Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Distinct transcriptional profiles of ozone stress in soybean (Glycine max) flowers and pods

BMC Plant Biology volume 14, Article number: 335 (2014) Cite this article

Abstract

Background

Tropospheric ozone (O3) is a secondary air pollutant and anthropogenic greenhouse gas. Concentrations of tropospheric O3 ([O3] have more than doubled since the Industrial Revolution, and are high enough to damage plant productivity. Soybean (Glycine max L. Merr.) is the world’s most important legume crop and is sensitive to O3. Current ground-level [O3] are estimated to reduce global soybean yields by 6% to 16%. In order to understand transcriptional mechanisms of yield loss in soybean, we examined the transcriptome of soybean flower and pod tissues exposed to elevated [O3] using RNA-Sequencing.

Results

Elevated [O3] elicited a strong transcriptional response in flower and pod tissues, with increased expression of genes involved in signaling in both tissues. Flower tissues also responded to elevated [O3] by increasing expression of genes encoding matrix metalloproteinases (MMPs). MMPs are zinc- and calcium-dependent endopeptidases that have roles in programmed cell death, senescence and stress response in plants. Pod tissues responded to elevated [O3] by increasing expression of xyloglucan endotransglucosylase/hydrolase genes, which may be involved with increased pod dehiscence in elevated [O3].

Conclusions

This study established that gene expression in reproductive tissues of soybean are impacted by elevated [O3], and flowers and pods have distinct transcriptomic responses to elevated [O3].

Background

Current tropospheric O3 concentrations ([O3]) are estimated to cost $14 to $26 billion in annual global crop economic losses [1] and severely impact human health, accounting for an estimated 0.7 million deaths per year [2]. Ozone in the troposphere is formed through the photochemical oxidation of volatile organic compounds (VOCs), carbon monoxide and methane in the presence of nitrogen oxides (NOx) [3]. Ozone is a dynamic pollutant and concentrations vary temporally and spatially, with higher concentrations in the Northern Hemisphere compared to the Southern Hemisphere, and typically higher [O3] in the summer compared to the winter [3]. Background tropospheric [O3] have more than doubled since the Industrial Revolution and are projected to increase by an additional ~20% by the year 2100 if current high emission rates continue [4]. In the crop growing regions of the Northern Hemisphere, summer concentrations of O3 often exceed 40 ppb, which exceeds the critical threshold for damage to sensitive crops, including soybean (Glycine max) [5].

When taken up by plants, O3 is converted into other reactive oxygen species (ROS), and can induce signaling pathways that lead to programmed cell death, especially with exposure to very high [O3] [6]. At lower concentrations, chronic exposure to elevated [O3] decreases photosynthetic carbon assimilation and stomatal conductance, and accelerates the process of senescence [7],[8]. In addition to leaf-level effects, O3 negatively impacts plant fitness and reproductive development, which can be mediated through reduced carbon allocation from source tissues and/or through direct effects on reproductive tissues [9],[10]. A meta-analysis of published studies from 1968 to 2010 of O3 effects on plant reproductive processes reported that exposure to elevated [O3] decreased seed number and seed size, as well as fruit number and fruit size when compared to plants grown in charcoal-filtered, O3-free air [11]. However, the meta-analysis also showed that elevated [O3] did not significantly alter inflorescence number, flower weight or flower number [11]. This suggests that plants can compensate to some extent from O3 damage [12], and also that the effects of O3 can be tissue-specific.

Soybeans have naturally high levels of floral and pod loss, and subsequent seed and yield loss is greatest when stress occurs during flower and early pod development [13]. Flower and pod abscission can range from 32 to 82% in soybean [14]-[16], but this varies considerably with location on the plant [15]-[17], location in the canopy [18], source-sink relations [19], hormone levels [13],[20],[21], shade [22] and water status [13],[23],[24]. Ethylene promotes flower and pod abscission in soybean [25], and elevated [O3] can increase ethylene emission in plants [26]. Therefore, elevated [O3] has the potential to increase flower and pod abscission. In field-grown soybean exposed to elevated [O3] for an entire growing season [27], pod production was decreased by elevated [O3], but flower number was not affected (Figure 1). Based on this evidence from the field, it is hypothesized that the transcriptional responses of soybean flowers and pods to elevated [O3] would be distinct.

Figure 1
figure 1

The effect of O 3 on the number of flowers and pods produced per node in field-grown soybean. (a) Linear regression of the average number of pods per node for soybean plants grown under eight [O3] at the SoyFACE facility (http://www.igb.illinois.edu/soyface/) in Champaign, Illinois in 2009 and 2010. Blue lines show the 95% confidence intervals. Experimental design, planting conditions, meteorological data and harvesting methods are found in [27]. (b) Average flower number per node for soybean plants grown under ambient (44 ppb) and elevated (100 ppb) [O3] at the SoyFACE facility in 2011. Flower number per node was monitored daily for five plants per ambient and elevated [O3] plot (n = 2 for ambient, n = 4 for elevated [O3]).

Full size image

Previous studies have examined changes in transcript abundance in plants in response to elevated [O3] [6],[28]-[35]; however, most of these studies have focused on leaves. In soybean, both flower and pod tissues also have stomata through which O3 could enter and elicit a signaling response [36],[37]. Next-generation sequencing technology allows examination of changes to the entire transcriptome, which could facilitate interpretation of the complex phenotypes that underpin O3 response in plants. By investigating how elevated [O3] affects the transcriptome of reproductive tissues, we can begin to understand the distinct responses in different tissues and identify potential targets for improving tolerance. Therefore, in this study, the transcriptome of flower and pod tissue from chamber-grown soybean plants at ambient (<20 ppb) and elevated [O3] (150 ppb) was investigated. Both flower and pod tissues showed significant transcriptomic responses to elevated [O3]. While 277 transcripts were responsive to elevated [O3] in both tissues, most of those transcripts did not change in the same direction or at the same magnitude in flowers and pods, indicating that the transcriptional response to O3 in different reproductive tissues was distinct.

Results and discussion

Overlapping effects of elevated [O3] on the transcriptome of flower and pod tissue in soybean

Flower and pod development in soybean are sensitive to environmental stress [23],[24],[38],[39], and elevated [O3] significantly impacted pod production, but not flower production (Figure 1). In order to identify the genetic mechanisms underpinning O3 response in soybean pods and flowers, the transcriptome of flower and pod tissues was compared using RNA-Sequencing (RNA-Seq). The global transcriptional analysis showed the magnitude of potential responses to elevated [O3] in flowers and pods was similar, with genes showing approximately the same range of both mean expression values in flowers and pods, and similar potential log fold change responses to elevated [O3] in the two tissues (Figure 2). However, more than three times as many genes were differentially expressed in flower tissue (4,595 genes) than in pod tissue (1,375 genes; Figure 3) in response to elevated [O3], and only 277 of those genes were differentially expressed in both flowers and pods (Figure 3).

