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Ragweed (Ambrosia artemisiifolia) pollen allergenicity: SuperSAGE transcriptomic analysis upon elevated CO2 and drought stress



Pollen of common ragweed (Ambrosia artemisiifolia) is a main cause of allergic diseases in Northern America. The weed has recently become spreading as a neophyte in Europe, while climate change may also affect the growth of the plant and additionally may also influence pollen allergenicity. To gain better insight in the molecular mechanisms in the development of ragweed pollen and its allergenic proteins under global change scenarios, we generated SuperSAGE libraries to identify differentially expressed transcripts.


Ragweed plants were grown in a greenhouse under 380 ppm CO2 and under elevated level of CO2 (700 ppm). In addition, drought experiments under both CO2 concentrations were performed. The pollen viability was not altered under elevated CO2, whereas drought stress decreased its viability. Increased levels of individual flavonoid metabolites were found under elevated CO2 and/or drought. Total RNA was isolated from ragweed pollen, exposed to the four mentioned scenarios and four SuperSAGE libraries were constructed. The library dataset included 236,942 unique sequences, showing overlapping as well as clear differently expressed sequence tags (ESTs). The analysis targeted ESTs known in Ambrosia, as well as in pollen of other plants. Among the identified ESTs, those encoding allergenic ragweed proteins (Amb a) increased under elevated CO2 and drought stress. In addition, ESTs encoding allergenic proteins in other plants were also identified.


The analysis of changes in the transcriptome of ragweed pollen upon CO2 and drought stress using SuperSAGE indicates that under global change scenarios the pollen transcriptome was altered, and impacts the allergenic potential of ragweed pollen.


Pollen of the common ragweed (Ambrosia artemisiifolia) is a main cause of allergic diseases in Northern America [1, 2]. This species is the most widespread Ambrosia and the weed has become spreading as a neophyte in Europe, and will become a serious health problem in sensitized populations [3]. The distribution of ragweed in Europe began approximately 100 years ago and is currently primarily found in the Rhône valley, Hungary, Croatia, Bulgaria, Northern Italy and Eastern Austria, but it is also spreading in Germany [4, 5] (

So far, the allergenic proteins of ragweed can be arranged into six biological groups [3, 6]. Approximately 48 allergenic proteins are known for the genus Ambrosia, and 32 proteins, including multiple isoforms, are known for A. artemisiifolia ( The major allergen of ragweed is Amb a 1, an acidic non-glycosylated 38-kDa protein consisting of a 26-kDa α-chain and an associated 12-kDa β-chain [3].

It is hypothesized that climate change and air pollution will affect the allergenic potential of pollen, either by a changed pollen season, by a changed pollen amount, by changes of the surface exine or by directly increasing the allergenic transcripts and proteins and interactions with biologically important ligands, e.g., flavonoids [2, 711]. Studies on effects on climate change on respiratory allergy are still lacking and only a few epidemiological reports on urbanization and air-pollution on pollen allergenicity are available [12]. An overview for risk factors on allergic disease discussing genetics aspects, indoor and outdoor pollution, socio-economic factors, climate change and migration has recently been published [12]. The proteomic profiling of birch pollen isolated from different sites indicated differences between allergenic and non-allergenic proteins [13]. In contrast, birch pollen isolated from an urban and rural site showed no difference in allergenic protein expression, indicating that allergenicity is determined by additional allergen carriers [14]. An in vivo study on birch pollen also sampled from different sites could correlate elevated ozone levels to higher allergenicity as well as to an increased allergen content [15]. It was recently shown that twice ambient ozone levels resulted in an increased content of allergenic proteins in two rye cultivars [16]. In ragweed, elevated ozone fumigation resulted in a changed transcriptional profile, including transcripts for allergenic proteins [17]. Elevated CO2 concentrations showed an increase growth of ragweed and its pollen production [1821], and an increased content of Amb a 1 allergen was observed [22].

In addition to increasing CO2 concentrations, future atmospheric warming, as well as hot and dry summer periods are also expected [23, 24]; IPPC Report 2007. Regulatory networks in cellular responses to drought, including abscisic acid-dependent and -independent systems, are well known during plant growth and development [2530]. Regarding transcriptomic and proteomic analyses of pollen, literature reports have focused on different developmental stages of pollen, mature pollen and pollen germination [3136]. Regarding temperature effects, differentially cold-regulated genes were detected in mature pollen of Arabidopsis thaliana[37].

Flavonoids are ubiquitous plant secondary metabolites and are important in plant development and reproduction, as well as in protection against abiotic and biotic stress factors [38, 39]. The yellow color of pollen can be traced back to flavonoids, thus shielding the pollen genome from UV-B radiation [40]. In addition, flavonoids play a role in male fertility, and quercetin is an important germination-inducing compound in maize and petunia but not in Arabidopsis or parsley [41, 42]. Flavonoids may be involved in the modulation of immune responses and thus may also be important in the allergenic response of pollen [43, 44]. In human health, IgE-binding of allergens may be influenced by attached flavonoids [45, 46]. The pathogenesis-related proteins (PRs) consist of a large group of homologous proteins in different plant species and many PRs are expressed in pollen and can act as allergens [47]. A direct interaction of birch PR-10c with biologically important molecules, including flavonoids, was shown by Koistinen et al. [48]. Similarly, flavonoids bind to the major birch allergen Bet v 1 [9], which also belongs to the PR-10 family [49]. Recently it was shown, that a quercetin derivative directly binds to the C-terminal helix of Bet v 1, and that this binding plays an important role during the inflammation response [50]. These results indicate that, in addition to allergenic proteins, additional allergenic carriers may also be involved in pollen allergenicity, which is not exclusively triggered by known allergenic proteins [14, 51, 52].

These studies suggest that global change will affect the allergenic potential of pollen and play a role in human health diseases related to allergic rhinitis and asthma. From this perspective, a transcriptome-wide analysis of the highly allergic pollen of ragweed would not only help in understanding climate impact on expressed pollen transcripts but also gain a deeper insight into the expected changes of pollen allergens. Flavonoids analysis will allow a better understanding of their possible function as additional allergenic carriers and also contribute to the relevant UV-B-absorbing metabolites of pollen. In a previous study, we showed that twice the ambient level of ozone resulted in a changed transcriptional profile of ragweed pollen, including encoded allergenic proteins [17]. In this study, we modified the global climate change approach by linking the transcriptional network changes of ragweed pollen to elevated CO2 concentrations and an extreme drought event. We highlight that the global change scenarios will affect the transcriptome of pollen and will also increase the abundance of allergen-related transcripts relevant for human health.

Results and discussion

Morphological parameters and pollen viability

Two main different leaf morphologies between the plants were observed: plants with strong pinnate leaves (i) and plants with only weak pinnate leaves (ii), as has been reported for ragweed with the same genetic background in exposure chambers [21].

Pollen viability was slightly reduced under elevated CO2 levels; however, this result was not statistically significant (Additional file 1). Similarly, it was shown that the pollen performance decreased in Raphanus sativus in response to elevated CO2 levels [53]. Drought stress resulted in a reduction of the pollen viability from approximately 46% to 24% (Additional file 1). The decreased pollen viability under drought stress is in accordance with several literature reports also demonstrating a reduced viability and pollen grain production [5457]. Interestingly, this drought effect could be partially mitigated by elevated CO2 with a slight increase from 24% to 30% (Additional file 1), indicating no additive effects of elevated CO2 and drought.

Secondary metabolites

Typical reverse-phase high-performance liquid chromatography (RP-HPLC) diagrams for water soluble metabolite extracts revealed 17 compounds, with the highest amounts in particular for metabolite 12 and 17, both are quercetin derivatives and methanolic extracts showed 12 different metabolites, congruent to data given by Kanter et al. [17] (Additional file 2). The total amounts of individual compounds for the final harvest are given in Figure 1. No significant changes could be observed between the control, elevated CO2, drought and elevated CO2 plus drought samples, similar to what has been described for ozone-treated pollen. However, individual metabolites of the PBS extract showed increased levels upon drought stress at both CO2 concentrations (Figure 1a; DA1, DA3, DA5, DA10, DA13 (quercetin derivative) and DA16 (kaempferol derivative). This change in individual metabolites is in contrast to pollen of ozone-fumigated ragweed that showed no change of such individual metabolites. Flavonoids have been shown to accumulate under drought stress in several plants, thus playing a physiological role in water tolerance and protection against oxidative stress [5860]. Moreover, detailed analyses showed that the level of quercetin derivatives also increased upon drought stress in different plants [6062], clearly indicating that in pollen of drought-stressed plants, the accumulation of individual flavonoid metabolites may play a protective role against oxidative stress and damage of the pollen tissue. Elevated CO2 resulted in increased levels of flavonoid metabolites in several plant species [6365]. In ragweed pollen, the metabolite level was approximately at the same levels under drought, irrespectively of the CO2 concentration (Figure 1). Thus, drought might be more important than elevated CO2 in increasing the levels of these individual metabolites. A single metabolite (DA 5) was also increased under CO2 treatment alone (Figure 1), similar to the impact of CO2 in soybean, where the concentration of only one flavonoid, a quercetin glycoside, was also increased [66]. This result indicates species-specific CO2 responses in flavonoid content and composition [67, 68].

