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\begin{document}

\title{Elevated atmospheric CO2 concentration triggers redistribution of
nitrogen to promote tillering in rice}



\author[1]{Juan Zhou}%
\author[1]{Yingbo Gao}%
\author[1]{Junpeng Wang}%
\author[1]{Chang Liu}%
\author[1]{Zi Wang}%
\author[1]{Minjia Lv}%
\author[1]{Xiaoxiang Zhang}%
\author[1]{Yong Zhou}%
\author[1]{Guichun Dong}%
\author[1]{Yulong Wang}%
\author[1]{Jianye Huang}%
\author[2]{Dafeng Hui}%
\author[1]{Zefeng Yang}%
\author[1]{Youli Yao}%
\affil[1]{Yangzhou University}%
\affil[2]{Tennessee State University}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\selectlanguage{english}
\begin{abstract}
Elevated atmospheric CO2 concentration (eCO2) reduces nitrogen (N)
content in rice plants and stimulates tillering. However, these are
contradictory to the general consensus that reduced N would constrain
rice tillering. To resolve this, we detected N distribution in organs
and transcriptomic changes of different organs after subjected to eCO2
in combination with different N application rates. Our results indicated
that eCO2 promoted rice tillers more under higher N supply conditions,
and confirmed that N availability constrained tillering in the early
growth stage. Despite N content declined in the leaf and sheath of rice
exposed to eCO2, the new-born tillers had a stable or higher N content
compared to those under ambient CO2. Apparently the redistribution of N
within the plant per se was a critical adaptation strategy to eCO2
condition. Transcriptomic analysis revealed that eCO2 introduced less
extensive alteration of gene expression than N application. Most
importantly, the expression levels of multiple N-related transporters
and receptors were differentially regulated, suggesting that multiple
genes were involved in sensing the N signal and transporting N
metabolites in adapting to eCO2. The redistribution of N in different
organs could be a universal adaptation strategy of terrestrial plants to
eCO2.%
\end{abstract}%



\sloppy


\textbf{Elevated atmospheric CO\textsubscript{2} concentration triggers
redistribution of nitrogen to promote tillering in rice}

\emph{Running title:} \textbf{N redistribution promotes tillering under
eCO\textsubscript{2}}

\emph{List of Authors:}

Juan Zhou\textsuperscript{1, \#}, Yingbo Gao\textsuperscript{1, 2, \#},
Junpeng Wang\textsuperscript{1}, Chang Liu\textsuperscript{1}, Zi
Wang\textsuperscript{1}, Minjia Lv \textsuperscript{1}, Xiaoxiang
Zhang\textsuperscript{1,3}, Yong Zhou\textsuperscript{1}, Guichun
Dong\textsuperscript{1}, Yulong Wang\textsuperscript{1}, Jianye
Huang\textsuperscript{1}, Dafeng Hui\textsuperscript{4}, Zefeng
Yang\textsuperscript{1,2 *}, Youli Yao\textsuperscript{1,2 *}

\# ZJ and GY should be considered joint first author.

* YY and YZ should be considered joint senior author: yaoyl@yzu.edu.cn;
zfyang@yzu.edu.cn

\emph{Institutional affiliations:}

1. Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation
Center for Modern Production Technology of Grain Crops, College of
Agriculture, Yangzhou University, Yangzhou 225009, China

2. Key Laboratory of Plant Functional Genomics of the Ministry of
Education/Jiangsu Key Laboratory of Crop Genomics and Molecular
Breeding, College of Agriculture, Yangzhou University, Yangzhou 225009,
China

3. Lixiahe Agricultural Research Institute of Jiangsu Province,
Yangzhou, 225007, China.

4. Department of Biological Sciences, Tennessee State University,
Nashville, TN 37209, USA.

\emph{Contact Information}

Youli Yao, Ph. D, College of Agriculture, Yangzhou University, Yangzhou,
Jiangsu Province, 225009, China. Tel: +86-514-87977963, +86-18362829071,
email: yaoyl@yzu.edu.cn