Figure 2
figure 2

Comparison of differential gene expression in flower and pod tissue under elevated [O 3 ]. The log fold change for all genes differentially expressed in flower and pod tissue (p <0.05) was plotted against the mean expression value for that gene measured in both ambient and elevated [O3]. Black circles represent genes differentially expressed in flowers and red circles represent genes differentially expressed in pods. Green triangles represent MMP genes differentially expressed in flowers. Yellow squares represent XTH genes differentially expressed in pods. Reference line represents a log fold change of zero. Values above the reference line are genes increased in abundance compared to ambient [O3] and values below the reference line are genes decreased in abundance compared to ambient [O3].

Full size image
Figure 3
figure 3

Venn diagram of differentially expressed genes in flower and pod tissues in response to elevated [O 3 ]. Numbers of genes that were differentially expressed in response to elevated [O3] in flowers (green), pods (purple) and in both tissues (overlapping).

Full size image

Differentially expressed genes in pods and flowers were grouped into functional categories (Figure 4). Nine of 15 total functional categories showed pod and flower genes changing in the same direction in response to elevated [O3] (Figure 4). Transcripts involved in signaling, development, transport, stress, protein and RNA were expressed at greater levels on average in both pods and flowers exposed to elevated [O3] compared to control (Figure 4). While average changes in expression based on functional categories suggests that there was overlap in the transcriptional response of flowers and pods to elevated [O3], investigation of individual genes showed that there was not good correspondence of the direction or magnitude of the response (Figure 5). Less than half of the 277 genes that were significantly affected by elevated [O3] in both flowers and pods responded in a similar direction, with 78 of the 277 genes increasing in both tissues in response to elevated [O3] and 33 decreasing in both tissues in response to elevated [O3] (Figure 5). Many of the transcripts that fell on the 1:1 line in Figure 5 were involved in signaling and RNA processing, including 12 leucine-rich repeat receptor-like kinases (RLKs) and 3 cysteine-rich Domain of Unknown Function 26 (DUF26) RLKs (also known as cysteine-rich receptor-like kinases, CRK). Plant RLKs are transmembrane proteins involved in signal perception and form a large multi-gene family with regulatory roles in development, abiotic and biotic stress responses in plants [40],[41]. Recent analysis of the response of Arabidopsis DUF26 RLKs showed that many of the 44 RLKs were specifically up-regulated in response to O3 stress in leaves [42], including DUF26 30 (CRK 26), DUF26 29 (CRK 29) and DUF26 41 (CRK 2), which also had a significant increase in expression in soybean pods and flowers exposed to elevated [O3]. Wraczek et al. [42] found that the general pattern of DUF26 expression responses to O3 was most similar to the transcriptional response to pathogen infection, which like O3 elicits an ROS burst in the apolost. The transcriptional response to O3 however, was very different from expression responses to high light treatments or chemical treatments that increased ROS production in chloroplasts or mitochondria [42]. Thus, it was further suggested that the DUF26 domain, which has a conserved cysteine motif C-8X-C-2X-C, could act as an apolastic ROS sensor [42].

Figure 4
figure 4

Average fold change of genes differentially expressed in both flowers and pods in response to elevated [O 3 ]. Average log fold change of all genes within a functional category that significantly responded to elevated [O3] in both flowers (black bars) and pods (red bars). A positive log fold change indicates increased abundance in elevated [O3] compared to ambient [O3], while a negative log fold change indicates decreased abundance in elevated [O3].

Full size image
Figure 5
figure 5

Comparison of expression changes in response to elevated [O 3 ] in soybean flowers and pods. The log fold change of the 277 individual genes significantly changing in response to elevated [O3] in both pods vs. flowers is shown. Functional groups are represented by different symbols/colors. The 1:1 line represents genes that have the same direction of fold change in flower and pod tissue.

Full size image

A number of WRKY domain transcription factors also showed significantly greater expression under elevated [O3] in both soybean flowers and pods (Glyma04g40130, Glyma06g14720, Glyma14g36430, Glyma14g36438, Glyma14g36446). The WRKY transcription factor family is one of the largest families of transcription factors in plants, with 133 members in the soybean genome [43]. WRKYs function in many plant processes including response to biotic and abiotic stresses, and senescence [44]. Up-regulation of WRKY transcription factors in response to O3 stress has been previous reported, primarily in the leaves of trees [45]-[47]. Two of the WRKY transcription factors with increased expression in both flower and pod tissues in response to elevated [O3] (Glyma04g40130 and Glyma06g14720) were likely formed through a segmental duplication event that is estimated to have occurred 20 million years ago [43].

Distinct effects of elevated [O3] on the transcriptome of flower and pod tissue in soybean

Although there was some overlap in transcriptional responses to elevated [O3] in flowers and pods, the vast majority of genes changing in either tissue were distinct (Figure 3), and even among the genes that were expressed in both flower and pod tissues, the fold changes in expression were of different magnitudes or in the opposite direction (Figure 5). In flower tissue, the genes with the greatest increase in abundance in response to elevated [O3] included matrix metalloproteinase (MMP) genes and genes related to hormone metabolism and signaling (Table 1). Genes annotated as MMPs also had high mean expression levels (Figure 2). While little is known about their role in soybean flowers, in other tissues MMPs function in degradation of the extracellular matrix (ECM) in response to senescence, stress and programmed cell death [48]-[51]. Domain analysis indicated that 7 of the 9 differentially expressed genes annotated as MMPs had both a cysteine switch domain and a zinc-binding domain, both of which are required for characterization as a MMP (Figure 6) [52]-[54]. Those genes with both required domains were termed putative soybean flower MMP genes.

Table 1 Genes with the greatest log fold change in response to elevated [O 3 ] in flower tissues
Full size table
Figure 6
figure 6

Domain analysis of plant matrix metalloproteinase (MMP) genes. General structure of known plant MMPs and putative MMPs identified in soybean flowers. The cysteine switch and zinc binding domain sequence motifs are shown for all genes (when present). The E to Q residue substitution in the zinc-binding motif of the catalytic domain is indicated in red.