Figure 1

Amount of PBS-soluble (a) and methanolic-extractable (b) phenolic metabolites in ragweed pollen. The separation was performed by RP-HPLC. The bars (N = 5) indicate SD and significant differences are indicated by an asterisk.

SuperSAGE dataset

The number of sequenced tags ranged from approximately 4.5 × 106 to 17.2 × 106 in the four libraries (Additional file 3, Info). The tag frequencies are given in Additional file 3 (All_Libs20101207). The SuperSAGE dataset included 236,942 different non redundant sequences (tags) of 26 bp in length (Additional file 3, All_Libs20101207). For each of these sequences (tag), the tag amounts are provided and count how often a unique sequence was found in each of the four libraries. One sequence (tag) can be found in one, two, three or all four of the libraries, as indicated in the overlapping regions in Figure 2a but, according to the transcript expression, in different quantities (tag amounts). The sequenced tag counts for each unique sequence in all of the libraries ranged from ≤ 50 (low), 50–500 (mid), 500–5000 (high) and ≥ 5000 (very high) (Table 1). The normalized values of each tag in relation to 106 tags (tpm) for each library resulted in approximately 99.5% of low- and mid-abundant unique tags, while high- and very high-abundant tags represented only approximately 0.2% - 0.4% (Table 1). A similar distribution of abundant classes has also been reported for other SuperSAGE libraries [6971]. The four libraries had approximately the same unique sequences for the very high-abundant class (31–37), the high abundant class (239–270) and the mid-abundant class (863–1129). In contrast, the low-abundant class was more variable, reflecting also the total number of unique sequences of each library (Table 1). According to the cumulative frequency distribution, only those tpm values greater than 0.6 to 0.8 can be considered expressed [72] (Additional file 4). Therefore, transcripts with a tpm threshold < 0.8 were eliminated, resulting in more stringent values, coming up with 40,221 unique sequences (Figure 2b). Finally, we eliminated all of the sequences with the description ‘no hits’ and the score of the BLAST hit was set to ≥ 40. These parameters resulted then in 9,078 unique sequences and an equal distribution in all 4 of the libraries (Figure 2c). The low-abundance sequences were strongly reduced in all of the libraries to approximately 90.0%, whereas those sequences in the mid- and high-abundant groups strongly increased up to 10% (Table 2, Figure 3). Additionally, MapMan was used to group the SuperSAGE tags into several functional categories (BIN-codes) [73]. For this grouping, the SuperSAGE tags were matched to Ambrosia 454-transcriptome data (contigs + singletons) [17]. The data were then BLASTed against Arabidopsis (TAIR) to identify Arabidopsis homologues, which then could be sorted to the BIN-codes (workflow: Additional file 5) and only log2-fold changes of at least 1.5 were further examined (Additional file 6). Interestingly, elevated CO2 + drought conditions resulted in higher log2-fold changes compared to the single treatments, indicating additive effects. Transcripts with homologies to abiotic stress were mainly up-regulated under all three scenarios, including also dehydration-responsive transcripts, heat-shock proteins and chaperones. Regarding drought stress, this result is not surprising and has also been reported in the literature [26, 30, 74, 75]. For the BIN-name cell wall, a pectate lyase family member and expansin were clearly up-regulated. Pectate lyases are important for pollen tube growth by pectin degradation. However, in ragweed pollen, pectate lyases belong to the major allergen Amb a 1 family (AllFam database; Expansins are important for the pollen tube and for cell wall changes and confer drought tolerance [76, 77]. Moreover, expansins also belong to pollen allergens (AllFam database). The most strongly up-regulated transcript (CER1) in all three of the treatments is involved in wax biosynthesis (log2-fold 5.3 - 9.2). CER1 is mainly expressed in inflorescences and siliques and is induced by osmotic stress [78]. This result demonstrates that wax biosynthesis is enhanced under climate change scenarios.

Figure 2

Venn diagram. Number of common and unique SuperSAGE sequence tags. For each sequence, the tag amount in the individual samples was analyzed. Sequences with ≥ 1 appearances in two, three or all of the samples are shown by individual overlapping regions. The total number of sequence tags per library is indicated. a reflects the distribution of sequence tags in the original dataset. b gives the distribution of sequenced tags filtered for tpm > 0.8. c indicates the sequence tag distribution for a stringently filtered dataset with the following criteria: tpm > 0.8; score of BLAST hit > 40; and removal of sequence tags without BLAST result (“no hit”).

Table 1 Distribution of low- to very high-abundant sequences detected in the four SuperSAGE libraries from the control (380 ppm CO 2 ), CO 2 (700 ppm CO 2 ), CO 2 plus drought and drought
Table 2 Distribution of low-abundant sequences found uniquely under control conditions (380 ppm CO 2 ), under elevated CO 2 (700 ppm CO 2 ), under elevated CO 2 plus drought and drought (380 ppm CO 2 ), or found to be common in all four SuperSAGE libraries at one time
Figure 3

Distribution of low- to very high-abundant sequence tags. The tags were found uniquely under control conditions (380 ppm CO2), under elevated CO2 (700 ppm CO2), under CO2 plus drought or under drought, or found to be common in all four libraries at one time. The data were analyzed for the different filter criteria indicated in the graph.

We also performed a pairwise comparison of the libraries according to possible global change scenarios: control vs. drought (AmK vs. AmT), control vs. elevated CO2 (AmK vs. AmC) and control vs. CO2 + drought (AmK vs. AmCT). Using the STDGE2GO-Tool kit, we first searched the AmK vs. the AmT library for the following parameters: Ambrosia, ragweed, pollen, extensin, exine, intine, cell wall, coat and allergen. Using the term Ambrosia, 50 differentially expressed genes were identified that were mainly related to an Ambrosia trifida pollen cDNA library. All of the genes with a clear homologue and not only the description pollen cDNA library are listed in Table 3. The term ragweed resulted in 48 differentially expressed genes that were also found in the Ambrosia search. The search term “pollen” showed 48 hits that were primarily related to an Ambrosia trifida pollen cDNA library and thus also present in the Ambrosia search. For pollen, we also carried out a search for exine, intine, extensin, coat and cell wall. However, no additional hits were found. Searching for “allergen” identified 4 Ambrosia genes, a calcium-binding protein isoallergen 1, Amb a 1.1, Amb a 1.2 and Amb a 1.3 that were all up-regulated under drought stress (Table 3). In total, we could identify eight transcripts for allergenic proteins from A. artemisiifolia: two calcium-binding proteins (EF hand domain, Amb a 9 and Amb a 10), pectate lyases (Amb a 1.1, Amb a 1.2, Amb a 1.3 and Amb a 1.2 precursor), an actin-binding protein (profilin-like) and a cystatin proteinase inhibitor (Amb a CPI) (shown bold in Table 3). Except for the transcript of the Amb a 1.2 precursor protein, all of the other transcripts were up-regulated under drought. However, four of these transcripts were below the threshold of 1.5-fold (log2 = 0.59). The transcript for a homologue of a down-regulated ABA-responsive HVA22 protein from A. trifida was found in very high abundance (more than 10,000 tpm). In vegetative tissue, the HVA22 genes are expressed in different tissues and show high levels of expression in flowers and inflorescences [79]. Drought stress suppressed HVA22a and HVA22c expression, had little effect on HVA22e expression and enhanced HVA22d expression in the inflorescent stems of Arabidopsis[79]. No changes or only small effects could be observed in the flower buds, except for a slight enhancement of expression under drought stress [79]. In accordance with our results, this result indicates that in addition to stress, the HVA22 genes may also be important for the reproduction of plants. A homologue for a putative pollen-specific transcript from A. trifida was also found in high abundance and was down-regulated by drought. Other pollen-specific sequences were homologues to a pistil- and pollen-expressed gene from sunflower (SF21), a pollen coat protein transcript from wild cabbage and a pollen-specific actin-depolymerising factor from tobacco that were both down-regulated. SF21 belongs to a gene family expressed in pollen and pistil in angiosperms and the encoded protein is important for pollen-pistil interactions [80, 81]; however, the molecular function is still unknown. A search with the term “drought” resulted in 38 transcripts to homologues of a drought-stress subtracted cDNA library of safflower, also belonging to the Asteraceae and 35 of these cDNAs were up-regulated in drought-stressed ragweed pollen. Among these cDNAs, homologues to a carbonic anhydrase 3, a cyclophilin and a plastocyanin, proteins that are known to be allergenic, showed highly up-regulated transcripts (AllFam database; ( Interestingly, a highly up-regulated transcript for a CBS (cystathionine β-synthase) domain-containing protein homologue of A. trifida was detected (log2 = 9.01). CBS domain-containing proteins can sense cell energy levels and regulate redox homeostasis [82, 83]. These proteins are important for stress regulation and corresponding genes are up-regulated upon drought stress [84].

