays) inbred lines differing in nitrogen-use efficiency. J. Plant Nutr. Soil Sci. 172, 230e236. Ma, X., Geisler-Lee, J., Deng, Y., Kolmakov, A., 2010. Interactions between engineered nanoparticles (enps) and plants: phytotoxicity, uptake and accumulation. Sci. Total Environ. 408, 3053e3061. Mishra, V., Mishra, R.K., Dikshit, A., Pandey, A.C., 2014. Chapter 8-Interactions of Nanoparticles with Plants: an Emerging Prospective in the Agriculture Industry. Emerging Technologies & Management of Crop Stress Tolerance. Elsevier Inc., pp. 159e180 Misson, J., Raghothama, K.G., Jain, A., Jouhet, J., Block, M.A., Bligny, R., Ortet, P., Creff, A., Somerville, S., Rolland, N., Doumas, P., Nacry, P., Herrerra-Estrella, L., Nussaume, L., Thibaud, M.C., 2005. A genome-wide transcriptional analysis using Arabidopsis thaliana affymetrix gene chips determined plant responses to phosphate deprivation. P. Natl. Acad. Sci. U. S. A. 102, 11934e11939. Mukhopadhyay, S.S., 2014. Nanotechnology in agriculture: prospects and constraints. Nanotechnol. Sci. Appl. 7, 63e71. €rster, G., 2010. Safety assessment for nanotechnology and nanomedicine: Oberdo concepts of nanotoxicology. J. Intern. Med. 267, 89e105. ret, B., Cle ment, M., Nussaume, L., Desnos, T., 2011. Root developmental adaptaPe tion to phosphate starvation: better safe than sorry. Trends Plant Sci. 16, 442e450. Phillips, K., Zaidan 3rd, F., Elizondo, O.R., Lowe, K.L., 2012. Phenotypic characterization and 16S rDNA identification of culturable non-obligate halophilic bacterial communities from a hypersaline lake, La Sal del Rey, in extreme South Texas (USA). Aquat. Biosyst. 8, 1e11. Prasad, R., Pandey, R., Barman, I., 2016. Engineering tailored nanoparticles with microbes: Quo vadis? WIREs Nanomed. Nanobiotechnol 8, 316e330. Sabir, S., Arshad, M., Chaudhari, S.K., 2014. Zinc oxide nanoparticles for revolutionizing agriculture: synthesis and applications. Sci. World J. 2014, 1e8. Saxena, N., 2009. Emerging trends of nanoparticles application in food technology: safety paradigms. Nanotoxicology 3, 10e18. Schmid, K., Danuser, B., Riediker, M., 2010. Nanoparticle usage and protection measures in the manufacturing industry-a representative survey. J. Occup. Environ. Hyg. 7, 224e232. Sekhon, B.S., 2014. Nanotechnology in agri-food production: an overview. Nanotechnol. Sci. Appl. 7, 31e53. Shpak, E.D., Berthiaume, C.T., Hill, E.J., Torii, K.U., 2004. Synergistic interaction of three erecta-family receptor-like kinases controls arabidopsis organ growth and flower development by promoting cell proliferation. Development 131, 1491e1501. Su, G.Y., Zhang, X.W., Giesy, J.P., Musarrat, J., Saquib, Q., Alkhedhairy, A.A., Yu, H.X., 2015. Comparison on the molecular response profiles between nano zinc oxide (ZnO) particles and free zinc ion using a genome-wide toxicogenomics approach. Environ. Sci. Pollut. R. 22, 17434e17442. Trapnell, C., Pachter, L., Salzberg, S.L., 2009. Tophat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105e1111. Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., Pachter, L., 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511e515. Tripathi, D.K., Shweta, Singh, S., Singh, S., Pandey, R., Singh, V.P., Sharma, N.C., Prasad, S.M., Dubey, N.K., Chauhan, D.K., 2017a. An overview on manufactured nanoparticles in plants: uptake, translocation, accumulation and phytotoxicity. Plant Physiol. Biochem. 110, 2e12. Tripathi, D.K., Singh, S., Singh, S., Srivastava, P.K., Singh, V.P., Singh, S., Prasad, S.M., Singh, P.K., Dubey, N.K., Pandey, A.C., Chauhan, D.K., 2017b. Nitric oxide alleviates silver nanoparticles (agnps)-induced phytotoxicity in Pisum sativum seedlings. Plant Physiol. Biochem. 110, 167e177. Van Aken, B., 2015. Gene expression changes in plants and microorganisms exposed to nanomaterials. Curr. Opin. Biotech. 33, 206e219. Wang, X., Yang, X., Chen, S., Li, Q., Wang, W., Hou, C., Gao, X., Wang, L., Wang, S., 2015. Zinc oxide nanoparticles affect biomass accumulation and photosynthesis in arabidopsis. Front. Plant Sci. 6, 1243. Yang, Z., Chen, J., Dou, R., Gao, X., Mao, C., Wang, L., 2015. Assessment of the phytotoxicity of metal oxide nanoparticles on two crop plants, maize (Zea mays L.) and rice (Oryza sativa L.). Int. J. Environ. Res. Public Health 12, 15100e15109. Zafar, H., Ali, A., Ali, J.S., Haq, I.U., Zia, M., 2016. Effect of ZnO nanoparticles on Brassica nigra seedlings and stem explants: growth dynamics and antioxidative response. Front. Plant Sci. 7, 535. Zhu, Z.J., Wang, H., Yan, B., Zheng, H., Jiang, Y., Miranda, O.R., Rotello, V.M., Xing, B., Vachet, R.W., 2012. Effect of surface charge on the uptake and distribution of gold nanoparticles in four plant species. Environ. Sci. Technol. 46, 12391e12398.