Full size image

The putative MMP gene Glyma02g03301 had two identical cysteine switch domains and zinc-binding domains, which was unique compared to the other putative flower MMP genes. All putative flower MMP genes had a signal peptide and transmembrane domain, with the exception of Glyma02g03250 (Figure 6; Additional file 1). Several of the putative flower MMP genes contained a GPI-anchor modification site, which was similar to 3 Arabidopsis MMP genes, At2-MMP, At4-MMP, At5-MMP [47], and the known soybean MMP gene GmMMP2 (referred to as Gm2-MMP) [42] (Figure 6). None of the putative flower MMP genes or known soybean genes (Gm2-MMP or SMEP1) [48],[55],[56] contained a furin cleavage site, which was present in several Arabidopsis MMP genes. When amino acid sequence similarity identity was compared between all putative flower MMP genes and Gm2-MMP using Clustal W (http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_server.html), little homology between the flower MMP and leaf MMP genes was found, with the exception of Glyma01g04350 which showed 99% sequence similarity to Gm2-MMP (data not shown). All putative flower MMP genes, with the exception of Glyma01g04350, had an E (glutamate) to Q (glutamine) residue substitution in the zinc-binding motif of the catalytic domain, which has been identified in other legume species [51]. The glutamate residue is required for functional protease activity [57], thus the amino acid switch in soybean flower MMPs may render these inactive. Still, they may be important for O3 stress response because experiments with Medicago truncatula have also demonstrated a functional role for proteolytically-inactive MMPs in biotic stress response [51].

The responsiveness of putative soybean flower MMPs to elevated [O3] is consistent with the ECM being the primary point of O3 contact within plant cells and the location where antioxidant metabolism begins to protect cells from ROS damage [34],[58]. Stress-responsive signaling pathways, including jasmonic acid, salicylic acid and ethylene-dependent redox signaling are all triggered by the redox sensing that occurs in the ECM [34]. The soybean MMP gene Gm2-MMP was up-regulated consistently with the release of ROS during pathogenic infection [50], possibly linking ROS signaling and MMP gene expression in soybean stress response. Previous analysis of Arabidopsis MMP gene expression revealed that At3-MMP was expressed at greater abundance in response to O3 treatment, with a slight increase in At2-MMP in response to O3 as well [59]. While the putative MMP genes are present in high abundance in flower tissues exposed to elevated [O3], analysis of the expression profiles of the putative MMP genes in soybean using RNA-Seq Atlas (http://soybase.org/soyseq/) found that these genes were not present, or present in low abundance in other soybean tissues. Therefore, it is hypothesized that the increase in abundance of the putative MMP genes identified in this study may represent a distinct flower response to O3 stress in soybean.

In pod tissue, cell wall modification and calcium signaling genes showed the greatest increase in abundance in response to elevated [O3] (Table 2). Gene ontology (GO) enrichment analysis of biological processes was performed for genes differentially expressed only in pod tissue. Apoptosis, signal transduction, ATP biosynthetic processes, cellular glucan metabolic processes, protein amino acid phosphorylation and innate immune responses were enriched in pod tissue (Additional file 2). These activities are known to increase in plants in response to both abiotic and biotic stress [60]-[65], and the possibility that O3 stress co-opts pathways involved in biotic stress response has been previously proposed [66],[67]. The genes with the greatest increase in abundance in response to elevated [O3] were xyloglucan endotransglucosylase/hydrolase family proteins (XTH) (Table 2). Genes annotated as XTHs also had high mean expression, along with the greatest increase in abundance in response to O3 in pod tissue (Figure 2). These genes belong to the GO biological process of cellular glucan metabolic processes, which is highly enriched in pod tissues (Figure 7). Analysis of the putative XTH genes in soybean using RNA-Seq Atlas (http://soybase.org/soyseq/) showed that these genes were not present or in low abundance in other tissues, indicating that these genes may represent a distinct pod response to elevated [O3].

Table 2 Genes with the greatest log fold change in response to elevated [O 3 ] in pod tissues
Full size table
Figure 7
figure 7

Analysis of gene ontology (GO) term enrichment of biological processes containing XTH genes in pod tissues. Biological terms with increasing overrepresentation in pod tissues exposed to elevated [O3] are represented by increasingly red colors. GO term enrichment was performed using single enrichment analysis (SEA) tool on AgriGo (http://bioinfo.cau.edu.cn/agriGO/).

Full size image

XTH is a well-known cell wall-modifying enzyme that plays a role in growth and differentiation in plants [68]. Genes in the XTH family are involved in cell elongation in vascular cells [69],[70], epidermal cells [71], inflorescence apices [72], primary roots [73] and during somatic embryogenesis [74]. XTH also plays a role in floral organ abscission [75],[76]. Due to the similarity of flower abscission and pod dehiscence zone [77] and the known response of XTH genes to oxidative [78], water [79] and biotic stress [80], it is hypothesized that XTH genes may play a role in pod dehiscence in soybean exposed to elevated [O3].

Conclusion

Soybean is an O3-sensitive crop, with current tropospheric [O3] costing billions of dollars in lost production annually. In this study, it was established that gene expression in reproductive tissues in soybean is altered by elevated [O3]. There were 4,703 transcripts responsive to elevated [O3] in both flower and pod tissues, yet those genes did not respond consistently in the two tissues. This indicates that reproductive tissues have more distinct than similar transcriptomic responses to elevated [O3]. Flower tissues responded to elevated [O3] through increased expression of MMP genes. It was notable that these flower MMP genes may not be proteolytically active based on amino acid composition, but they clearly respond to O3 stress. Pod tissues responded to elevated [O3] through increased expression of cell expansion genes. The increased transcript abundance of XTH genes supports a role of these genes in pod dehiscence in soybean exposed to elevated [O3].

Methods

Growth chamber experimental design and conditions

Soybean (Glycine max L. Merr. cv. 93B15; Pioneer Hi-Breed) was grown in ambient air (<20 ppb) and elevated ozone (150 ppb) in 14 h/10 h day/night schedules under PPFD of ~650-750 μmol m−2 s−1; RH 60%; 25°C day/21°C night conditions in 8 growth chambers (Conviron, Winnipeg, Manitoba, Canada). Soybean plants were grown in 6-L pots (Classic C600, Nursery Supplies, Chambersburg PA, USA) in sterilized soil (LC-1 Sunshine Mix (SunGro Horticulture Canada Ltd, Bellevue, WA, USA)) and treated with 50% Long Ashton solution supplemented with 3 mM NH4NO3 [81]. Two seeds were planted per pot ~4 cm below the soil surface and then thinned to one plant per pot once seeds successfully germinated. A total of 12 plants were grown per chamber in a randomized complete block design (n = 4). Plants were rotated among chambers once a week and within chambers every two days to minimize chamber effects.

Tissue sampling and molecular analyses

Tissue sampling for RNA was done during R2 (full bloom) and R4 (full pod) for growth chamber grown plants. Plants were considered at full bloom when there was an open flower at one of the first two uppermost nodes with a fully expanded leaf. Plants were considered at full pod when there was a pod 2 cm in length present on one of the four uppermost nodes with a fully expanded leaf. At each stage the appropriate tissue was sampled (full open flowers at R2 and initiating pods at R4). Sampling was done at the nodes 2–4 (from the top of the plant) in order to avoid compensation and senescence effects on the upper and lowermost nodes. Tissue from four plants was sampled per developmental stage per block. Immediately after collection flower and pod tissue was plunged into liquid N and stored at −80°C. Flower or pod tissue was ground to a fine power using a mortar and pestle.

