Abstract

   Exploring the transcriptome of crops in response to warming and
   elevated CO[2] (eCO[2]) is important to gaining insights of botanical
   adaption and feedback to climate change. This study deployed Illumina
   sequencing technology to characterize transcriptomic profile of maize
   plants at the silking stage, which were grown under warming (2 °C
   higher than ambient temperature) and eCO[2] (550 ppm) conditions. The
   treatment of ambient temperature and ambient CO[2] concentration was
   considered as control (CK). Warming, eCO[2] and warming plus eCO[2]
   resulted in 2732, 1966 and 271 genes expressing differently (DEGs)
   compared to the CK, respectively. Among the DEGs, 48, 47 and 36 gene
   ontology (GO) terms were enriched in response to warming, eCO[2] and
   warming plus eCO[2] compared to the CK, respectively. The majority of
   genes were assigned to the biological process category and the cellular
   component category. Elevated CO[2] significantly inhibited gene
   expressions in terms of photosynthesis and carbohydrate biosynthesis
   pathways. Warming not only negatively affected expressions of these
   genes, but also secondary pathways of nitrogen (N) metabolism,
   including key enzymes of GST30, GST7, GST26, GST15, GLUL and glnA.
   These results indicated the negative biochemical regulation and
   physiological functions in maize in response to warming and eCO[2],
   highlighting the necessity to improve the genetic adaptability of plant
   to future climate change.

   Subject terms: Transcriptomics, Abiotic

Introduction

   The increase in atmospheric concentration of CO[2] is the main driver
   of global warming. According to the Intergovernmental Panel on Climate
   Change^[38]1, atmospheric CO[2] concentration has exceeded 400 parts
   per million (ppm) with a consequence of global temperature increase by
   0.85 °C since the industrial revolution. The atmospheric CO[2]
   concentration is predicted to reach 500~700 ppm by 2050 and 650~1200
   ppm by 2100, which is likely to contribute to a global temperature
   increase of 2 to 4 °C by the end of this century^[39]2.

   Crop production is likely to be influenced by elevated CO[2] (eCO[2])
   and global warming. In general, eCO[2] stimulates photosynthesis and
   increases yield in C3 crops while the effect may become less pronounced
   in C4 crops such as maize^[40]3 due to greater CO[2] concentration
   inside the bundle sheath cells than atmospheric CO[2] saturating
   Rubisco^[41]4. The yield response of maize to eCO[2] are not consistent
   among studies with positive^[42]5–[43]7 or no effect^[44]8. However,
   the growth of maize plants may be sensitive to temperature. Using
   models, Hatfield et al. (2016) predicted that every 2 °C increase may
   result in yield reduction of up to 8% in the Corn Belt region,
   U.S^[45]9. In north-west India, Abebe et al. (2011) found that the
   1.5 °C rise of temperature decreased maize yield by 15%^[46]7.

   A better understanding the mechanisms of eCO[2] and warming interactive
   effect on maize yield requires explorations at the genetic level,
   linking the gene-manipulated physiological functions to plant growth
   and productivity under climate change. High through-put transcriptome
   profiling has been widely deployed to identify genes in soybean or
   maize, which are differentially expressed in response to those
   environmental factors^[47]10–[48]12. There were total of 1390 genes in
   the maize leaf either that were up- or down-regulated during 14 days of
   exposure to eCO[2]^[49]10. However, to our knowledge, very few studies
   have been performed regarding the transcriptome analysis on maize in
   response to warming plus elevated CO[2].

   With the RNA sequencing technology, we investigated the gene expression
   profile of maize in response to eCO[2] and warming at the silking stage
   which is critical to yield formation. We hypothesized that the
   expression of genes regulating the photosynthetic pathway might be
   inhibited by warming, but eCO[2] might alleviate this negative trend.
   The results would provide novel data on the biochemical regulation and
   physiological functions in maize in response to the two environmental
   factors, which will help to develop better management strategies for
   crops adapting to future climate changes.