\section*{Abstract}

{\label{abstract}}

Elevated atmospheric CO\textsubscript{2} concentration
(eCO\textsubscript{2}) reduces nitrogen (N) content in rice plants and
stimulates tillering. However, these are contradictory to the general
consensus that reduced N would constrain rice tillering. To resolve
this, we detected N distribution in organs and transcriptomic changes of
different organs after subjected to eCO\textsubscript{2} in combination
with different N application rates. Our results indicated that
eCO\textsubscript{2} promoted rice tillers more under higher N supply
conditions, and confirmed that N availability constrained tillering in
the early growth stage. Despite N content declined in the leaf and
sheath of rice exposed to eCO\textsubscript{2}, the new-born tillers had
a stable or higher N content compared to those under ambient
CO\textsubscript{2}. Apparently the redistribution of N within the
plant\emph{per se} was a critical adaptation strategy to
eCO\textsubscript{2}condition. Transcriptomic analysis revealed that
eCO\textsubscript{2}introduced less extensive alteration of gene
expression than N application. Most importantly, the expression levels
of multiple N-related transporters and receptors were differentially
regulated, suggesting that multiple genes were involved in sensing the N
signal and transporting N metabolites in adapting to
eCO\textsubscript{2}. The redistribution of N in different organs could
be a universal adaptation strategy of terrestrial plants to
eCO\textsubscript{2}. Keywords: atmospheric CO\textsubscript{2}, rice,
tiller, nitrogen, distribution, gene expression.

\section*{1. Introduction}

{\label{introduction}}

The accelerating pace of rising atmospheric carbon dioxide
(CO\textsubscript{2}) concentration has been a serious global climatic
issue for a couple of decades. Despite the Kyoto Protocol being adopted
in 1997 and entered into force in 2005, followed by Paris Agreement in
November 2016, the atmospheric CO\textsubscript{2} concentration
({[}CO\textsubscript{2}{]}) shows no sign of slowing down in its
increasing rate. The current historical record of atmospheric
{[}CO\textsubscript{2}{]} reached a climax of 417.07 ppm in May 2020
(ESRL, Mauna Loa, Hawaii, USA). In terms of crop production, the
elevated atmospheric CO\textsubscript{2} concentration
(eCO\textsubscript{2}) and the consequential temperature hike will bring
in more extreme weathers (such as drought and flood, extreme heatwave
and freezing temperature, hurricane and hails), frequent crop diseases
and pests occurrences, and recurrent adverse crop growth conditions. All
these will reduce crop productivity, quality, and stability, especially
for wheat and maize (Easterling et al, 2005; Rosenzweig et al, 2014; Li
et al, 2015; Wang et al, 2019a). It seems that rice and soybean are the
mere major crops that may marginally benefit from the
eCO\textsubscript{2}, especially when considering the collateral
temperature and ozone hikes (Long et al, 2006; Ainsworth, 2008; Kimball,
2016; Usui et al, 2016; Zhao et al, 2017). To mitigate the threat and
secure the crop production, it is critically important to breed crop
varieties adaptable for the future {[}CO\textsubscript{2}{]} and
temperature conditions, and to integrate a novel cultivation management
system to take the advantage of the fertilization effect of this
irreversible eCO\textsubscript{2} change (Long et al, 2004). However,
genes responsive for eCO\textsubscript{2} adaptation are largely unknown
(Morita et al, 2015; Nakano et al, 2017; Hasegawa et al, 2019), thus, we
need to clarify targets for breeding purpose. Top priority should be
given to those yield-limiting agronomic characters that showing
beneficially responsive to eCO2.

Rice (\emph{Oryza sativa} L.) is a staple cereal crop for more than half
of the world population, especially in the densely populated Asian
regions. Despite the divergent responses of the productivity to
eCO\textsubscript{2} by different crops, it is generally concluded that
rice may marginally benefit from the CO\textsubscript{2} fertilization
in temperate and tropical regimes (Ruiz-Vera et al, 2013; Rosenzweig et
al, 2014; Kimball et al, 2016). Most studies conclude that the
eCO\textsubscript{2} increases grain yield of rice (Hasegawa et al,
2017). For example, multiple-year free-air CO\textsubscript{2}enrichment
(FACE) field experiments conducted at different locations with various
\emph{indica} , \emph{japonica} and hybrid rice varieties concluded
that, eCO\textsubscript{2} at 550 ppm can enhance rice grain yield up to
5\textasciitilde{}35\% compared to the ambient {[}CO\textsubscript{2}{]}
(Kim et al, 2003; Kimball et al, 2016). Among the four major rice yield
components, namely, panicles per area, spikelets per panicle, seed
setting rate (\%), and grain weight, eCO\textsubscript{2} consistently
increases panicles per area, while the other three yield components show
both negative and positive responses (Kim et al, 2003a; Huang et al,
2004; Lai et al, 2014; Hasegawa et al, 2019). The panicle per area is a
fundamental factor that being determined at the earlier stage of rice
growth. It impacts the other three yield components at later stage of
growth and arbitrates final grain yield. Therefore, endeavor to achieve
a stable panicle number is always a top priority in rice production
management.