Table 3 Up- and down-regulated transcripts in pollen of ragweed from control and drought stressed plants

Next, we searched the AllFam database of allergen families, restricted to plants and inhalation. This search included 59 allergen families with 233 allergens ( In this search, the p-value was set to < E−10, except for safflower, which belongs also to the Asteraceae. In addition to the known allergens found under the Ambrosia search, eight transcripts for putative allergenic proteins from other plants according to the Allfam database were identified (Table 3). Seven of these transcripts were clearly up-regulated under drought, at least by a three-fold log2 change. In contrast, a homologue to an aspartic proteinase precursor from maize was down-regulated. The highest abundances were seen for the transcripts homologous to a profilin of rubber tree, an acidic chitinase of jelly fig and a safflower carbonic anhydrase. As pathogenesis-related (PR) proteins are known to be allergenic, we also searched for this term, coming up with a single hit for PR 5–1 homologue of sunflower, which was up-regulated under drought (Table 3). However, it is important to note that the abundances of all of these transcripts are low as compared to the ‘Amb a’ abundances in ragweed pollen.

The search of the AmK vs. the AmC library was performed for the terms given above. Under elevated CO2 concentration, the term Ambrosia resulted in 62 differentially regulated transcripts that were also mainly related to an A. trifida pollen cDNA library and the specified homologues are given in Tables 4 and 5. A search for ragweed resulted in 57 transcripts that were already present in the Ambrosia search. Under the search for allergen, five genes of A. artemisiifolia were identified: Amb a 1.1, Amb a 1.2, Amb a 1.3 and calcium-binding protein isoallergen 1 were up-regulated under elevated CO2, while the low-abundant profilin isoallergen 1 was down-regulated (Tables 4 and 5). This increase in Amb a 1 transcripts is in accordance with an increased level of Amb a 1 protein content in ragweed pollen grown under increased CO2 concentrations [22]. In total, nine transcripts for allergenic proteins from A. artemisiifolia were identified: two calcium-binding proteins (Amb a 9, Amb a 10), pectate lyases (Amb a 1.1, Amb a 1.2, Amb a 1.3 and Amb a 1.2 precursor protein), a cystatin proteinase inhibitor (Amb a CPI), a profilin allergen (Amb a 8.1) (shown bold in Tables 4 and 5). Seven of these transcripts were up-regulated and two were down-regulated (Amb a CPI and Amb a 8.1) under elevated CO2. However, for two transcripts, the log2 fold change was below the threshold (Amb a CPI and Amb a 1.2 precursor). Although at low abundance, the transcript homologous to a lipid transfer protein (LTP) from A. trifida was highly up-regulated (log2 = 9.2) under elevated CO2. LTPs are basic proteins that are abundant in higher plants [85]. These proteins belong to the prolamin superfamily and their role in allergenicity has been reviewed recently [86]. Similar to the drought library, the homologue for an abscisic acid-responsive HVA22 transcript of A. trifida was found in high abundance and was down-regulated under elevated CO2 concentrations (Table 4). The transcript for the homologue of a putative pollen-specific protein from A. trifida was present in very high abundance and was slightly down-regulated under the elevated CO2 regime (Table 4). In contrast, the transcript for the pollen-specific protein SF21 homolog from sunflower was clearly up-regulated (Table 5). Other up-regulated pollen proteins included transcripts for a homologue of a pollen tube protein from tobacco and a pistil-specific extensin-like protein from safflower, while the transcript for a homologue of a pollen coat protein from wild cabbage was down-regulated. However, this value was below the threshold. Although not directly linked to pollen, the transcript for a homologue of a seed coat protein from rapeseed was extremely up-regulated (log2 = 12.99) (Table 5). The general search for pollen showed 59 transcripts and 56 out of these transcripts were from the pollen cDNA of A. trifida. Other highly regulated transcripts of Ambrosia included a ribokinase (log2 = −8.68) and a ribosomal protein L36 (log2 = 9.28).

Table 4 Up- and down-regulated transcripts in pollen of ragweed from 380ppm CO 2 (control) and 700ppm CO 2 concentrations filtered for the terms Ambrosia , ragweed, pollen, extensin, exine, intine, cell wall, coat, allergen and the Allfam database
Table 5 Up- and down-regulated transcripts in pollen of ragweed from 380ppm CO 2 (control) and 700ppm CO 2 concentrations filtered for the terms Ambrosia , ragweed, pollen, extensin, exine, intine, cell wall, coat, allergen and the Allfam database

The AllFam database search indicated seven transcripts for putative allergenic proteins from other plants. Five of these proteins were up-regulated under elevated CO2 concentrations, whereas the transcripts of a protein kinase and an aspartic proteinase were down-regulated, similar as under drought stress (Table 5). Interestingly, the transcript of a homologue for a non-specific lipid-transfer protein of red sage was also strongly up-regulated, although at low abundance. As described for the drought stress conditions, the transcript level of PR 5–1 homologue from sunflower was also elevated (Table 5).

In a final step, we compared the Ambrosia control library (AmK) vs. the elevated CO2 + drought-stressed library (AmCT). Under the search term Ambrosia, 55 transcripts and for ragweed 50 transcripts, mainly homologues from A. trifida, were identified. The homology description is given in Table 6. The search term “allergen” resulted in five trancripts from A. artemisiifolia and the calcium-binding protein isoallergen 1, Amb a 1.1, Amb a 1.2 and Amb a 1.3 were up-regulated (Table 6). In total, eight transcripts of up-regulated allergenic proteins were identified for A. artemisiifolia: two calcium-binding proteins (Amb a 9 and Amb a10), pectate lyases (Amb a 1.1, Amb a 1.2, Amb a 1.3 and Amb a 1.2 precursor), a profilin-like protein (Amb a 8) and a cystatin proteinase inhibitor (Amb a CPI). However, the change of Amb a 1.2 precursor and Amb a CPI were below the threshold of 1.5. An LTP homologue from A. trifida was highly up-regulated (Table 6). The transcript of a low-abundance aspartic protease homologue from A. trifida, allergenic according to the AllFam database, was highly up-regulated (Table 6). The transcript of the very high abundant pollen-specific protein homologue from A. trifida was slightly down-regulated, similar to the other two libraries, while the transcript of the pollen-specific protein SF21 homologue from sunflower was up-regulated (Table 6). The transcript of a pollen coat protein homologue from wild cabbage was slightly down-regulated and the seed coat protein transcripts homologous to the one from rapeseed was extremely highly up-regulated (log2 = 14.71) (Table 6). The general search for pollen resulted in 51 transcripts that were mainly related to the A. trifida pollen cDNA library. The search input drought resulted in 33 differentially regulated transcripts with homology to a safflower drought stress-subtracted library and 25 of these transcripts were up-regulated. The homologue of an ABA-responsive HVA22 transcript from A. trifida was down-regulated, as in the other two libraries. Although at low abundance, the transcript for the CBS domain-containing protein was highly up-regulated (Table 6).

Table 6 Up- and down-regulated transcripts in pollen of ragweed plants grown under control (380 ppm CO 2 ) and 700 ppm CO 2 + drought conditions

The AllFam database indicated five additional transcripts for allergenic proteins. Three of these transcripts were up-regulated and two were down-regulated (Table 6).