Materials and Method

Experimental design and plant growth

   A pot experiment was conducted in open-top chambers (OTC) at the
   Northeast Institute of Geography and Agroecology (45^o73′N, 126^o61′E),
   Chinese Academy of Sciences, Harbin, China. The experimental design was
   a random block design with two atmospheric CO[2] concentrations
   (ambient CO[2] and eCO[2]) and two temperature levels (ambient
   temperature and warming). Thus, there were four treatments in total,
   i.e. ambient CO[2] and ambient temperature as control (CK), warming
   only, eCO[2] only, and warming plus eCO[2]. Each treatment had three
   replicates. Thus, there were 12 octagonal OTCs. Each OTC had a steel
   frame body with 2.0 m high and a 0.5 m high canopy, which formed a 45^o
   angle with plane^[50]13. The OTCs were covered with polyethylene film.
   The transparency of the film was tested with a portable quantum sensor
   (MQ-100, Apogee Instruments, Inc. USA) to determine photosynthetically
   active radiation (PAR), of which the wave length ranges from 409 to 659
   nanometers. The photosynthetic photon flux measured inside OTC was more
   than 95% of the full photo flux outside, indicating that the film cover
   won’t affect plant photosynthesis in this study. A number of
   CO[2]-associated studies used similar designs of OTC^[51]14,[52]15. A
   digital CO[2]-regulating system (Beijing VK2010, China) was installed
   to monitor the CO[2] level inside OTCs and automatically regulated the
   supply of CO[2] gas (99.9%) through CO[2] cylinders to achieve CO[2]
   concentrations at 550 ± 30 ppm for eCO[2] and at 400 ± 30 ppm for
   ambient CO[2]. An electronic pump was installed at the ground level in
   each OTC to circulate inner air through a temperature-regulating
   conditioner system to achieve the OTC-inner temperature as same as
   ambient temperature or 2 ± 0.5^oC higher than ambient temperature. The
   temperature records during plant growth were presented in Fig. [53]S1.

   Soil used in this study was collected at Puyang in Henan province
   (116°52′E, 35°20′N), China. The soil type is Fluvisol^[54]16. The soil
   had 18.0 g kg^−1 of organic C, 1.1 g kg^−1 of total N, and a pH (1:5 in
   0.01 M CaCl[2]) 5.8. The soil was air-dried under a shad and then
   sieved (≤4 mm). Thirteen kg of the soil was loaded into each pot (30 cm
   diameter, 35 cm height). Basal nutrients were added into soil at the
   following rates: 100 mg N kg^−1 as urea, 50 mg P kg^−1 and 63 mg K
   kg^−1 as KH[2]PO[4], and 45 mg Ca kg^−1 as CaCl[2]·2H[2]O. The rates of
   other nutrients were as follows: 4.2 mg Mg kg^−1 as MgSO[4]·7H[2]O,
   1.7 mg Fe kg^−1 as Fe-EDTA, 2.2 mg Mn kg^−1 as MnSO[4]·H[2]O, 2.4 mg Zn
   kg^−1 as ZnSO[4], 2 mg Cu kg^−1 as CuSO[4], 0.12 mg B kg^−1 as
   H[3]BO[3], 0.03 mg Co kg^−1 as CoSO[4]·7H[2]O, and 0.08 mg Mo kg^−1 as
   Na[2]MoO[4]·2H[2]O^[55]17. All pots were allocated into respective OTCs
   before sowing. Three maize seeds (Zea mays L. cv. Xiangyu 998) were
   sown in each pot and then thinned to 1 plant 10 days after emergence.
   Soil water content was maintained daily at 80 ± 5% of field capacity by
   weighing and watering.

Measurement

   At the silking stage (55 days after emergence), approximately 1 g of
   the top fully extended leaf was sampled from each pot. In particular,
   the sampling positions were on both sides of the main leaf vein about
   20 cm away from leaf tip. The sampling area was similar across plants
   with about 4 × 7 cm^2. Leaf samples were immediately frozen in liquid N
   and stored at −80°C for the RNA extraction. Before RNA extraction, leaf
   samples were homogeneously ground in liquid N. For chlorophyll
   measurement, 1 g of fresh leaf on the same position near of each leaf
   was sampled and ground in alcohol and acetone mixture (1:1). The leaf
   chlorophyll a and b concentrations in the supernatant of the solution
   was measured using a spectrophotometer at 663 and 645 nm,
   respectively^[56]18. Shoots were also cut at this time at ground level.
   Then the root system was separated from soil, and washed with tap water
   to remove soil and sand particles adhering to the roots. Shoot and root
   samples were oven-dried at 105 °C for 30 min, and then dried at 80 °C
   for three days. The dry biomass of shoot and root were weighed and
   recorded. The dried plant samples were ground using a ball mill (Retsol
   MM2000, Retsch, Haan, Germany) and N concentration was determined by an
   ELEMENTAR III analyzer (Hanau, Germany).