Total RNA was extracted from ground tissue using PureLink Plant RNA Reagent (Ambion, by Life Technologies Corp., Grand Island, NY, USA) according to the manufacturer’s protocol. RNA quantity was determined with a spectrophotometer (Nanodrop 1000, Thermo Fischer Scientific, Waltham, MA, USA) and RNA quality was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) on an RNA Nano chip. Genomic DNA contamination was removed from RNA samples using Turbo DNase treatment (Applied Biosystems/Ambion, Austin, TX, USA) according to the manufacturer’s protocol. cDNA library preparation was done using the Illumnina TruSeq Sample Prep kits (Illumina Inc. San Diego, CA, USA). Each library fragment was barcoded during library preparation and multiplexed for sequencing. Tissue samples per block (4 subsamples) were pooled for a total of 8 libraries prepared for each tissue (16 libraries total).

RNA-sequencing (RNA-seq), bioinformatics and statistical analysis

Sequencing was done at the Roy J. Carver Biotechnology Center using the Illumina Genome HiSeq 2000 (Illumina Inc. San Diego, CA, USA, http://www.illumina.com) and Cassava pipeline 1.8 to obtain 100 nt single-end reads. Samples were sequenced in groups of 4 across 4 lanes and generated ~31-63 million reads per sample. All FASTQ files from all sequencing runs are located in the Small Read Archive (http://www.ncbi.nlm.nih.gov/sra), SRP035871, BioProject number PRJNA236472. Quality control for reads generated from sequencing was performed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Sequenced reads were aligned to the soybean reference genome (Gmax_189.fa, www.phytozome.net) using Bowtie [82]. All valid alignments per read were reported allowing up to three mismatches. Alignment summary statistics are presented in Additional file 3. Aligned sequence reads and a list of genomic features (Gmax_189_gene.gff3, www.phytozome.net) were input into HTSeq to generate read counts using the htseq-count and –m union options. These counts were then input into SAS (SAS Institute, Version 9.2, Cary, NC, USA) for normalization and statistical analysis. Genes with counts of 10 or less were removed from all subsequent statistical analyses. Read counts were normalized using the natural log of the upper quartile (ln_uq) [83],[84]. All count data can be found in Additional files 4 and 5. Differential gene expression was determined using a mixed effects linear model Yijkl = m + ti + γj + ρk + ɛijkl. Y is the normalized estimate of the expression for the fixed effect of condition (i = ozone/ambient), the random effect of block (j = 1,2,3,4) and the random effect of lane (k = 1,2,3,4). A log fold change represents the difference of the ln_uq normalized count data for elevated [O3] minus ambient [O3]. The assumptions of normality were tested using the Shapiro-Wilk test [85] for each gene. A multiple test correction was applied using the linear step-up method of [86]. Analyses were conducted in SAS (SAS Institute, Version 9.2, Cary, NC, USA).

Availability of supporting data

The data set supporting the results of this article are included within the article (and its additional files). Additionally, all FASTQ files from all sequencing runs are located in the Small Read Archive (http://www.ncbi.nlm.nih.gov/sra), SRP035871, BioProject number PRJNA236472. .

Additional files

Abbreviations

O3:

Ozone

[O3]:

Ozone concentration

ppb:

Part per billion

ROS:

Reactive oxygen species

PPFD:

Photosynthetic photon flux density

RH:

Relative humidity

ECM:

Extra-cellular matrix

MMP:

Matrix metalloproteinase

XTH:

Xyloglucan endotransglucosylase/hydrolase

References

  1. Van Dingenen R, Dentener FJ, Raes F, Krol MC, Emberson L, Cofala J: The global impact of ozone on agricultural crop yields under current and future air quality legislation. Atmos Environ. 2009, 43: 604-618. 10.1016/j.atmosenv.2008.10.033.

    Article  CAS  Google Scholar 

  2. Anenberg SC, Horowitz LW, Tong DQ, West JJ: An estimate of the global burden of anthropogenic ozone and fine particulate matter on premature human mortality using atmospheric modeling. Environ Health Persp. 2010, 118: 1189-1195. 10.1289/ehp.0901220.

    Article  CAS  Google Scholar 

  3. The Royal Society: Ground-level ozone in the 21st century: future trends, impacts and policy implications. In Sci Policy Rep 15/08, R. Soc. London; 2008:1–132.

    Google Scholar 

  4. Young PJ, Archibald AT, Bowman KW, Lamarque J-F, Naik V, Stevenson DS, Tilmes S, Voulgarakis A, Wild O, Bergmann D, Cameron-Smith P, Cionni I, Collins WJ, Dalsoren SB, Doherty RM, Eyring V, Faluvegi G, Horowitz LW, Josse B, Lee YH, MacKenzie IA, Nagashima T, Plummer DA, Righi M, Rumbold ST, Skeie RB, Shindell DT, Strode SA, Sudo K, Szopa S, et al: Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmos Chem Phys. 2013, 13: 2063-2090. 10.5194/acp-13-2063-2013.

    Article  CAS  Google Scholar 

  5. Emberson LD, Buker P, Ashmore MR, Mills G, Jackson LS, Agrawal M, Atikuzzaman MD, Cinderby S, Engardt M, Jamir C, Kobayashi K, Oanh NTK, Quadir QF, Wahid A: A comparison of North American and Asian exposure-response data for ozone effects on crop yields. Atmos Environ. 2009, 43: 1945-1953. 10.1016/j.atmosenv.2009.01.005.

    Article  CAS  Google Scholar 

  6. Kangasjarvi J, Jaspers P, Kollist H: Signalling and cell death in ozone-exposed plants. Plant Cell Environ. 2005, 28: 1021-1036. 10.1111/j.1365-3040.2005.01325.x.

    Article  CAS  Google Scholar 

  7. Ainsworth EA, Yendrek CR, Sitch S, Collins WJ, Emberson LD: The effects of tropospheric ozone on net primary productivity and implications for climate change. Annu Rev Plant Biol. 2012, 63: 637-661. 10.1146/annurev-arplant-042110-103829.

    Article  CAS  PubMed  Google Scholar 

  8. Wilkinson S, Mills G, Illidge R, Davies WJ: How is ozone pollution reducing our food supply?. J Exp Bot. 2012, 63: 527-536. 10.1093/jxb/err317.

    Article  CAS  PubMed  Google Scholar 

  9. Black VJ, Black CR, Roberts JA, Stewart CA: Impact of ozone on the reproductive development of plants. New Phytol. 2000, 147: 421-447. 10.1046/j.1469-8137.2000.00721.x.

    Article  CAS  Google Scholar 

  10. Ashmore MR: Assessing the future global impacts of ozone on vegetation. Plant Cell Environ. 2005, 28: 949-964. 10.1111/j.1365-3040.2005.01341.x.