Quantitative real-time RT-PCR (qRT-PCR)

qRT-PCR was performed for selected ‘Amb a’ transcripts (Figure 4). The relative expression rate ranged from 1 to 4 and increased for Amb a 1.1, Amb a 1.2, Amb a 1.3, Amb a 1.4, Amb a 8 and Amb a 9, while the expression levels of Amb a 1.5, Amb a 5 and Amb a 6 were not influenced or even reduced. The highest values were observed for drought and CO2 + drought (Figure 4) and Amb a 1.4, Amb a 8 and Amb a 9 showed the strongest increase. To validate the results from the SuperSAGE, we compared the log2 fold change of ‘Amb a’ transcripts found in the SuperSAGE libraries and the qRT-PCR results. For the Amb a 1 transcripts, a relatively good correlation was found. The best correlation was observed for the drought treatment, whereas the elevated CO2 and elevated CO2 + drought showed the same expression trend but not identical absolute values. Using only the significantly changed qRT-PCR ratios a significant correlation with the SuperSAGE data sets was found (Additional file 7). For Amb a 8, the qRT-PCR data contrasted the SuperSAGE data and for Amb a 9, the fold changes were much higher for the SuperSAGE data compared to the qRT-PCR values. However, this kind of result has also been reported in the literature with coincident and contrasting data for SuperSAGE and microarrays [87], as well as for the SuperSAGE and qRT-PCR analyses [88]. In sheepgrass differences up to a factor of 2.5 between digital gene expression data and RT-PCR ratio and even inconsistencies were reported [89]. In poplar differences by factors of 4–16 between microarray and qRT-PCR data were reported and in switchgrass also factors up to 15 were found [90, 91]. This result reflects a general problem when comparing transcript abundance with different platforms and might be caused by allele-specific gene expression [88, 92]. Moreover, it is interesting to note that transcript abundances are important when comparing different platforms and that good correlations were found for high abundance transcripts and a correlation decrease for lower abundance transcripts [93], as it was also given for Amb a 8 in this study.

Figure 4

Quantitative real-time RT-PCR of selected ragweed allergens. The relative expression is indicated as fold change. The gene-specific primers are given in Additional file 8. As a reference gene, α-tubulin was used. The bars indicate SE and an asterisk indicates significant changes; N = 4 individual plants and three technical replicates.


Our data on ragweed plants fumigated with elevated CO2 and drought stress conditions support the idea that pollen transcripts related to allergenicity are influenced by such global climate change factors. A strong up-regulation of ‘Amb a’ transcripts was evident under elevated CO2, drought stress and elevated CO2 + drought stress conditions. Based on normalized tags, Amb a 1.1 and Amb a CPI were expressed at the highest levels. The increased Amb a 1 transcript level is in accordance with an increased Amb a 1 protein content under elevated CO2 concentrations [22].This result clearly indicates that under expected global change conditions, the allergenicity of ragweed pollen may increase, thereby affecting human health. However, we cannot exclude the possibility that the increased ‘Amb a’ transcript level will also reflect the corresponding allergenic protein level, as an incongruent expression between transcripts and proteins is well described in the literature [9496]. In addition to the well-known ‘Amb a’ transcripts, transcript homologies to other plant allergens were found that might modulate the ‘Amb a’ allergenic response. However, this possibility requires to be tested in suitable model systems.


Plant growth conditions

Ragweed seeds were collected from a single plant from an outdoor stand to avoid parental environmental effects on the growth and development of the next generation [97]. The experiment began on March 29, 2010. The plants were grown in fully air-conditioned greenhouse cabins, each 36 m2 ( as recently described [21]. One cabin was fumigated with 380 ppm CO2 (control samples) and in the second the CO2 was enriched to 700 ppm (CO2 samples). The light conditions and temperatures were according to the outside (10°C - 35°C) and the relative humidity ranged from 55% -70% (Additional file 8). The watering of plants was carried automatically by a tube system applying 100 ml per pot each day. The drought stress began on May 21 by reducing the watering to 100 ml per 36 h. The pollen was collected continuously from August 9 to November 22 using a modified ARACON system (BETATECH, Ghent, Belgium) [17] and stored at −80°C until use.

Pollen viability

The pollen viability was analyzed by the p-phenylenediamine test according to Rodriguez-Riano and Dafni [98].

Analyses of phenolic metabolites

15 mg of frozen pollen was extracted with 1.2 ml phosphate buffer saline (PBS) for 1 h at room temperature (RT). After centrifugation the residue was then extracted with 1.2 ml MeOH for 1 h at RT. Reverse-phase high-performance liquid chromatography (RP-HPLC) separation of the aqueous and methanol extracts was as described by Ghirardo et al. [99].

SuperSAGE libraries

Pollen from three single plants of each treatment were combined for RNA isolation. The isolation was carried out by GenXPro GmbH (Frankfurt, Germany) using the InviTrap® Spin Plant RNA Mini Kit (STRATEC Molecular GmbH, Berlin, Germany). In detail: 20–30 mg pollen was added to 900 μl lysis solution DCT + 10 μl 2-mercaptoethanol and homogenized for 2× 1 min at 30 Hz using a TissueLyser II by Retsch (QIAGEN, Hilden, Germany). The homogenate was then thoroughly mixed by vortexing and incubated for 10 min under continuous shaking. The remaining steps followed the kit instructions. The yield was 10–24 μg of total RNA (measured with Implen NanoPhotometer™ (Implen GmbH, München, Germany) using the LabelGuard™ Microliter Cell with LF10 lid. A DNAse I digestion was carried out with Baseline-ZERO DNAse (Biozym Scientific GmbH, Hessisch Oldendorf, Germany) in order to exclude even traces of genomic DNA. Purification of total RNA after DNAse I digestion was carried out with MACHEREY-NAGEL “NucleoSpinRNA Clean-up XS-Kit (MACHEREY-NAGEL, Düren, Germany). The quality of total RNA was checked on a Bioanalyzer with a 2100 expert Plant RNA Nano chip (Agilent Technologies, Waldbronn, Germany). The total RNA had RIN-values between 6.2 and 8.0.

The construction of the ST-DGE/SuperSAGE libraries was carried out by GenXPro essentially as described by Matsamura et al. [100] with the implementation of GenXPro-specific technology. For each of the 4 SuperSAGE libraries 5 μg of total RNA was applied for processing the ST-DGE library preparation with improved SOPs for quality control as well as specific bias proved adapters (patent pending) for elimination of PCR artifacts (TrueQuant methodology).

Bioinformatic analysis

The four libraries L1 = AmK (380 ppm CO2), L2 = AmC (700 ppm CO2), L3 = AmCT (700 ppm CO2 + drought stress) and L4 = AmT (380 ppm CO2 + drought stress) were BLASTed against the Asteraceae databases of TIGR and NCBI and then against TIGR all plant and against the plant GDB. The pairwise comparison of the libraries was performed using the STDGE2GO-Tool analyses tool for gene ontology (GenXPro) with a score value of at least 36. For the probability of a tag to be differentially expressed, we used a p-value of < 0.0001 for Asteraceae and a p-value < E−10 for all other plants and a fold change of at least 1.5 [101]. The normalized values of each tag in relation to one million tags are listed (tpm = tags per million). Tags that are present zero times are replaced by 0.05 to allow for the calculation. According to the cumulative frequency distribution and approximately 40% - 50% of the expressed genes, a tpm threshold of > 0.8 was used for each of the library comparisons (Additional file 4) [72]. Additionally, MapMan [73] was used to group the SuperSAGE tags into distinct functional categories (BIN-codes). For this grouping, the SuperSAGE tags were first matched to Ambrosia 454-transcriptome data (contigs + singletons) by Kanter et al. [17], allowing a maximum of one mapping error per 26 mer. To define homologous Arabidopsis genes, the sequences of the Ambrosia (454-transcriptome) were compared to the gene set of Arabidopsis (TAIR10). For this comparison, a BLAST search was performed and the first best matched Arabidopsis gene was extracted. Furthermore, only first best hits with ≥ 70% identity covering at least 30 amino acids were assigned to each contig (workflow: Additional file 5). A total of 2,184 non-redundant Arabidopsis genes could be assigned to 454 contigs using SuperSAGE evidence. Next, the hit counts were calculated for each contig and to allow for a between-sample comparison, the hit counts were normalized and the tpm values were calculated. Moreover, for a pairwise comparison, the log2 fold-change (contig x, sample s1, control s2) = log2 [tpm (x,s1) / tpm (x,s2)] was calculated. For samples that were present zero times, the tpm was replaced by 0.05 to allow for the calculation of the ratio. The data were then filtered tpm > 0.8 and were analyzed by MapMan.


RNA was isolated according to Kanter et al. [17]. The DNA digestion was performed with RQ1 RNase-Free DNAse (Promega, Mannheim, Germany). The RNA yield and quality were determined by spectral photometry at 230, 260 and 280 nm. Only RNA with acceptable ratios of 260/280 (>2.0) and 260/230 (>2.0) was used and reversed transcribed. Reverse transcription was carried out using 2 μg total RNA and superscript II reverse transcriptase according to the manufacturer’s instructions (Invitrogen, Karlsruhe, Germany).