In addition to the main stem, rice plant produces tillers (branches)
that may eventually develop into panicle florescence to generate grain
yield (Wang and Li, 2011). The tiller number of a rice plant is not only
a basis for panicle number, but also an indicator of plant growth
status. Rice plants with more tillers at the early stages usually
indicate they are in a healthy developmental path toward higher yield.
Tiller number also shows a significant accrual response to
eCO\textsubscript{2} (Kim et al, 2003b; Huang et al. 2004). Since the
discovery of MOC1 in tillering regulation (Li et al, 2003), recent
advances in molecular genetics have clarified that more than 60 genes
involving in tiller regulation in rice plants (Wang et al, 2011; Wang et
al, 2018a). However, most of the knowledge is derived from mutant or
gene manipulation experiments, limited info is available on how they
coordinate in a regular variety (Zhang et al, 2019).

Moreover, nitrogen (N) is a major macronutrient that constrains tiller
growth. Reports have clarified that eCO\textsubscript{2} alters the
element stoichiometry in plants, especially a reduction of N content was
consistently observed from grasses to crops and trees (Luo et al, 2006;
Norby et al, 2010; Deng et al, 2015). Insufficient N availability is a
constraint on the growth of perennial grass species in response to
eCO\textsubscript{2} (Reich et al, 2006; Mueller et al, 2013). Multiple
reviews summarize that quite a range of crops and model plants
displaying certain pattern of interaction between N requirement and
eCO\textsubscript{2}, though interpretation differs but N constraint is
a consistency (Stitt and Krapp, 1999; Wang et al, 2010; Bloom et al,
2015; Rubio-Asensio and Bloom 2017; Andrew et al, 2019).
eCO\textsubscript{2} reduces the N content in rice plant as well (Makino
et al, 1997; Kim et al, 2001; Lieffering et al, 2004; Zhang et al, 2013;
Wang et al, 2019b). However, low N content in rice plants is supposed to
inhibit tiller occurrence (Jiang et al, 1997). Despite the expected
inhibition effect on tillering by reduced N content in rice plants under
the eCO\textsubscript{2}, multiple reports have confirmed that tillers
are promoted by eCO\textsubscript{2} (Jitla et al, 1997; Huang et al,
2004; Yang et al, 2007; Jiang et al, 2020). However, the underlying
physiological and molecular mechanisms remain unclear.

We hypothesized that N distribution changes in favor of tillering under
eCO2 condition. To test this, we investigated the interactive effects of
CO\textsubscript{2} and N rate on rice tillering in growth chambers. Our
objectives were 1) to analyze the interaction effect of
eCO\textsubscript{2} and N application rate on rice tillering at early
stage; 2) to decipher the change in the N distribution among different
organs; and 3) to investigate the molecular change in response to the
eCO\textsubscript{2}. Clarifying the molecular adaptation mechanism of
rice to the eCO\textsubscript{2} would directly enable breeders to
target certain genes in order to breed varieties that can better benefit
from the CO\textsubscript{2} fertilization effect. The mechanism may
also help interpreting the adaptation of other terrestrial plants to the
eCO\textsubscript{2} and develop new approaches to mitigate the
eCO\textsubscript{2}.

\section*{2. Materials and Methods}

{\label{materials-and-methods}}

\section*{2.1 Rice material and culture
conditions}

{\label{rice-material-and-culture-conditions}}

The experiment was conducted in two growth chambers (CMP6050, Conviron,
Winnipeg, Manitoba, Canada) at the Yangzhou University in Yangzhou,
Jiangsu Province, China. Japanese rice variety Nipponbare (\emph{Oryza
sativa} , ssp. \emph{japonica} ) was used in the experiments. Nipponbare
is widely used in rice production, breeding, and molecular genetics
studies. It is the first rice variety being deeply genome sequenced as a
monocots model plant (Sasaki et al, 2005; Matsumoto et al, 2016).