RNA extraction, cDNA library construction and sequencing

   The leaf RNA was extracted using a RNeasy® Plant Mini kit (Qiagen,
   Shanghai, China) according to the manufacturer’s instructions. A cDNA
   library was constructed using a TruSeqt^TM RNA Sample Preparation Kit
   (Illumina, Inc.) and sequenced using an Illumina HiSeq. 2000 platform
   at Shanghai Majorbio Biopharm Technology Co., Ltd (Shanghai, China).
   Quality control was performed to eliminate low-quality reads, adaptor
   polluted reads and ambiguous Ns reads.

Gene functional annotation and expression analysis

   The quality-controlled sequencing data were aligned to the maize genome
   (NCBI BioProject Accession NO. PRJNA10769). The identification of
   differentially expressed genes (DEGs) was detected with the
   Bioconductor package “edgeR” between CK and any of other treatments.
   The different expression was indicated with P value, and the threshold
   of P value was determined by the false discovery rate (FDR)^[57]19.
   Genes with P ≤ 0.05 and an absolute value of log[2] fold changes
   (FC) ≥ 1 were used as the screening criteria^[58]20. The gene
   expression among functional groups was analyzed by the Wilcoxon
   signed-rank test and the influence of CO[2] and temperature at the
   transcriptomic level was evaluated. The P value ≤ 0.05 was considered
   as statistical significance.

   Differentially expressed genes were further processed with Gene
   Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes
   (KEGG) pathway enrichment analysis. GO and KEGG analyses were performed
   to identify which DEGs were significantly enriched in GO terms or
   metabolic pathways. Based on Wallenius non-central hyper-geometric
   distribution^[59]20, GO enrichment analysis of the DEGs was conducted
   using GOseq R packages. GO terms with P value < 0.05 were considered
   significantly enriched among the DEGs. The enriched DEGs in KEGG
   pathways were analyzed with the KOBAS (v2.0.12) software^[60]21. A P
   value < 0.05 also was the threshold for DEGs in KEGG pathways. Plant
   MetGenMAP, a web-based system was used to assign DEGs to metabolic
   pathways^[61]22. Sequences of selected unigenes were aligned within the
   Plant Transcription Factor Database v3.0.

Gene expression verification by quantitative real time PCR (qRT-PCR)

   According to RNA-Seq data, 8 DEGs were randomly selected to verify
   expressions in sequencing via the qRT-PCR. Selected genes and primers
   were shown in Table [62]S1. Total RNA (0.3 μg) from each selected gene
   was treated with gDNA wiper mix and translated into the first strand
   cDNA, which was stored at −20 °C for the subsequent analysis. All
   qRT-PCRs were performed in three technical replicates in following
   steps: pre-denaturation for 5 min at 95 °C and 35 cycles of
   denaturation for 30 s at 95 °C, annealing for 30 s at 56 °C and
   extension for 1 min at 72 °C. Outliers were manually discarded. The
   housekeeping gene GAPDH was used as internal standard to estimate the
   relative expression. Then the gene expressions were standardized to
   transcript levels for PtACTIN calculated with the 2^−ΔΔCt
   method^[63]23.

Statistical analysis

   Data were analysed with Genstat 13 (VSN International, Hemel
   Hemspstead, UK) including plant biomass, N concentration, chlorophyll
   concentration, DEGs and KEGG enrichment. One-Way ANOVA tests were used
   to assess the differences between treatments at significant levels at
   P < 0.05 and P < 0.01^[64]24.

Results

Plant biomass, and N and chlorophyll concentrations

   Neither warming nor eCO[2] significantly influenced the shoot dry
   biomass. A similar result was found in root dry biomass (Table [65]1).
   The leaf N concentration was not affected by warming or eCO[2], but
   warming plus eCO[2] resulted in a significant increase in N
   concentration compared with the CK. Similar trend was found in stem N
   concentration. Warming did not affect the root N concentration.
   However, the concentrations of chlorophyll a and chlorophyll a + b were
   decreased by 20.3% and 18.1% in response to warming compared to the CK.
   Elevated CO[2] did not affect chlorophyll concentrations (Table [66]1).

Table 1.