The obtained cDNA was diluted 1:20 and the qRT-PCR was performed in a 20-μl reaction mixture of SYBR Green ROX mix (12.5 μl) (Thermo Scientific QPCR), 5 μl cDNA and 1.25 μl forward and reverse primer each using the ABIPrism 7500 fast real-time PCR system (Applied Biosystems, Darmstadt, Germany). The PCR conditions were as follows: 1 cycle at 50°C for 2 min, 1 cycle at 95°C for 10 min, 40 cycles at 95°C for 15 s and 60°C for 1 min. As an internal standard, α-tubulin was used; the relative expression was calculated using the REST© software tool [102]. The gene-specific primers for α-tubulin and ragweed allergens are given in Additional file 9.


  1. 1.

    Gadermaier G, Wopfner N, Wallner M, Egger M, Didierlaurent A, Regl G, Aberger F, Lang R, Ferreira F, Hawranek T: Array-based profiling of ragweed and mugwort pollen allergens. Allergy. 2008, 63: 1543-1549.

    CAS  PubMed  Google Scholar 

  2. 2.

    Ziska L, Knowlton K, Rogers C, Dalan D, Tierney N, Elder MA, Filley W, Shropshire J, Ford LB, Hedberg C, Fleetwood P, Hovanky KT, Kavanaugh T, Fulford G, Vrtis RF, Patz JA, Portnoy J, Coates F, Bielory L, Frenz D: Recent warming by latitude associated with increased length of ragweed pollen season in central North America. Proc Natl Acad Sci U S A. 2011, 108: 4248-4251.

    PubMed Central  CAS  PubMed  Google Scholar 

  3. 3.

    Wopfner N, Gadermaier G, Egger M, Asero R, Ebner C, Jahn-Schmid B, Ferreira F: The spectrum of allergens in ragweed and mugwort pollen. Int Arch Allergy Immunol. 2005, 138: 337-346.

    CAS  PubMed  Google Scholar 

  4. 4.

    Alberternst B, Nawrath S, Klingenstein F: Biology, distribution and pathways of introduction of Ambrosia artemisiifolia in Germany and assessment from a nature conservation point of view. Nachrichtenblatt des Deutschen Pflanzenschutzdienstes. 2006, 58: 279-285.

    Google Scholar 

  5. 5.

    D'Amato G, Spieksma FTM, Liccardi G, Jäger S, Russo M, Kontou-Fili K, Nikkels H, Wüthrich B, Bonini S: Pollen-related allergy in Europe. Allergy. 1998, 53: 567-578.

    PubMed  Google Scholar 

  6. 6.

    Léonard R, Wopfner N, Pabst M, Stadlmann J, Petersen BO, Duus JO, Himly M, Radauer C, Gadermaier G, Razzazi-Fazeli E, Ferreira F, Altmann F: A new allergen from ragweed (Ambrosia artemisiifolia) with homology to Art v 1 from mugwort. J Biol Chem. 2010, 285: 27192-27200.

    PubMed Central  PubMed  Google Scholar 

  7. 7.

    D'Amato G, Cecchi L: Effects of climate change on environmental factors in respiratory allergic diseases. Clin Exp Allergy. 2008, 38: 1264-1274.

    PubMed  Google Scholar 

  8. 8.

    Gilles S, Behrendt H, Ring J, Traidl-Hoffmann C: The pollen enigma: Modulation of the allergic immune response by non-allergenic, pollen-derived compounds. Curr Pharm Des. 2012, 18: 2314-2319.

    CAS  PubMed  Google Scholar 

  9. 9.

    Mogensen JE, Wimmer R, Larsen JN, Spangfort MD, Otzen DE: The major birch allergen, Bet v 1, shows affinity for a broad spectrum of physiological ligands. J Biol Chem. 2002, 277: 23684-23692.

    CAS  PubMed  Google Scholar 

  10. 10.

    Motta AC, Marliere M, Peltre G, Sterenberg PA, Lacroix G: Traffic-related air pollutants induce the release of allergen-containing cytoplasmic granules from grass pollen. Int Arch Allergy Imm. 2006, 139: 294-298.

    CAS  Google Scholar 

  11. 11.

    Ring J: Davos Declaration: Allergy as a global problem. Allergy. 2012, 67 (2): 141-143.

    CAS  PubMed  Google Scholar 

  12. 12.

    Pawankar R, Canocia GW, Holgate ST, Lockey RF: WAO White Book on Allergy. Milwaukee: World Allergy Organization 2011.

    Google Scholar 

  13. 13.

    Erler A, Hawranek T, Krückemeier L, Asam C, Egger M, Ferreira F, Briza P: Proteomic profiling of birch (Betula verrucosa) pollen extracts from different origins. Proteomics. 2011, 11: 1486-1498.

    CAS  PubMed  Google Scholar 

  14. 14.

    Bryce M, Drews O, Schenk MF, Menzel A, Estrella N, Weichenmeier I, Smulders MJM, Buters J, Ring J, Görg A, Behrendt H, Traidl-Hoffmann C: Impact of urbanization on the proteome of birch pollen and its chemotactic activity on human granulocytes. Int Arch Allergy Immunol. 2010, 151: 46-55.

    CAS  PubMed  Google Scholar 

  15. 15.

    Beck I, Jochner S, Gilles S, McIntyre M, Buters JTM, Schmidt-Weber C, Behrendt H, Ring J, Menzel A, Traidl-Hoffmann C: High environmental ozone levels lead to enhanced allergenicity of birch pollen. Plos One. 2013, 8: e80147-

    PubMed Central  PubMed  Google Scholar 

  16. 16.

    Eckl-Dorna J, Klein B, Reichenauer TG, Niederberger V, Valenta R: Exposure of rye (Secale cereale) cultivars to elevated ozone levels increases the allergen content in pollen. J Allergy Clin Immunol. 2010, 126: 1315-1317.

    CAS  PubMed  Google Scholar 

  17. 17.

    Kanter U, Heller W, Durner J, Winkler JB, Engel M, Behrendt H, Holzinger A, Braun P, Hauser H, Ferreira F, Mayer K, Pfeifer M, Ernst D: Molecular and immunological characterization of ragweed (Ambrosia artemisiifolia L.) pollen after exposure of the plants to elevated ozone over a whole growing season. PLoS One. 2013, 8: e61518-

    PubMed Central  CAS  PubMed  Google Scholar 

  18. 18.

    Rogers CA, Wayne PM, Macklin EA, Muilenberg ML, Wagner CJ, Epstein PR, Bazzaz FA: Interaction of the onset of spring and elevated atmospheric CO2 on ragweed (Ambrosia artemisiifolia L.) pollen production. Environ Health Perspect. 2006, 114: 865-869.

    PubMed Central  CAS  PubMed  Google Scholar 

  19. 19.

    Ziska LH, Epstein PR, Rogers CA: Climate change, aerobiology, and public health in the Northeast United States. Mitig Adapt Strateg Glob Chang. 2008, 13: 607-613.

    Google Scholar 

  20. 20.

    Stinson KA, Bazzaz FA: CO2 enrichment reduces reproductive dominance in competing stands of Ambrosia artemisiifolia (common ragweed). Oecologia. 2006, 147: 155-163.

    CAS  PubMed  Google Scholar 

  21. 21.

    El-kelish A, Winkler JB, Lang H, Holzinger A, Behrendt H, Durner J, Kanter U, Ernst D: Effects of ozone and CO2 , drought stress on the growth and pollen production of common ragweed (Ambrosia artemisiifolia). Julius-Kühn-Archiv. 2014, In press

    Google Scholar 

  22. 22.

    Singer BD, Ziska LH, Frenz DA, Gebhard DE, Straka JG: Increasing Amb a 1 content in common ragweed (Ambrosia artemisiifolia) pollen as a function of rising atmospheric CO2 concentration. Funct Plant Biol. 2005, 32: 667-670.

    CAS  Google Scholar 

  23. 23.

    Tebaldi C, Hayhoe K, Arblaster J, Meehl GA: Going to the extremes. Clim Chang. 2006, 79: 185-211.

    Google Scholar 

  24. 24.

    Long SP, Ort DR: More than taking the heat: crops and global change. Curr Opin Plant Biol. 2010, 13: 241-248.

    PubMed  Google Scholar 

  25. 25.

    Ahuja I, de Vos RCH, Bones AM, Hall RD: Plant molecular stress responses face climate change. Trends Plant Sci. 2010, 15: 664-674.

    CAS  PubMed  Google Scholar 

  26. 26.