    Article  CAS  Google Scholar 

  11. Leisner CP, Ainsworth EA: Quantifying the effects of ozone on plant reproductive growth and development. Global Change Biol. 2012, 18: 606-616. 10.1111/j.1365-2486.2011.02535.x.

    Article  Google Scholar 

  12. Stewart CA, Black VJ, Black CR, Roberts JA: Direct effects of ozone on the reproductive development of Brassica species. J Plant Physiol. 1996, 148: 172-178. 10.1016/S0176-1617(96)80311-7.

    Article  CAS  Google Scholar 

  13. Liu FL, Andersen MN, Jensen CR: Loss of pod set caused by drought stress is associated with water status and ABA content of reproductive structures in soybean. Funct Plant Biol. 2003, 30: 271-280. 10.1071/FP02185.

    Article  CAS  Google Scholar 

  14. van Schaik PH, Probst AH: Effects of some environmental factors on flower production and reproductive efficiency in soybeans. Agron J. 1958, 50: 192-197. 10.2134/agronj1958.00021962005000040007x.

    Article  Google Scholar 

  15. Hansen WR, Shibles R: Seasonal log of flowering and podding activity of field-grown soybeans. Agron J. 1978, 70: 47-50. 10.2134/agronj1978.00021962007000010012x.

    Article  Google Scholar 

  16. Wiebold WJ, Ashley DA, Boerma HR: Reproductive abscission levels and patterns for eleven determinate soybean cultivars. Agron J. 1981, 73: 43-46. 10.2134/agronj1981.00021962007300010010x.

    Article  Google Scholar 

  17. Huff A, Dybing CD: Factors affecting shedding of flowers in soybean (Glycine max (L.) Merrill). J Exp Bot. 1980, 31: 751-762. 10.1093/jxb/31.3.751.

    Article  CAS  Google Scholar 

  18. Heindl JC, Brun WA: Patterns of reproductive abscission, seed yield, and yield components in soybean. Crop Sci. 1984, 24: 542-545. 10.2135/cropsci1984.0011183X002400030027x.

    Article  Google Scholar 

  19. Brun WA, Betts KJ: Source/sink relations of abscising and nonabscising soybean flowers. Plant Physiol. 1984, 75: 187-191. 10.1104/pp.75.1.187.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Carlson DR, Dyer DJ, Cotterman CD, Durley RC: The physiological basis for cytokinin induced increases in pod set in IX93-100 soybeans. Plant Physiol. 1987, 84: 233-239. 10.1104/pp.84.2.233.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Nagel L, Brewster R, Riedell WE, Reese RN: Cytokinin regulation of flower and pod set in soybeans (Glycine max (L.) Merr.). Ann Bot London. 2001, 88: 27-31. 10.1006/anbo.2001.1423.

    Article  CAS  Google Scholar 

  22. Jiang HF, Egli DB: Shade induced changes in flower and pod number and flower and fruit abscission in soybean. Agron J. 1993, 85: 221-225. 10.2134/agronj1993.00021962008500020011x.

    Article  Google Scholar 

  23. Westgate ME, Peterson CM: Flower and pod development in water-deficient soybeans (Glycine max L. Merr.). J Exp Bot. 1993, 44: 109-117. 10.1093/jxb/44.1.109.

    Article  Google Scholar 

  24. Kokubun M, Shimada S, Takahashi M: Flower abortion caused by preanthesis water deficit is not attributed to impairment of pollen in soybean. Crop Sci. 2001, 41: 1517-1521. 10.2135/cropsci2001.4151517x.

    Article  Google Scholar 

  25. Cheng Y-Q, Liu J-F, Yang X, Ma R, Liu C, Liu Q: RNA-seq analysis reveals ethylene-mediated reproductive organ development and abscission in soybean (Glycine max L. Merr.). Plant Mol Biol Rep. 2013, 31: 607-619. 10.1007/s11105-012-0533-4.

    Article  CAS  Google Scholar 

  26. Tuomainen J, Betz C, Kangasjarvi J, Ernst D, Yin Z-H, Langebartels C, Sangermann H: Ozone induction of ethylene emission in tomato plants: regulation by differential accumulation of transcripts for the biosynthetic enzymes. Plant J. 1997, 12: 1151-1162. 10.1046/j.1365-313X.1997.12051151.x.

    Article  CAS  Google Scholar 

  27. Betzelberger AM, Yendrek CR, Sun J, Leisner CP, Nelson RL, Ort DR, Ainsworth EA: Ozone exposure response for U.S. soybean cultivars: Linear reductions in photosynthetic potential, biomass and yield. Plant Physiol. 2012, 160: 1827-1839. 10.1104/pp.112.205591.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Sharma YK, Davis KR: Ozone-induced expression of stress-related genes in Arabidopsis thaliana . Plant Physiol. 1994, 105: 1089-1096.

    PubMed Central  CAS  PubMed  Google Scholar 

  29. Sharma YK, Leon J, Raskin I, Davis KR: Ozone-induced responses in Arabidopsis thaliana: The role of salicylic acid in the accumulation of defense-related transcripts and induced resistance. Proc Natl Acad Sci U S A. 1996, 93: 5099-5104. 10.1073/pnas.93.10.5099.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Eckey-Kaltenbach H, Kiefer E, Grosskopf E, Ernst D, Sandermann H: Differential transcript induction of parsley pathogenesis-related proteins and of a small heat shock protein by ozone and heat shock. Plant Mol Biol. 1997, 33: 343-350. 10.1023/A:1005786317975.

    Article  CAS  PubMed  Google Scholar 

  31. Miller JD, Arteca RN, Pell EJ: Senescence-associated gene expression during ozone-induced leaf senescence in Arabidopsis. Plant Physiol. 1999, 120: 1015-1023. 10.1104/pp.120.4.1015.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Mahalingam R, Gomez-Buitrago A, Eckardt N, Shah N, Guevara-Garcia A, Day P, Raina R, Fedoroff NV: Characterizing the stress/defense transcriptome of Arabidopsis. Genome Biol. 2003, 4: R20-10.1186/gb-2003-4-3-r20.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Tamaoki M, Nakajima N, Kubo A, Aono M, Matsuyama T, Saji H: Transcriptome analysis of O3-exposed Arabidopsis reveals that multiple signal pathways act mutually antagonistically to induce gene expression. Plant Mol Biol. 2003, 53: 443-456. 10.1023/B:PLAN.0000019064.55734.52.

    Article  CAS  PubMed  Google Scholar 

  34. Baier M, Kandlbinder A, Golldack D, Dietz KJ: Oxidative stress and ozone: perception, signalling and response. Plant Cell Environ. 2005, 28: 1012-1020. 10.1111/j.1365-3040.2005.01326.x.