   Effects of warming and elevate CO[2] (eCO[2]) on plant dry biomass, N
   concentration and chlorophyll concentrations at the silking stage.
   Treatment Shoot dry biomass (g plant^−1) Root dry biomass (g plant^−1)
   Leaf N concentration (mg g^−1) Stem N concentration (mg g^−1) Root N
   concentration (mg g^−1) Chlorophyll a (mg g^−1 FW) Chlorophyll b (mg
   g^−1 FW) Chlorophyll a + b (mg g^−1 FW)
   CK 286 ± 1.11a 53.6 ± 0.96a 15.1 ± 1.01b 9.03 ± 0.52ab 6.35 ± 0.45ab
   11.8 ± 0.18a 53.6 ± 0.97a 16.6 ± 0.87a
   Warming 293 ± 5.04a 47.3 ± 0.19a 16.1 ± 0.37b 9.41 ± 0.33a
   6.84 ± 0.73ab 9.4 ± 1.67b 47.3 ± 0.19a 13.6 ± 2.35b
   eCO[2] 317 ± 16.8a 57.0 ± 7.25a 14.1 ± 0.47b 7.68 ± 0.18b 5.63 ± 0.08b
   12.3 ± 0.66a 57.0 ± 7.25a 17.2 ± 0.27a
   Warming + eCO[2] 301 ± 8.51a 52.2 ± 1.01a 18.7 ± 0.03a 11.5 ± 0.49a
   8.16 ± 0.4a 9.4 ± 1.18b 52.2 ± 1.01a 13.1 ± 1.37b
   [67]Open in a new tab

   The data are means of three replicates ± standard deviation. Different
   letters indicate significant difference between treatments (P > 0.05).
   FW represents fresh weight.

RNA-seq and transcriptomic profile

   To comprehensively investigate the transcriptomic and gene expression
   profiles of maize under eCO[2] and warming, three cDNA samples
   extracted from leaves were prepared and sequenced using Illumina
   NovaSeq. An overview of the RNA-Seq reads of libraries was presented in
   Table [68]S2. After the low-quality reads were removed, 29 Gb clean
   reads were obtained with an average of 7.3 Gb reads for each sample,
   and the proportion of Q30 was greater than 93.33%, which indicates that
   the sequencing results were highly accurate.

   With the Trinity program^[69]25, the total of 190,928 transcripts were
   obtained from clean reads. Among them, the length of 99,410 transcripts
   was more than 1800 bp. The length distribution of transcripts was given
   in Table [70]S2, suggesting that the sequencing assembly was ideal. The
   average correlation between the replicates in each treatment reached
   0.901. The variation of transcription among replicates was marginally
   acceptable due to the sampling size of leaf and the influence of other
   environmental factors such as temperature change during the day.

Analysis of DEGs and GO annotation

   Wilcoxon signed-rank test showed that the ratio of DEGs was not more
   than 1% of the total number of genes in maize. Warming, eCO[2] and
   warming plus eCO[2] caused 2732, 1966 and 271 DEGs compared to the CK,
   respectively (Fig. [71]1A). In particular, warming resulted in 986
   up-regulated and 1746 down-regulated DEGs, and eCO[2] led to 741
   up-regulated and 1225 down-regulated DEGs. However, only 141
   up-regulated and 130 down-regulated DEGs were observed for warming plus
   eCO[2] (Fig. [72]1B). These results indicated that warming and eCO[2]
   interactively reduced the expression of numerous genes in maize.

Figure 1.

   Figure 1
   [73]Open in a new tab

   Summary of differentially expressed genes (DEGs) in response to
   warming, elevated CO[2] (eCO[2]) and warming + eCO[2] in a Venn chart
   (A) and up- and down-regulated DEGs in a bar chart (B).

   Among the DEGs, 48, 47 and 37 GO terms were enriched in warming,
   eCO[2], eCO[2] + warming compared with the CK, respectively, which were
   mainly in biological process, cellular component and the molecular
   function categories (Fig. [74]2). In the biological process category,
   warming and eCO[2] resulted in 1008 and 749 enriched DEGs respectively,
   but only 102 for eCO[2] plus warming. In the cellular component
   category, there were 691 and 499 of DEGs for warming and eCO[2],
   respectively, while only 59 for eCO[2] plus warming. Regarding
   molecular function, the numbers of DEGs reached 939 and 673 for warming
   and eCO[2], respectively, but only 101 for warming plus eCO[2].

Figure 2.