    Shinozaki K, Yamaguchi-Shinozaki K, Seki M: Regulatory network of gene expression in the drought and cold stress responses. Curr Opin Plant Biol. 2003, 6: 410-417.

    CAS  PubMed  Google Scholar 

  27. 27.

    Tardieu F, Granier C, Muller B: Water deficit and growth. Co-ordinating processes without an orchestrator?. Curr Opin Plant Biol. 2011, 14: 283-289.

    PubMed  Google Scholar 

  28. 28.

    Valliyodan B, Nguyen HT: Understanding regulatory networks and engineering for enhanced drought tolerance in plants. Curr Opin Plant Biol. 2006, 9 (2): 189-195.

    CAS  PubMed  Google Scholar 

  29. 29.

    Verslues PE, Juenger TE: Drought, metabolites, and Arabidopsis natural variation: a promising combination for understanding adaptation to water-limited environments. Curr Opin Plant Biol. 2011, 14: 240-245.

    CAS  PubMed  Google Scholar 

  30. 30.

    Yamaguchi-Shinozaki K, Shinozaki K: Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol. 2006, 57: 781-803.

    CAS  PubMed  Google Scholar 

  31. 31.

    Honys D, Twell D: Comparative analysis of the Arabidopsis pollen transcriptome. Plant Physiol. 2003, 132 (2): 640-652.

    PubMed Central  CAS  PubMed  Google Scholar 

  32. 32.

    Pina C, Pinto F, Feijó JA, Becker JD: Gene family analysis of the Arabidopsis pollen transcriptome reveals biological implications for cell growth, division control, and gene expression regulation. Plant Physiol. 2005, 138: 744-756.

    PubMed Central  CAS  PubMed  Google Scholar 

  33. 33.

    Noir S, Bräutigam A, Colby T, Schmidt J, Panstruga R: A reference map of the Arabidopsis thaliana mature pollen proteome. Biochem Biophys Res Commun. 2005, 337: 1257-1266.

    CAS  PubMed  Google Scholar 

  34. 34.

    Holmes-Davis R, Tanaka CK, Vensel WH, Hurkman WJ, McCormick S: Proteome mapping of mature pollen of Arabidopsis thaliana. Proteomics. 2005, 5: 4864-4884.

    CAS  PubMed  Google Scholar 

  35. 35.

    Wang Y, Zhang W-Z, Song L-F, Zou J-J, Su Z, Wu W-H: Transcriptome analyses show changes in gene expression to accompany pollen germination and tube growth in Arabidopsis. Plant Physiol. 2008, 148: 1201-1211.

    PubMed Central  CAS  PubMed  Google Scholar 

  36. 36.

    Whittle CA, Malik MR, Li R, Krochko JE: Comparative transcript analyses of the ovule, microspore, and mature pollen in Brassica napus. Plant Mol Biol. 2010, 72: 279-299.

    CAS  PubMed  Google Scholar 

  37. 37.

    Changsong Z, Diqiu Y: Analysis of the cold-responsive transcriptome in the mature pollen of Arabidopsis. J Plant Biol. 2010, 53: 400-416.

    Google Scholar 

  38. 38.

    Winkel-Shirley B: Biosynthesis of flavonoids and effects of stress. Curr Opin Plant Biol. 2002, 5: 218-223.

    CAS  PubMed  Google Scholar 

  39. 39.

    Pourcel L, Routaboul J-M, Cheynier V, Lepiniec L, Debeaujon I: Flavonoid oxidation in plants: from biochemical properties to physiological functions. Trends Plant Sci. 2007, 12: 29-36.

    CAS  PubMed  Google Scholar 

  40. 40.

    Flenley JR: Why is pollen yellow? And why are there so many species in the tropical rain forest?. J Biogeogr. 2011, 38: 809-816.

    Google Scholar 

  41. 41.

    Shirley BW: Flavonoid biosynthesis: 'new' functions for an 'old' pathway. Trends Plant Sci. 1996, 1: 377-382.

    Google Scholar 

  42. 42.

    Mo Y, Nagel C, Taylor LP: Biochemical complementation of chalcone synthase mutants defines a role for flavonols in functional pollen. Proc Natl Acad Sci U S A. 1992, 89: 7213-7217.

    PubMed Central  CAS  PubMed  Google Scholar 

  43. 43.

    Berrens L, delaCuadra B, Gallego MT: Complement inactivation by allergenic plant pollen extracts. Life Sci. 1997, 60 (17): 1497-1503.

    CAS  PubMed  Google Scholar 

  44. 44.

    Yoon M-S, Lee JS, Choi B-M, Jeong Y-I, Lee C-M, Park J-H, Moon Y, Sung S-C, Lee SK, Chang YH, Chung HY, Park Y-M: Apigenin inhibits immunostimulatory function of dendritic cells: Implication of immunotherapeutic adjuvant. Mol Pharmacol. 2006, 70: 1033-1044.

    CAS  PubMed  Google Scholar 

  45. 45.

    Hidvégi T, Berrens L, Varga L, Marañon F, Schmidt B, Kirschfink M, Füst G: Comparative study of the complement-activating and specific IgE-binding properties of ragweed pollen allergen. Clin Exp Immunol. 1997, 108: 122-127.

    PubMed Central  PubMed  Google Scholar 

  46. 46.

    Romano MLG, Gallego MT, Berrens L: Extraordinary stability of IgE-binding Parietaria pollen allergens in relation to chemically bound flavonoids. Mol Immunol. 1996, 33: 1287-1293.

    CAS  Google Scholar 

  47. 47.

    van Loon LC, Rep M, Pieterse CMJ: Significance of inducible defense-related proteins in infected plants. Annu Rev Phytopathol. 2006, 44: 135-162.

    CAS  PubMed  Google Scholar 

  48. 48.

    Koistinen KM, Soininen P, Venäläinen TA, Häyrinen J, Laatikainen R, Peräkylä M, Tervahauta AI, Kärenlampi SO: Birch PR-10c interacts with several biologically important ligands. Phytochemistry. 2005, 66: 2524-2533.

    CAS  PubMed  Google Scholar 

  49. 49.

    Liu J-J, Ekramoddoullah AKM: The family 10 of plant pathogenesis-related proteins: Their structure, regulation, and function in response to biotic and abiotic stresses. Physiol Mol Plant Pathol. 2006, 68: 3-13.

    CAS  Google Scholar 

  50. 50.

    Seutter von Loetzen C, Hoffmann T, Hartl MJ, Schweimer K, Schwab W, Rösch P, Hartl-Spiegelhauer O: Secret of the major birch pollen allergen Bet v 1: identification of the physiological ligand. Biochem J. 2014, 457: 379-390.

    CAS  PubMed  Google Scholar 

  51. 51.

    Gilles S, Fekete A, Zhang X, Beck I, Blume C, Ring J, Schmidt-Weber C, Behrendt H, Schmitt-Kopplin P, Traidl-Hoffmann C: Pollen metabolome analysis reveals adenosine as a major regulator of dendritic cell-primed TH cell responses. J Allergy Clin Immunol. 2011, 127: 454-461.

    CAS  PubMed  Google Scholar 

  52. 52.

    Traidl-Hoffmann C, Kasche A, Menzel A, Jakob T, Thiel M, Ring J, Behrendt H: Impact of pollen on human health: More than allergen carriers?. Int Arch Allergy Immunol. 2003, 131: 1-13.

    PubMed  Google Scholar 

  53. 53.

    Marshall DL, Tyler AP, Abrahamson NJ, Avritt JJ, Barnes MG, Larkin LL, Medeiros JS, Reynolds J, Shaner MGM, Simpson HL, Maliakal-Witt S: Pollen performance of Raphanus sativus (Brassicaceae) declines in response to elevated [CO2]. Sex Plant Reprod. 2010, 23: 325-336.

    CAS  PubMed  Google Scholar 

  54. 54.

    Ji X, Dong B, Shiran B, Talbot MJ, Edlington JE, Hughes T, White RG, Gubler F, Dolferus R: Control of abscisic acid catabolism and abscisic acid homeostasis is important for reproductive stage stress tolerance in cereals. Plant Physiol. 2011, 156: 647-662.

    PubMed Central  CAS  PubMed  Google Scholar 

  55. 55.

    Tunc-Ozdemir M, Tang C, Ishka MR, Brown E, Groves NR, Myers CT, Rato C, Poulsen LR, McDowell S, Miller G, Mittler R, Harper JF: A cyclic nucleotide-gated channel (CNGC16) in pollen is critical for stress tolerance in pollen reproductive development. Plant Physiol. 2013, 161 (2): 1010-1020.