Rice seeds were acquired from Yangzhou Agricultural Research Center, and
surface sterilized with disinfectant prochloraz (Jinbao, Yangzhou Suling
Agricultural Chemicals, Jiangdu, China) following the supplier
recommendation. Seeds were immersed in tap water for 48 hrs before
subjected to germination promotion at 32\selectlanguage{ngerman}°C with a humidity of 85\% in an
incubator for 15 hrs, and at 28°C till shoot and root appearance to
0.5\textasciitilde{}1 cm. The germinated seeds were screened for
uniformity and planted to soil in plastic trays at a spacing of 3.5 cm x
3.2 cm, with each tray containing 96 plants. The plastic tray contained
premixed soil, 12 cm in depth (Zhang et al, 2019), prepared in a similar
way as in field nursery for seedling.

During the growth period, soil was checked three times a day to keep its
moisture at saturated point but without persistent water layer. The
temperature of the growth chamber was preset to 30/22°C (actual records
at ± 0.67°C range) in the 12/12hrs day/night regime, respectively. The
illumination was programmed to 800 \selectlanguage{greek}μ\selectlanguage{english}mol photons
m\textsuperscript{-2}s\textsuperscript{-1} (actual records at \selectlanguage{ngerman}± 58 \selectlanguage{greek}μ\selectlanguage{english}mol
photons m\textsuperscript{-2} s\textsuperscript{-1}) during the day and
0 at night. The relative humidity was set at 70\% (actual records range
65-80\%).

\section*{2.2 Experimental design}

{\label{experimental-design}}

This experiment used a split-plot experimental design with four
replications. The main treatment factor was {[}CO\textsubscript{2}{]}
and subplot treatment factor was N application rate. There were two
{[}CO\textsubscript{2}{]} levels: ambient CO\textsubscript{2} (400 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1}) and eCO\textsubscript{2} (600 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1}). Since we have two independent growth chambers,
we set one chamber for ambient CO\textsubscript{2} and the other for
eCO\textsubscript{2} treatments, and repeated the whole experiment four
times. Each time was considered one replication. The
eCO\textsubscript{2} concentration was achieved by pumping in industrial
grade pure CO\textsubscript{2} to a partial pressure of 600 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1} (actual records range 595-640 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1}), and the ambient condition had a
CO\textsubscript{2} partial pressure range of 405 to 430 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1} during the growth period. As we focused on the
adaptation of rice plants to eCO\textsubscript{2} at the early seedling
stage, we initiated eCO\textsubscript{2} treatment from seeding. N
application rates included four levels: 0 (N0), 5 (N5), 10 (N10) and 15
(N15) kg N 666.7 m\textsuperscript{-2}, which was comparable to the
general range of N fertilization rate in the region at this growth
phase. Half of the N fertilizer (in the form of urea, 46\% N) was
premixed into the soil three days prior to seeding, and the remaining
portion was top-dressed in a water-solution form at true leaf-age 1, 2,
3 and 4, respectively, each at a 12.5\% of the total N application rate.
Together, there were eight treatments, combined from two levels of
{[}CO\textsubscript{2}{]} and four levels of N application rate, and
each treatment had two plastic trays comprising 192 plants.

\section*{2.3 Measurements of total N and carbohydrates
content}

{\label{measurements-of-total-n-and-carbohydrates-content}}

Leaf-age, plant height and tiller numbers were measured at true leaf-age
four and six, respectively. Tiller occurrence percentage was calculated
as total tiller number divided by main stem number, reflecting plant
consistency at 4\textsuperscript{th} true leaf emerging stage as each
plant usually has only one tiller. Plants were harvested at true
leaf-age six for biomass measurement (at 25-27 days post-seeding). The
plants were separated into leaf, sheath, and new-born tillers (with less
than 3 true leaves) for N content analysis using Kjeldhal method with an
automatic Kjeltec 8400 Analyzer Unit (Foss Analytical AB, H\selectlanguage{ngerman}öganäs,
Sweden) following the manufacturer's recommendations. For carbohydrates
measurement, plants were further separated into new-born tillers, leaves
(leaf positions 1-5), or sheaths (upper and lower parts). Carbohydrates
were measured by anthrone reagent (Zhang et al, 2019).