    Article  CAS  Google Scholar 

  35. Gillespie KM, Xu FX, Richter KT, McGrath JM, Markelz RJC, Ort DR, Leakey ADB, Ainsworth EA: Greater antioxidant and respiratory metabolism in field-grown soybean exposed to elevated O3 under both ambient and elevated CO2. Plant Cell Environ. 2012, 35: 169-184. 10.1111/j.1365-3040.2011.02427.x.

    Article  CAS  PubMed  Google Scholar 

  36. Andrews AD, Svec LV: Photosynthetic activity of soybean pods at different growth stages compared to leaves. Can J Plant Sci. 1975, 55: 501-505. 10.4141/cjps75-076.

    Article  CAS  Google Scholar 

  37. Horner HT, Healy RA, Cervantes-Martinez T, Palmer RG: Floral nectary fine structure and development in Glycine max L. (Fabaceae). Int J Plant Sci. 2003, 164: 675-690. 10.1086/377060.

    Article  Google Scholar 

  38. Mann JD, Jaworski EG: Comparison of stresses which may limit soybean yields. Crop Sci. 1970, 10: 620-624. 10.2135/cropsci1970.0011183X001000060003x.

    Article  Google Scholar 

  39. Sionit N, Kramer PJ: Effect of water stress during different stages of growth of soybean. Agron J. 1977, 69: 274-278. 10.2134/agronj1977.00021962006900020018x.

    Article  Google Scholar 

  40. Shiu SH, Bleecker AB: Receptor-like kinases from Arabidopsis form a monophyletic gene family related to animal receptor kinases. Proc Natl Acad Sci U S A. 2001, 98: 10763-10768. 10.1073/pnas.181141598.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Shiu SH, Bleecker AB: Expansion of the receptor-like kinase/Pelle gene family and receptor-like proteins in Arabidopsis. Plant Physiol. 2003, 132: 530-543. 10.1104/pp.103.021964.

    Article  CAS  PubMed  Google Scholar 

  42. Wrzaczek M, Brosche M, Salojarvi J, Kangasjarvi S, Idanheimo N, Mersmann S, Robatzek S, Karpinski S, Karpinska B, Kangasjarvi J: Transcriptional regulation of the CRK/DUF26 group of receptor-like protein kinases by ozone and plant hormones in Arabidopsis. BMC Plant Biol. 2010, 10: 95-10.1186/1471-2229-10-95.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Yin G, Xu H, Xiao S, Qin Y, Li Y, Yan Y, Hu Y: The large soybean (Glycine max) WRKY TF family expanded by segmental duplication events and subsequent divergent selection among subgroups. BMC Plant Biol. 2013, 13: 148-10.1186/1471-2229-13-148.

    Article  PubMed Central  PubMed  Google Scholar 

  44. Llorca CM, Potschin M, Zentgraf U: bZIPs and WRKYs: two large transcription factor families executing two different functional strategies. Front Plant Sci. 2014, 5: 169-10.3389/fpls.2014.00169.

    Article  PubMed Central  PubMed  Google Scholar 

  45. Olbrich M, Betz G, Gerstner E, Langebartels C, Sandermann H, Ernst D: Transcriptome analysis of ozone-responsive genes in leaves of European beech (Fagus sylvatica L.). Plant Biol. 2005, 7: 670-676. 10.1055/s-2005-873001.

    Article  CAS  PubMed  Google Scholar 

  46. Rizzo M, Bernardi R, Salvini M, Nali C, Lorenzini G, Durante M: Identification of differentially expressed genes induced by ozone stress in sensitive and tolerant poplar hybrids. J Plant Physiol. 2007, 164: 945-949. 10.1016/j.jplph.2006.07.012.

    Article  CAS  PubMed  Google Scholar 

  47. Pandey V, Haikio E, Lehesranta S, Koistinen K, Karenlampi S, Oksanen E: Proteomic analysis of two hybrid aspen clones subjected to long-term chronic ozone exposure in open field. Curr Proteomics. 2013, 10: 67-74. 10.2174/15701646112099990007.

    Article  CAS  Google Scholar 

  48. Pak JH, Liu CY, Huangpu J, Graham JS: Construction and characterization of the soybean leaf metalloproteinase cDNA. FEBS Lett. 1997, 404: 283-288. 10.1016/S0014-5793(97)00141-5.

    Article  CAS  PubMed  Google Scholar 

  49. Delorme VGR, McCabe PF, Kim DJ, Leaver CJ: A matrix metalloproteinase gene is expressed at the boundary of senescence and programmed cell death in cucumber. Plant Physiol. 2000, 123: 917-927. 10.1104/pp.123.3.917.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Liu YQ, Dammann C, Bhattacharyya MK: The matrix metalloproteinase gene GmMMP2 is activated in response to pathogenic infections in soybean. Plant Physiol. 2001, 127: 1788-1797. 10.1104/pp.010593.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Combier JP, Vernie T, de Billy F, El Yahyaoui F, Mathis R, Gamas P: The MtMMPL1 early nodulin is a novel member of the matrix metalloendoproteinase family with a role in Medicago truncatula infection by Sinorhizobium meliloti . Plant Physiol. 2007, 144: 703-716. 10.1104/pp.106.092585.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Jiang WP, Bond JS: Families of metalloendopeptidases and their relationships. FEBS Lett. 1992, 312: 110-114. 10.1016/0014-5793(92)80916-5.

    Article  CAS  PubMed  Google Scholar 

  53. Bode W, Gomisruth FX, Stockler W: Astacins, serralysins, snake-venom and matrix metalloproteinases exhibit identical zinc-binding environments (HEXXHXXGXXH and Met-Turn) and topologies and should be grouped into a common family, the metzincins. FEBS Lett. 1993, 331: 134-140. 10.1016/0014-5793(93)80312-I.

    Article  CAS  PubMed  Google Scholar 

  54. Hooper NM: Families of zinc metalloproteases. FEBS Lett. 1994, 354: 1-6. 10.1016/0014-5793(94)01079-X.

    Article  CAS  PubMed  Google Scholar 

  55. Graham JS, Xiong J, Gillikin JW: Purification and developmental analysis of a metalloendoproteinase from the leaves of Glycine max . Plant Physiol. 1991, 97: 786-792. 10.1104/pp.97.2.786.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. McGeehan G, Burkhart W, Anderegg R, Becherer JD, Gillikin JW, Graham JS: Sequencing and characterization of the soybean leaf metalloproteinase - structural and functional similarity to the matrix metalloproteinase family. Plant Physiol. 1992, 99: 1179-1183. 10.1104/pp.99.3.1179.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Rowsell S, Hawtin P, Minshull CA, Jepson H, Brockbank SMV, Barratt DG, Slater AM, McPheat WL, Waterson D, Henney AM, Pauptit RA: Crystal structure of human MMP9 in complex with a reverse hydroxamate inhibitor. J Mol Biol. 2002, 319: 173-181. 10.1016/S0022-2836(02)00262-0.