   [75]Figure 2
   [76]Open in a new tab

   Differentially expressed genes (DEGs) annotated against Gene Ontology
   (GO) function in response to warming, elevated CO[2] (eCO[2]) and
   warming + eCO[2]. The horizontal axis shows the secondary annotation of
   three primary categories in GO. The left vertical axis indicates the
   percentage of DEGs to the total annotated genes, and the right vertical
   axis indicates the number DEGs in this secondary classification of GO.

KEGG enrichment analysis

   To further assess the biological functions of these DEGs, pathway-based
   analysis was performed using KEGG. Compared to the CK, warming, eCO[2],
   and warming plus eCO[2] significantly enriched 14, 15, and 3 pathways
   (Fig. [77]3), respectively. The warming-induced enrichment in metabolic
   pathways not only included photosynthesis, carbon fixation in
   photosynthetic organisms, and glyoxylate and dicarboxylate metabolism,
   but also the secondary metabolisms, such as N metabolism (Fig. [78]3A).
   Elevated CO[2] mainly affected photosynthesis and carbohydrate
   metabolisms pathways (Fig. [79]3B). Interestingly, only three
   metabolism pathways were enriched in response to warming plus eCO[2].
   They were plant hormone signal transduction, glycine, serine,
   threonine, starch and sucrose metabolisms (Fig. [80]3C).

Figure 3.

   [81]Figure 3
   [82]Open in a new tab

   Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for
   the effect of warming (A), elevated CO[2] (eCO[2]) (B) and
   eCO[2] + warming (C). The horizontal axis lists the metabolic pathways,
   and the vertical axis indicates the proportion of annotated genes in
   each pathway to the total number of annotated genes. * and ** represent
   P ≤ 0.05 and P ≤ 0.01.

Differential expression of photosynthesis-related genes

   Compared to the CK, 22 DEGs involved in the photosynthesis pathways,
   including Photosystem II core complex proteins (psbY), Photosystem I
   reaction center subunit (psaK) and Photosystem I reaction center
   subunit N (psaN). Oxygen-evolving enhancer protein 1 (psbO) and
   Oxygen-evolving enhancer protein 3–1 (psbQ1) were identified as
   significant down-regulated genes under warming (P < 0.05 &
   |log[2]FC| ≥ 1). Similarly, eCO[2] significantly altered these DEGs as
   well. However, eCO[2] plus warming resulted in only 6 down-regulated
   genes compared to the CK (Table [83]S3).

Differential expression of carbohydrate biosynthesis-related genes

   Warming and eCO[2] had similar effects on carbohydrate
   biosynthesis-related genes (Fig. [84]4). Seventeen DEGs in carbohydrate
   metabolism pathways were significantly down-regulated in response to
   warming, such as genes responsible for gutathione S-transferase (GST
   30), glucose-1-phosphate adenylyltransferase (glgC),
   fructose-bisphosphate aldolase (ALDO), pyruvate phosphate dikinase
   (ppdK), gluconokinase (gntK) and glutathione s-transferase (GST). Only
   two genes were up-regulated, which were associated with pyruvate
   dehydrogenase (PDHB) and dihydrolipoamide acetyltransferase component
   of pyruvate dehydrogenase (DLAT). These genes expressed the similar
   trends in response to eCO[2] except for the chitinase activity
   associated genes. Only 7 genes responded to warming plus eCO[2]
   (Table [85]S4).

Figure 4.

   Figure 4
   [86]Open in a new tab

   Summary of gene expressions in photosynthesis, sugar, primary and
   secondary metabolisms in response to warming, elevated CO[2] (eCO[2])
   and warming + eCO[2]. Annotated Genes and enzymes in the metabolic
   pathway are shown in green letters. C, W and CW represent eCO[2],
   warming and eCO[2] + warming, respectively.

Differential expression of primary and secondary metabolism-related genes

   In response to warming, 14 DEGs were significantly down-regulated,
   while 8 were up-regulated in primary and secondary metabolism pathways.
   Warming resulted in the down-regulated genes including
   1-aminocyclopropane-1-carboxylate synthase activity (ACS), terpene
   synthase activity (TPS11), 12-oxophytodienoate reductase (OPR),
   cytokinin dehydrogenase (CKX) and lipoxygenase (LOX2S). However, eCO[2]
   did not affect these genes. For the up-regulated genes, warming and
   eCO[2] had similar effects. There were 10 DEGs, of which 7 were
   up-regulated expressions and 3 were down-regulated under warming plus
   eCO[2] (Table [87]S5).

qRT-PCR verification for RNA-Seq

   To confirm the accuracy of the Illumina RNA-Seq results, 8 of the
   transcripts related to photosynthesis, sugar and secondary metabolism
   were selected for qRT-PCR (Table [88]S1). The expression levels of
   these DEGs with qRT-PCR were compared to those of DEGs with RNA-Seq. A
   significant correlation (R^2 = 0.819) was observed between the RNA-Seq
   and qRT-PCR (Fig. [89]S3). The qPCR results were consistent with their
   transcript-abundance in RNA-seq, which verified the accuracy of DEGs
   from RNA-seq analyses in this experiment.