\section*{2.3 Samples for RNA sequencing and bioinformatics
analysis}

{\label{samples-for-rna-sequencing-and-bioinformatics-analysis}}

Samples for RNA extraction and sequencing were collected at true
leaf-age four: the third leaf and tissues near shoot apical meristem
(SAM, after removal of outside 2 layers of sheath keeping about ±3 mm of
the growth point) were collected swiftly on ice and snap frozen in
liquid N\textsubscript{2}, then stored at -80°C till use. Tillers are
supposed to start to appear from the leaf axillary zone at this stage,
therefore, the SAM were collected for the RNA sequencing to reflect
tiller-related genes profile. RNA extraction, pre-treatment, sequencing
library generation and bioinformatics algorithm all followed protocols
of a previous report (Zhang et al, 2019). We chose four treatments from
two {[}CO\textsubscript{2}{]} combined with two N application rates (N0
and N10) for RNA sequencing analysis. Three independent biological
replication samples for each treatment was sequenced, and the acquired
RNA sequencing data were no less than 6 Gb bases per sample, which were
qualified for gene expression analysis (Table S1).

The filtered data were used in the mapping and expression analysis
following previous protocol (Trapnell et al, 2012). The FPKM (Fragments
Per Kilo base transcript per Million mapped reads) value was used to
indicate the expression level of a gene, which was calculated by this
equation: total exon reads / (mapped reads (millions) * exon length
(kilo base)). Differentially expressed gene (DEG) was defined as genes
showing the ratio of average FPKM between two groups surpassed two-fold
(\selectlanguage{english}[?]2, or \selectlanguage{english}[?]0.5) and the adjusted false discovery rate (adj FDR)
being\emph{p} \selectlanguage{english}[?]0.05.

\section*{2.4 Statistical analysis}

{\label{statistical-analysis}}

The generalized linear model (GLM) multivariate procedure from SPSS
software (Ver22, IBM, Armonk, NY, USA) was used for the ANOVA to test
the effects among the treatments. The CO\textsubscript{2} level, N
supply rate and tissue type were defined as independent factors, and
their full interactions were analyzed using the GLM procedure.
Significance level was set at (\emph{p} \selectlanguage{english}[?]0.05) for post-hoc test. Data
presented in figures and tables were the average +- standard deviation.

\section*{3. Results}

{\label{results}}

\section*{\texorpdfstring{3.1 Interactive effects of
eCO\textsubscript{2}and N application rate on
tillering}{3.1 Interactive effects of eCO2and N application rate on tillering}}

{\label{interactive-effects-of-eco2-and-n-application-rate-on-tillering}}

Compared with the ambient {[}CO\textsubscript{2}{]},
eCO\textsubscript{2} significantly increased the number of tillers (Fig.
1A). In respect to N application, the number of tillers increased with N
application rate, with more enhancement at leaf-age six than at leaf-age
four. Under the higher N application, eCO\textsubscript{2} stimulated
more tillers (Fig 1A). Tiller occurrence percentage and uniformity were
also enhanced by eCO\textsubscript{2} at leaf-age four (Fig. 1B),
especially under eCO\textsubscript{2} and higher N application.
Apparently, the tiller enhancement effect by
eCO\textsubscript{2}depended on N application rate, indicating that the
N availability constrained tillering, especially under
eCO\textsubscript{2}.

Similarly, eCO\textsubscript{2} increased biomass more at the higher
than lower N application rate (Fig. 1C). Though N application promoted
biomass accumulation in both the ambient and
eCO\textsubscript{2}conditions, the biomass accumulation reached a
plateau at the N10 treatment under the ambient CO\textsubscript{2}
condition. At the N15 treatment, biomass still showed a significant
increment under the eCO\textsubscript{2} condition (Fig. 1C). It can be
concluded that eCO\textsubscript{2} promoted biomass accumulation,
especially when combined with higher N application rate.

\section*{\texorpdfstring{3.2 Relative distribution change of N to
new-born tillers under
eCO\textsubscript{2}}{3.2 Relative distribution change of N to new-born tillers under eCO2}}

{\label{relative-distribution-change-of-n-to-new-born-tillers-under-eco2}}

Higher N application rate increased N content in both leaf and sheath,
regardless of CO\textsubscript{2} condition (Fig. 2 A and B). However,
eCO\textsubscript{2} reduced the N content consistently under all N
application rates, compared to the ambient
CO\textsubscript{2}treatments. It is worthy to mention that under the
ambient CO\textsubscript{2} condition, N content reached a plateau at
the N10 treatment, whereas under the eCO\textsubscript{2} it still
increased at the N15, regardless of tissue type (leaf or sheath). This
further showed that N application rate was more a constraint to rice
growth at the eCO\textsubscript{2} than at the ambient
CO\textsubscript{2} condition.