    Article  CAS  PubMed  Google Scholar 

  58. Ranieri A, Castagna A, Pacini J, Baldan B, Mensuali-Sodi A, Soldatini GF: Early production and scavenging of hydrogen peroxide in the apoplast of sunflower plants exposed to ozone. J Exp Bot. 2003, 54: 2529-2540. 10.1093/jxb/erg270.

    Article  CAS  PubMed  Google Scholar 

  59. Flinn BS: Plant extracellular matrix metalloproteinases. Funct Plant Biol. 2008, 35: 1183-1193. 10.1071/FP08182.

    Article  CAS  Google Scholar 

  60. Afzal AJ, Wood AJ, Lightfoot DA: Plant receptor-like serine threonine kinases: Roles in signaling and plant defense. Mol Plant Microbe. 2008, 21: 507-517. 10.1094/MPMI-21-5-0507.

    Article  CAS  Google Scholar 

  61. ten Hove CA, Bochdanovits Z, Jansweijer VMA, Koning FG, Berke L, Sanchez-Perez GF, Scheres B, Heidstra R: Probing the roles of LRR RLK genes in Arabidopsis thaliana roots using a custom T-DNA insertion set. Plant Mol Biol. 2011, 76: 69-83. 10.1007/s11103-011-9769-x.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  62. Greeff C, Roux M, Mundy J, Petersen M: Receptor-like kinase complexes in plant innate immunity. Front Plant Sci. 2012, 3: A209-

    Google Scholar 

  63. Tanaka H, Osakabe Y, Katsura S, Mizuno S, Maruyama K, Kusakabe K, Mizoi J, Shinozaki K, Yamaguchi-Shinozaki K: Abiotic stress-inducible receptor-like kinases negatively control ABA signaling in Arabidopsis. Plant J. 2012, 70: 599-613. 10.1111/j.1365-313X.2012.04901.x.

    Article  CAS  PubMed  Google Scholar 

  64. Zhang XJ, Yang GY, Shi R, Han XM, Qi LW, Wang RG, Xiong LM, Li GJ: Arabidopsis cysteine-rich receptor-like kinase 45 functions in the responses to abscisic acid and abiotic stresses. Plant Physiol Bioch. 2013, 67: 189-198. 10.1016/j.plaphy.2013.03.013.

    Article  CAS  Google Scholar 

  65. Yang L, Wu KC, Gao P, Liu XJ, Li GP, Wu ZJ: GsLRPK, a novel cold-activated leucine-rich repeat receptor-like protein kinase from Glycine soja, is a positive regulator to cold stress tolerance. Plant Sci. 2014, 215: 19-28. 10.1016/j.plantsci.2013.10.009.

    Article  PubMed  Google Scholar 

  66. Babosha AV: Inducible lectins and plant resistance to pathogens and abiotic stress. Biochem Mosc. 2008, 73: 812-825. 10.1134/S0006297908070109.

    Article  CAS  Google Scholar 

  67. Osakabe Y, Yamaguchi-Shinozaki K, Shinozaki K, Tran LSP: Sensing the environment: key roles of membrane-localized kinases in plant perception and response to abiotic stress. J Exp Bot. 2013, 64: 445-458. 10.1093/jxb/ers354.

    Article  CAS  PubMed  Google Scholar 

  68. Nishitani K: Implication of xyloglucan related protein (XRP) family in regulation of plant growth and development. Trends Glycosci Glycotech. 1997, 9: 233-234. 10.4052/tigg.9.233.

    Article  CAS  Google Scholar 

  69. Matsui A, Yokoyama R, Seki M, Ito T, Shinozaki K, Takahashi T, Komeda Y, Nishitani K: AtXTH27 plays an essential role in cell wall modification during the development of tracheary elements. Plant J. 2005, 42: 525-534. 10.1111/j.1365-313X.2005.02395.x.

    Article  CAS  PubMed  Google Scholar 

  70. Jimenez T, Martin I, Labrador E, Dopico B: The immunolocation of xyloglucan endotransglucosylase/hydrolase specific to elongating tissues in Cicer arietinum suggests a role in the elongation of vascular cells. J Exp Bot. 2006, 57: 3979-3988. 10.1093/jxb/erl169.

    Article  CAS  PubMed  Google Scholar 

  71. Romo S, Jimenez T, Labrador E, Dopico B: The gene for a xyloglucan endotransglucosylase/hydrolase from Cicer arietinum is strongly expressed in elongating tissues. Plant Physiol Biochem. 2005, 43: 169-176. 10.1016/j.plaphy.2005.01.014.

    Article  CAS  PubMed  Google Scholar 

  72. Hyodo H, Yamakawa S, Takeda Y, Tsuduki M, Yokota A, Nishitani K, Kohchi T: Active gene expression of a xyloglucan endotransglucosylase/hydrolase gene, XTH9, in inflorescence apices is related to cell elongation in Arabidopsis thaliana . Plant Mol Biol. 2003, 52: 473-482. 10.1023/A:1023904217641.

    Article  CAS  PubMed  Google Scholar 

  73. Liu YB, Lu SM, Zhang JF, Liu S, Lu YT: A xyloglucan endotransglucosylase/ hydrolase involves in growth of primary root and alters the deposition of cellulose in Arabidopsis. Planta. 2007, 226: 1547-1560. 10.1007/s00425-007-0591-2.

    Article  CAS  PubMed  Google Scholar 

  74. Malinowski R, Filipecki M, Tagashira N, Wisniewska A, Gaj P, Plader W, Malepszy S: Xyloglucan endotransglucosylase/hydrolase genes in cucumber (Cucumis sativus) - differential expression during somatic embryogenesis. Physiol Plant. 2004, 120: 678-685. 10.1111/j.0031-9317.2004.0289.x.

    Article  CAS  PubMed  Google Scholar 

  75. Singh AP, Tripathi SK, Nath P, Sane AP: Petal abscission in rose is associated with the differential expression of two ethylene-responsive xyloglucan endotransglucosylase/ hydrolase genes, RbXTH1 and RbXTH2. J Exp Bot. 2011, 62: 5091-5103. 10.1093/jxb/err209.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  76. Kumpf RP, Shi CL, Larrieu A, Sto IM, Butenko MA, Peret B, Riiser ES, Bennett MJ, Aalen RB: Floral organ abscission peptide IDA and its HAE/HSL2 receptors control cell separation during lateral root emergence. Proc Natl Acad Sci U S A. 2013, 110: 5235-5240. 10.1073/pnas.1210835110.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Jinn TL, Stone JM, Walker JC: HAESA, an Arabidopsis leucine-rich repeat receptor kinase, controls floral organ abscission. Gene Dev. 2000, 14: 108-117.