Discussion

   Atmospheric CO[2] concentration and temperature are expected to be the
   most important factors impacting the crop production in
   agriculture^[90]26. Although maize is the most widely produced crop
   worldwide, few studies have evaluated the interactive effects of eCO[2]
   and warming on transcriptome in relevant to photosynthesis and growth.
   In this study, we examined the gene expression profiles in
   transcriptome. Although the RNA expression may not directly alter the
   synthesis of proteins and relevant metabolisms, the data analysis of
   gene expression in the present study indicated that temperature and
   CO[2] concentration potentially had a significant impact on metabolic
   pathways such as photosynthesis, C fixation in photosynthetic
   organisms, and primary and secondary metabolic pathways
   (Figs. [91]2–[92]4).

   Elevated CO[2] primarily inhibited gene expression in relation to
   photosynthesis and sugar metabolic pathways. It was evident that eCO[2]
   down regulated the genes coding psbY, psaK, petF, PRK, LHCB1, rbcS,
   GST30, glgC, ppdK, WAXY, pfkA, PDHB, DLAT, GST 15, ALDO, BX4, MDH1,
   DLD, GLUL, MDH2, P5CS, POP2 and thrC, which were involved in the
   Glycolysis, Glyoxylate and dicarboxylate metabolism, fructose and
   mannose metabolism (Figs. [93]3 and [94]4; Tables [95]S3, [96]S4).
   Although eCO[2] results in accumulation of photosynthetic carbon
   assimilation, such as fructose, glucose and sucrose in many C3 species
   due to eCO[2]-induced stimulation on photosynthesis, and sugar
   metabolic pathways^[97]5,[98]27–[99]29, down-regulation of LDH, ALDO,
   glgC and WAXY genes related to sugar metabolism was found in this
   study. This down-regulated gene expression in the sugar metabolism may
   refer to the acclimation responses of maize to eCO[2.] It is probably
   because a large amount of carbohydrates were accumulated under eCO[2],
   inhibiting the relevant sugar metabolisms. The lack of response of
   biomass to eCO[2] supported this conclusion (Table [100]1). In
   addition, PRK can catalyze the transformation of ribulose-5P into
   ribulose-1,5P[2,] which is involved in the glyoxylate and dicarboxylate
   metabolism. The down regulation of PRK gene would negatively impact the
   relevant metabolisms.

   The eCO[2]-induced inhibition of sugar metabolism was likely to
   contribute to the down-regulation of photosynthesis pathways. In this
   study, significant down-regulations in transcripts pertaining to PsaD,
   PsaF, PsaG, PsaK, PsaO subunit in PSI, and PsbY, Psb27, Psb28, PetE and
   PetF in PSII were found under eCO[2]. Chen et al.^[101]30 and Ainsworth
   et al.^[102]31 reported long-term exposure to eCO[2] can reduce
   photosynthetic capacity of plants due to accumulation of carbohydrates
   in the leaf. As C4 crop species have the CO[2]-concentrating mechanism
   (CCM) in photosynthesis^[103]32, leaf photosynthesis is closely
   correlated with the Rubisco. Genes encoding sub-units rbcS that are
   related with Rubisco were inhibited in this experiment. The
   eCO[2]-induced down-regulations of the expression of genes encoding for
   sub-units rbcS have been observed in other species such as wheat and
   pea^[104]32–[105]34. However, the link of photosynthetic capacity with
   the expression of gene rbcS needs further experimental test under
   eCO[2]. Moreover, the down regulation of PPDK may affect
   phosphonend-pyruvate, xaloacetate, and alanine in the C4-dicarboxylic
   acid cycle (Fig. [106]3), which is consistent with the findings by
   Zhang et al.^[107]35.