    PubMed Central  CAS  PubMed  Google Scholar 

  56. 56.

    Sheoran IS, Saini HS: Drought-induced male sterility in rice: changes in carbohydrate levels and enzyme activities associated with the inhibition of starch accumulation in pollen. Sex Plant Reprod. 1996, 9: 161-169.

    Google Scholar 

  57. 57.

    Fang X, Turner NC, Yan G, Li F, Siddique KHM: Flower numbers, pod production, pollen viability, and pistil function are reduced and flower and pod abortion increased in chickpea (Cicer arietinum L.) under terminal drought. J Exp Bot. 2010, 61: 335-345.

    PubMed Central  CAS  PubMed  Google Scholar 

  58. 58.

    Kang Y, Han Y, Torres-Jerez I, Wang M, Tang Y, Monteros M, Udvardi M: System responses to long-term drought and re-watering of two contrasting alfalfa varieties. Plant J. 2011, 68: 871-889.

    CAS  PubMed  Google Scholar 

  59. 59.

    Jaafar HZE, Ibrahim MH, Fakri NFM: Impact of soil field water capacity on secondary metabolites, phenylalanine ammonia-lyase (PAL), maliondialdehyde (MDA) and photosynthetic responses of Malaysian Kacip Fatimah (Labisia pumila Benth). Molecules. 2012, 17: 7305-7322.

    CAS  PubMed  Google Scholar 

  60. 60.

    Yuan Y, Liu Y, Wu C, Chen S, Wang Z, Yang Z, Qin S, Huang L: Water deficit affected flavonoid accumulation by regulating hormone metabolism in Scutellaria baicalensis Georgi roots. PLoS ONE. 2012, 7: e42946-

    PubMed Central  CAS  PubMed  Google Scholar 

  61. 61.

    Ballizany WL, Hofmann RW, Jahufer MZZ, Barrett BA: Multivariate associations of flavonoid and biomass accumulation in white clover (Trifolium repens) under drought. Funct Plant Biol. 2012, 39: 167-177.

    CAS  Google Scholar 

  62. 62.

    Sánchez-Rodríguez E, Moreno DA, Ferreres F, Rubio-Wilhelmi MM, Ruiz JM: Differential responses of five cherry tomato varieties to water stress: Changes on phenolic metabolites and related enzymes. Phytochemistry. 2011, 72: 723-729.

    PubMed  Google Scholar 

  63. 63.

    Wang SY, Bunce JA, Maas JL: Elevated carbon dioxide increases contents of antioxidant compounds in field-grown strawberries. J Agric Food Chem. 2003, 51: 4315-4320.

    CAS  PubMed  Google Scholar 

  64. 64.

    Estiarte M, Peñuelas J, Kimball BA, Hendrix DL, Pinter PJ, Wall GW, LaMorte RL, Hunsaker DJ: Free-air CO2 enrichment of wheat: leaf flavonoid concentration throughout the growth cycle. Physiol Plant. 1999, 105: 423-433.

    CAS  Google Scholar 

  65. 65.

    Ibrahim MH, Jaafar HZE, Rahmat A, Rahman ZA: The relationship between phenolics and flavonoids production with total non structural carbohydrate and photosynthetic rate in Labisia pumila Benth. under high CO2 and nitrogen fertilization. Molecules. 2011, 16: 162-174.

    CAS  Google Scholar 

  66. 66.

    O'Neill BF, Zangerl AR, Dermody O, Bilgin DD, Casteel CL, Zavala JA, DeLucia EH, Berenbaum MR: Impact of elevated levels of atmospheric CO2 and herbivory on flavonoids of soybean (Glycine max Linnaeus). J Chem Ecol. 2010, 36: 35-45.

    PubMed  Google Scholar 

  67. 67.

    Stutte GW, Eraso I, Rimando AM: Carbon dioxide enrichment enhances growth and flavonoid content of two Scutellaria species. J Am Soc Hortic Sci. 2008, 133 (5): 631-638.

    Google Scholar 

  68. 68.

    Ghasemzadeh A, Jaafar HZE, Karimi E, Ibrahim MH: Combined effect of CO2 enrichment and foliar application of salicylic acid on the production and antioxidant activities of anthocyanin, flavonoids and isoflavonoids from ginger. BMC Complement Altern Med. 2012, 12: 229-

    PubMed Central  CAS  PubMed  Google Scholar 

  69. 69.

    Gilardoni PA, Schuck S, Jüngling R, Rotter B, Baldwin IT, Bonaventure G: SuperSAGE analysis of the Nicotiana attenuata transcriptome after fatty acid-amino acid elicitation (FAC): identification of early mediators of insect responses. BMC Plant Biol. 2010, 10: 66-

    PubMed Central  PubMed  Google Scholar 

  70. 70.

    Molina C, Zaman-Allah M, Khan F, Fatnassi N, Horres R, Rotter B, Steinhauer D, Amenc L, Drevon J-J, Winter P, Kahl G: The salt-responsive transcriptome of chickpea roots and nodules via deepSuperSAGE. BMC Plant Biol. 2011, 11: 31-

    PubMed Central  CAS  PubMed  Google Scholar 

  71. 71.

    Yang Z-B, Eticha D, Rotter B, Rao IM, Horst WJ: Physiological and molecular analysis of polyethylene glycol-induced reduction of aluminium accumulation in the root tips of common bean (Phaseolus vulgaris). New Phytol. 2011, 192: 99-113.

    CAS  PubMed  Google Scholar 

  72. 72.

    Shi W, de Graaf CA, Kinkel SA, Achtman AH, Balwin T, Schofield I, Scott HS, Hilton DJ, Smyth GK: Estimating the proportion of microarray probes expressed in an RNA sample. Nucleic Acids Res. 2010, 38: 2168-2176.

    PubMed Central  CAS  PubMed  Google Scholar 

  73. 73.

    Usadel B, Poree F, Nagel A, Lohse M, Czedik-Eysenberg A, Stitt M: A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, maize. Plant Cell Environ. 2009, 32: 1211-1229.

    PubMed  Google Scholar 

  74. 74.

    Benešová M, Holá D, Fischer L, Jedelský PL, Hnilička F, Wilhelmová N, Rothová O, Kočová M, Procházková D, Honnerová J, Fridrichová L, Hniličková H: The physiology and proteomics of drought tolerance in maize: Early stomatal closure as a cause of lower tolerance to short-term dehydration?. Plos One. 2012, 7: 38017-

    Google Scholar 

  75. 75.

    Swindell WR, Huebner M, Weber AP: Transcriptional profiling of Arabidopsis heat shock proteins and transcription factors reveals extensive overlap between heat and non-heat stress response pathways. BMC Genomics. 2007, 8: 125-

    PubMed Central  PubMed  Google Scholar 

  76. 76.

    Lü P, Kang M, Jiang X, Dai F, Gao J, Zhang CQ: RhEXPA4, a rose expansin gene, modulates leaf growth and confers drought and salt tolerance to Arabidopsis. Planta. 2013, 237: 1547-1559.

    PubMed  Google Scholar 

  77. 77.

    Tabuchi A, Li L-C, Cosgrove DJ: Matrix solubilization and cell wall weakening by ß-expansin (group-1 allergen) from maize pollen. Plant J. 2011, 68: 546-559.

    CAS  PubMed  Google Scholar 

  78. 78.

    Bourdenx B, Bernard A, Domergue F, Pascal S, Léger A, Roby D, Pervent M, Vile D, Haslam RP, Napier JA, Lessire R, Joubès J: Overexpression of Arabidopsis ECERIFERUM1 promotes wax very-long-chain alkane biosynthesis and influences plant response to biotic and abiotic stresses. Plant Physiol. 2011, 156: 29-45.

    PubMed Central  CAS  PubMed  Google Scholar 

  79. 79.

    Chen C-N, Chu C-C, Zentella R, Pan S-M, Ho T-HD: AtHVA22 gene family in Arabidopsis: phylogenetic relationship, ABA and stress regulation, and tissue-specific expression. Plant Mol Biol. 2002, 49: 633-644.

    CAS  PubMed  Google Scholar 

  80. 80.

    Allen AM, Lexer C, Hiscock SJ: Characterisation of sunflower-21 (SF21) genes expressed in pollen and pistil of Senecio squalidus (Asteraceae) and their relationship with other members of the SF21 gene family. Sex Plant Reprod. 2010, 23: 173-186.

    CAS  PubMed  Google Scholar 

  81. 81.

    Kräuter-Canham R, Bronner R, Steinmetz A: SF21 is a protein which exhibits a dual nuclear and cytoplasmic localization in developing pistils of sunflower and tobacco. Ann Bot. 2001, 87: 241-249.