    PubMed Central  CAS  PubMed  Google Scholar 

  78. Olbrich M, Gerstner E, Welzl G, Winkler JB, Ernst D: Transcript responses in leaves of ozone-treated beech saplings seasons at an outdoor free air model fumigation site over two growing seasons. Plant Soil. 2009, 323: 61-74. 10.1007/s11104-009-0129-4.

    Article  CAS  Google Scholar 

  79. Wu YJ, Jeong BR, Fry SC, Boyer JS: Change in XET activities, cell wall extensibility and hypocotyl elongation of soybean seedlings at low water potential. Planta. 2005, 220: 593-601. 10.1007/s00425-004-1369-4.

    Article  CAS  PubMed  Google Scholar 

  80. Broekgaarden C, Poelman EH, Steenhuis G, Voorrips RE, Dicke M, Vosman B: Responses of Brassica oleracea cultivars to infestation by the aphid Brevicoryne brassicae: an ecological and molecular approach. Plant Cell Environ. 2008, 31: 1592-1605. 10.1111/j.1365-3040.2008.01871.x.

    Article  CAS  PubMed  Google Scholar 

  81. Hewitt EJ: Sand and water culture methods used in the study of plant nutrition. 1952, Commonwealth Agricultural Bureau, London

    Google Scholar 

  82. Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25-10.1186/gb-2009-10-3-r25.

    Article  PubMed Central  PubMed  Google Scholar 

  83. Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010, 11: 94-10.1186/1471-2105-11-94.

    Article  PubMed Central  PubMed  Google Scholar 

  84. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Guedj M, Jaffrézic F: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013, 14: 671-683. 10.1093/bib/bbs046.

    Article  CAS  PubMed  Google Scholar 

  85. Shapiro SS, Wilk MB: An analysis of variance test for normality (complete samples). Biometrika. 1965, 52: 591-611. 10.1093/biomet/52.3-4.591.

    Article  Google Scholar 

  86. Benjamini Y, Hochberg Y: Controlling the false discovery rate – a practical and powerful approach to multiple testing. J Royal Stat Soc Series B Method. 1995, 57: 289-300.

    Google Scholar 

  87. Petersen TN, Brunak S, von Heijne G, Nielsen H: SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011, 8: 785-786. 10.1038/nmeth.1701.

    Article  CAS  PubMed  Google Scholar 

  88. Simossis VA, Heringa J: PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information. Nucleic Acids Res. 2005, 33: 289-294. 10.1093/nar/gki390.

    Article  Google Scholar 

  89. Combet C, Blanchet C, Geourjon C, Deleage G: NPS@: Network protein sequence analysis. Trends Biochem Sci. 2000, 25: 147-150. 10.1016/S0968-0004(99)01540-6.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge Robert Van Buren for help with cDNA library preparation for Illumina sequencing. This work was supported by a grant to EAA from the USDA NIFA (Grant No. 2010-65114-20355) and to RM from the UIUC Campus Research Board (Award No. 12265).

Author information

Authors and Affiliations

  1. Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA

    Courtney P Leisner, Ray Ming & Elizabeth A Ainsworth

  2. USDA ARS Global Change and Photosynthesis Research Unit, 1201 W. Gregory Drive, Urbana, IL, 61801, USA

    Elizabeth A Ainsworth

Authors
  1. Courtney P Leisner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ray Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elizabeth A Ainsworth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elizabeth A Ainsworth.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

CPL collected field data from SoyFACE, designed and did the growth chamber experiment, constructed the cDNA libraries for RNA-Seq, completed bioinformatics and statistical analysis on the sequencing data, and wrote the manuscript. RM participated in design of the experiments and provided technical support in the cDNA library preparation. EAA designed the experiments, provided critical input in the transcriptomic analysis and wrote the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

12870_2014_335_MOESM1_ESM.pdf

Additional file 1: Domain analysis of putative MMP genes found in flower tissue. The general structure (domain analysis) of all members of the Arabidopsis MMP family (At1-MMP to At5-MMP), and the two known soybean MMPs (SMEP1 and GmMMP2) was found in [59]. Domain analysis of the putative MMP genes found in flower tissue in our dataset was also completed to compare with known MMP genes. The protein sequence for each gene in our data set was determined using the Phytozome database (http://www.phytozome.net/) and the amino acid length and the presence of a signal peptide, transmembrane and catalytic domain was analyzed using InterPro (https://www.ebi.ac.uk/interpro/). The signal peptide cleavage site and C-terminal transmembrane domain were also analyzed using the predictive software program SignalP 4.1 (http://www.cbs.dtu.dk/services/SignalP/) [87] and Localizome (http://localodom.kobic.re.kr/LocaloDom/), respectively. The presence of a furin cleavage site was analyzed using the predictive software ProP 1.0 (http://www.cbs.dtu.dk/services/ProP/) and the presence of a GPI anchor domain was analyzed using the predictive software big-PI Plant Predictor (http://mendel.imp.ac.at/gpi/plant_server.html). The cysteine switch and zinc-binding motifs of putative soybean flower MMP genes were determined using sequence alignment with known Arabidopsis and soybean MMP genes and generated using PRALINE (http://www.ibi.vu.nl/programs/pralinewww/) [88]. Percent identity of amino acid sequence analysis was performed Network Protein Sequence Analysis (http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_server.html) [89]. Modification sites (signal cleavage and GPI-anchor) are predicted to occur between the given locations of the residues in the amino acid sequence shown in the table. The domain of the GPI-anchor modification is also given and predicted to occur at one of the two bolded and underlined residues. The putative soybean MMP gene Glyma02g03301 has two cysteine switch motifs and two zinc-binding motifs, which is indicated in the table. (PDF 16 KB)

12870_2014_335_MOESM2_ESM.pdf

Additional file 2: Gene ontology (GO) term enrichment of biological processes in pod tissue only. GO term enrichment performed using single enrichment analysis (SEA) tool on AgriGo (http://bioinfo.cau.edu.cn/agriGO/). (PDF 197 KB)

12870_2014_335_MOESM3_ESM.pdf

Additional file 3: Summary statistics for each FASTQ file aligned to the soybean reference genome. The alignment statistics generated from Bowtie are presented in this table. (PDF 14 KB)

12870_2014_335_MOESM4_ESM.csv

Additional file 4: Differentially expressed genes in flower tissue, including the fold change in elevated [O 3 ], the FDR-adjusted p -value and a description of the functional category. (CSV 228 KB)

12870_2014_335_MOESM5_ESM.csv

Additional file 5: Differentially expressed genes in pod tissue, including the fold change in elevated [O 3 ], the FDR-adjusted p -value and a description of the functional category. (CSV 68 KB)

Authors’ original submitted files for images

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leisner, C.P., Ming, R. & Ainsworth, E.A. Distinct transcriptional profiles of ozone stress in soybean (Glycine max) flowers and pods. BMC Plant Biol 14, 335 (2014). https://doi.org/10.1186/s12870-014-0335-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-014-0335-y

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us