   Warming also negatively affects the expressions of
   photosynthesis-associated genes (Fig. [108]3, Table [109]S3),
   contributing to significant down-regulation in transcripts pertaining
   to PsaD, PsaF, PsaG, PsaK, PsaL, PsaO subunit in PSI and PsbA, PsbO,
   PsbP, Psb27, Psb28 PetE and PetF in PSII (data not shown). Warming not
   only affects enzymes in all biochemical reactions associated with
   photosynthesis, but also the fluidity and integrity of the chloroplast
   membrane^[110]35. At the current CO[2] concentration, Rubisco activity
   is the main limiting factor for photosynthesis. A study has shown that
   increasing leaf temperature can affect the expression of Rubisco
   carboxylase and reduce Rubisco activity, which in turn affects the
   enzyme activity involved in electron transfer^[111]36, supporting the
   results of this study. Although leaf temperature was not measured in
   this study, a significant relationship between leaf temperature of
   maize and air temperature (r = 0.948, P < 0.001) was found in a
   preliminary study. When the leaf temperature is beyond the optimum
   temperature range for plant growth, the balance between chlorophyll
   biosynthesis and catabolism is likely broken, resulting in reduce in
   leaf chlorophyll content^[112]37,[113]38. Thus, the increase of leaf
   temperature due to warming may inhibit the photosynthetic
   activities^[114]38. These impaired photosynthesis metabolisms may cause
   the decrease in chlorophyll concentration of maize in response to
   warming (Table [115]1). In addition, warming also fundamentally affects
   secondary metabolisms such as galactose metabolism, and N metabolism.
   In particular, the glutamine synthetase (GS) is a key enzyme involved
   in plant N metabolism, which can catalyze glutamate to synthesize
   glutamine (Gln). Some Glns are substrates for the production of amino
   acids in plant^[116]39. In this study, the expression of GST 30, GST7,
   GST26, GST 15, GLUL and glnA were significantly down regulated under
   warming (Tables [117]S4, [118]S5), which may inhibit the N metabolism
   and the synthesis of amino acids. The slow-down N metabolism under
   warming may potentially lower N accumulation during the reproductive
   stage of maize (Fig. [119]3, Table [120]S5). Research showed that
   warming reduced leaf N concentration of maize, which may be associated
   with photosynthetic acclimation^[121]26. Therefore, the change in N
   metabolism under warming in this study might be another contributor to
   the reduction of gene expression in relevant to photosynthetic
   pathways. It is worthy to note that plant performance in pots in this
   study may be slightly different from the field condition, as the effect
   of warming on root and soil microbial activity, and subsequent nutrient
   mineralization and uptake by plants is likely stronger than the field
   condition. The air warming normally influence the surface soil only in
   the field. Moreover, the leaf N concentration in this pot experiment
   (Table [122]1) was lower than the critical N level in plant^[123]40.
   The N deficiency may influence maize metabolisms in response to warming
   as previous studies showed that low N decreased the production of
   numerous proteins associated with C and N metabolism and hormonal
   metabolism at the silking stage of maize^[124]41,[125]42. Therefore,
   the plant growth response and relevant gene expression under warming in
   the pot experiment may differ from field.

   In recent years, the interactive effects of warming and eCO[2] on plant
   biology have drawn increasing attention^[126]43–[127]45. However, the
   number of DEGs in response to warming and eCO[2] was much less than
   either the warming or eCO[2] effect. This may be due to the trade-off
   effect between warming and eCO[2] on plant physiological
   characteristics^[128]46,[129]47, and consequent non-effect on plants
   biomass (p > 0.05) (Table [130]1). Ruiz-Vera et al.^[131]48 also
   reported that there was no significant effect of warming and eCO[2] on
   plant biomass of soybean. However, the reason for the trade-off of
   environmental factors on maize warrants further investigation, which is
   important to predict how future climate change may affect plant
   functions.

Conclusions

   This study not only provided transcriptomic datasets of maize at the
   silking stage under warming and eCO[2] conditions, but also new
   biological insights into the expression of genes associated with
   photosynthesis, carbohydrate biosynthesis and secondary pathways. As a
   summary diagram showed (Fig. [132]4), eCO[2] significantly inhibited
   gene expressions involved in photosynthesis and carbohydrate
   biosynthesis pathways. Warming not only negatively affected these genes
   expression, but also secondary pathways such as N metabolism. The
   inhibition of these genes in response to warming and eCO[2] may limit
   the plant adaptability to future climatic changes.

Supplementary information

   [133]Supplementary section^ (153.9KB, docx)

Acknowledgements