    Google Scholar 

  82. 82.

    Baykov AA, Tuominen HK, Lahti R: The CBS domain: A protein module with an emerging prominent role in regulation. ACS Chem Biol. 2011, 6: 1156-1163.

    CAS  PubMed  Google Scholar 

  83. 83.

    Bertoni G: CBS domain proteins regulate redox homeostasis. Plant Cell. 2011, 23: 3562-

    PubMed Central  CAS  PubMed  Google Scholar 

  84. 84.

    Kushwaha HR, Singh AK, Sopory SK, Singla-Pareek SL, Pareek A: Genome wide expression analysis of CBS domain containing proteins in Arabidopsis thaliana (L.) Heynh and Oryza sativa L. reveals their developmental and stress regulation. BMC Genomics. 2009, 10 (1): 200-

    PubMed Central  PubMed  Google Scholar 

  85. 85.

    Ng TB, Cheung RCF, Wong JH, Ye X: Lipid-transfer proteins. Pept Sci. 2012, 98: 268-279.

    CAS  Google Scholar 

  86. 86.

    Egger M, Hauser M, Mari A, Ferreira F, Gadermaier G: The role of lipid transfer proteins in allergic diseases. Curr Allergy Asthma Rep. 2010, 10 (5): 326-335.

    CAS  PubMed  Google Scholar 

  87. 87.

    Molina C, Rotter B, Horres R, Udupa SM, Besser B, Bellarmino L, Baum M, Matsumura H, Terauchi R, Kahl G, Winter P: SuperSAGE: the drought stress-responsive transcriptome of chickpea roots. BMC Genomics. 2008, 9: 553-581.

    PubMed Central  PubMed  Google Scholar 

  88. 88.

    Sharbel TF, Voigt M-L, Corral JM, Galla G, Kumlehn J, Klukas C, Schreiber F, Vogel H, Rotter B: Apomictic and sexual ovules of Boechera display heterochronic global gene expression patterns. Plant Cell Online. 2010, 22: 655-671.

    CAS  Google Scholar 

  89. 89.

    Chen SY, Cai YY, Zhang LX, Yan XQ, Cheng LQ, Qi DM, Zhou QY, Li XX, Liu GS: Transcriptome analysis reveals common and distinct mechanisms for sheepgrass (Leymus chinensis) responses to defoliation compared to mechanical wounding. Plos One. 2014, 9: e89495-

    PubMed Central  PubMed  Google Scholar 

  90. 90.

    Janz D, Behnke K, Schnitzler J-P, Kanawati B, Schmitt-Kopplin P, Polle A: Pathway analysis of the transcriptome and metabolome of salt sensitive and tolerant poplar species reveals evolutionary adaption of stress tolerance mechanisms. BMC Plant Biol. 2010, 10: 150-

    PubMed Central  PubMed  Google Scholar 

  91. 91.

    Li Y-F, Wang Y, Tang Y, Kakani VG, Mahalingam R: Transcriptome analysis of heat stress response in switchgrass (Panicum virgatum L.). BMC Plant Biol. 2013, 13: 153-

    PubMed Central  PubMed  Google Scholar 

  92. 92.

    Sharbel TF, Voigt M-L, Corral JM, Thiel T, Varshney A, Kumlehn J, Vogel H, Rotter B: Molecular signatures of apomictic and sexual ovules in the Boechera holboellii complex. Plant J. 2009, 58: 870-882.

    CAS  PubMed  Google Scholar 

  93. 93.

    Wang SM: Understanding SAGE data. Trends Genet. 2007, 23 (1): 42-50.

    PubMed  Google Scholar 

  94. 94.

    Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19: 1720-1730.

    PubMed Central  CAS  PubMed  Google Scholar 

  95. 95.

    Sánchez-Pons N, Irar S, García-Muniz N, Vicient CM: Transcriptomic and proteomic profiling of maize embryos exposed to camptothecin. BMC Plant Biol. 2011, 11: 91-

    PubMed Central  PubMed  Google Scholar 

  96. 96.

    Perco P, Mühlberger I, Mayer G, Oberbauer R, Lukas A, Mayer B: Linking transcriptomic and proteomic data on the level of protein interaction networks. Electrophoresis. 2010, 31: 1780-1789.

    CAS  PubMed  Google Scholar 

  97. 97.

    Elwell AL, Gronwall DS, Miller ND, Spalding EP, Durham Brooks TL: Separating parental environment from seed size effects on next generation growth and development in Arabidopsis. Plant Cell Environ. 2011, 34: 291-301.

    PubMed  Google Scholar 

  98. 98.

    Rodriguez-Riano T, Dafni A: A new procedure to asses pollen viability. Sex Plant Reprod. 2000, 12: 241-244.

    Google Scholar 

  99. 99.

    Ghirardo A, Heller W, Fladung M, Schnitzler J-P, Schroeder H: Function of defensive volatiles in pedunculate oak (Quercus robur) is tricked by the moth Tortrix viridana. Plant Cell Environ. 2012, 35: 2192-2207.

    CAS  PubMed  Google Scholar 

  100. 100.

    Matsumura H, Yoshida K, Luo S, Kimura E, Fujibe T, Albertyn Z, Barrero RA, Krüger DH, Kahl G, Schroth GP, Terauchi R: High-throughput SuperSAGE for digital gene expression analysis of multiple samples using next generation sequencing. PLoS ONE. 2010, 5: e12010-

    PubMed Central  PubMed  Google Scholar 

  101. 101.

    Audic S, Claverie J-M: The significance of digital gene expression profiles. Genome Res. 1997, 7: 986-995.

    CAS  PubMed  Google Scholar 

  102. 102.

    Pfaffl MW, Horgan GW, Dempfle L: Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002, 30: e36-

    PubMed Central  PubMed  Google Scholar 

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This work was supported by the grant 3/09 CK-CARE, Christine Kühne - Center for Allergy Research & Education; the German Academic Exchange Service (DAAD), the Egyptian Ministry of Higher Education & Scientific Research and the China Scholarship Council. We acknowledge the excellent technical support by E. Gerstner, B. Groß, P. Kary and H. Lang. The ragweed seeds were kindly provided by B. Alberternst (Friedberg).

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Corresponding author

Correspondence to Ulrike Frank.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contribution

JD, HB, CTH, UF and DE performed and designed the experiments. AE, FZ, WH and UF performed the experiments. AE, WH, RH, MP, UF and DE analysed the data. JBW was responsible for the greenhouse cabins. UF and DE wrote the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

Additional file 1: Viability of ragweed pollen.(PDF 7 KB)

Additional file 2: RP-HPLC diagram of water-soluble and methanol-extractable metabolites.(PDF 366 KB)

Additional file 3: SuperSAGE libraries. Number of sequenced tags and tag frequencies. (ZIP 6 MB)

Additional file 4: Cumulative frequency distribution TPM values.(XLS 43 KB)

Workflow of the

Additional file 5: Ambrosia transcriptome analysis via MapMan.(PDF 34 KB)

Interesting BIN-names detected by MapMan.

Additional file 6: BIN-codes, BIN-names, the Arabidopsis gene ID as well as a short description are given. Log2 fold changes for treatments as compared to the control are shown. Arabidopsis sequence matches were grouped according to their log2 fold change value. Only values of a log2 fold change of at least 1.5 were considered important; blue = up-regulation (log2 > 1.5), yellow = down-regulation (log2 < −1.5). (XLSX 22 KB)

Correlation of SuperSAGE data with qRT-PCR data.

Additional file 7: 1–4: drought stress, 1: Amb a 1.1; 2: Amb a 1.2, 3: Amb a 1.3; 4: Amb a 9; 5–6: 700 ppm CO2 + drought, 5: Amb a 1.1; 6: Amb a 1.2. (PDF 21 KB)

Additional file 8: Greenhouse data. Temperature, relative humidity and light conditions in the greenhouse during the vegetation period of ragweed are given. (PDF 141 KB)

Additional file 9: Sequences of primers that were used for quantitative real-time RT-PCR (qRT-PCR).(PDF 141 KB)

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El Kelish, A., Zhao, F., Heller, W. et al. Ragweed (Ambrosia artemisiifolia) pollen allergenicity: SuperSAGE transcriptomic analysis upon elevated CO2 and drought stress. BMC Plant Biol 14, 176 (2014).

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  • Ambrosia artemisiifolia
  • Allergen
  • Allergy
  • CO2
  • Drought
  • Flavonoids
  • Pollen
  • Ragweed
  • Scanning electron microscopy
  • Transcriptome