Contrary to the reductions of N contents in the leaf and sheath of rice
plant, the new-born tillers under the eCO\textsubscript{2} tended to
have higher N content than under the ambient CO\textsubscript{2} (Fig.
2C). Further comparison of their relative changes clearly demonstrated,
contrasting to the dramatic decrements of N content in both leaf and
sheath, the new-born tillers under the eCO\textsubscript{2} showed a
slight increment (Fig. 2D), suggesting distribution favored the new-born
tillers. A plausible explanation was that a higher proportion of N
distributed to the new-born tillers under eCO\textsubscript{2} than
under ambient CO\textsubscript{2} condition.

\section*{\texorpdfstring{3.3 Carbohydrate accumulation promoted by the
eCO\textsubscript{2}}{3.3 Carbohydrate accumulation promoted by the eCO2}}

{\label{carbohydrate-accumulation-promoted-by-the-eco2}}

Under the ambient CO\textsubscript{2} condition, soluble sugar content
reduced significantly when N application rate increased from N0 to N5
and N10. Further enrichment to N15 significantly increased the soluble
sugar content. However, the starch content was relatively stable (Fig.
3A, B, C and D). Likewise, under the eCO\textsubscript{2}, enrichment of
N from N0 to N5 reduced soluble sugar, but further N enrichment to N10
or N15 increased soluble sugar content. In contrast, the starch content
under the eCO\textsubscript{2} was consistently high under all N
application rates (Fig. 3E, F, G and H). The new-born tillers under the
eCO\textsubscript{2} had higher contents of both soluble sugar and
starch than the ambient CO\textsubscript{2}, implying that relative
higher distribution of N content in them was not a result of
carbohydrate drainage. Overall, under the eCO\textsubscript{2}condition,
all tissues contained higher carbohydrate content than the
ambientCO\textsubscript{2}, regardless of N application rate.

\section*{\texorpdfstring{3.3 Limited transcriptomic change induced by
eCO\textsubscript{2} than by N application
rate}{3.3 Limited transcriptomic change induced by eCO2 than by N application rate}}

{\label{limited-transcriptomic-change-induced-by-eco2-than-by-n-application-rate}}

To understand the underlying transcriptional changes in response to the
eCO\textsubscript{2}, we employed RNA sequencing approach to compare the
gene expression profiles of tissues near the SAM (where the tiller was
about to appear) and the third true leaf at leaf-age four. The total
number of DEG introduced by tissue type (leaf and SAM), N application
rate (N0 and N10), and CO\textsubscript{2} treatment (ambient
CO\textsubscript{2} and eCO\textsubscript{2}) were strikingly different
(Fig. 4A). Tissue had the highest effect, followed by N application
rate, and eCO\textsubscript{2}. The N application had more influences on
leaf than SAM, similar to the impact of CO\textsubscript{2} (Fig. 4A).

Using the Venn diagram, we found that the universal DEG (commonly
altered genes) introduced by tissue was the most (11221, Fig. 4B),
followed by N application rate (197, Fig. 4C), and lastly by
CO\textsubscript{2} effect (5, Fig. 4D). These numbers indicated that
the global gene expression profile was overwhelmingly defined by the
tissue type, only being altered at limited range by N application rate,
and at a very narrow scope by {[}CO\textsubscript{2}{]}.

\section*{\texorpdfstring{3.4 Tiller genes displayed a more wide-scale
change by eCO\textsubscript{2} than by N application
rate}{3.4 Tiller genes displayed a more wide-scale change by eCO2 than by N application rate}}

{\label{tiller-genes-displayed-a-more-wide-scale-change-by-eco2-than-by-n-application-rate}}

To elaborate their responses to eCO\textsubscript{2} and N application
rate, we extracted the transcription data of 65 tiller-related genes
from the transcriptome. Among the 51 genes that showed decent expression
(FPKM\textgreater{}0.1) in leaf and/or SAM at 4\textsuperscript{th} true
leaf emerging stage, there were 50 genes varied between tissues (leaf
and SAM), 29 genes being altered by {[}CO\textsubscript{2}{]}, and 24
genes being significantly affected by N application (Table 1). This
further corroborated that tissue type (organ) predominantly defines the
gene expression profile. However, as there were more tiller genes being
responsive to {[}CO\textsubscript{2}{]} than N application, the
{[}CO\textsubscript{2}{]} was more influential in regulating the
tiller-related genes than the N application. Of the 29
{[}CO\textsubscript{2}{]} responsive tiller-genes and 24 N application
responsive tiller-genes, there were 16 genes being commonly responsive
to both factors. Eight out of the 22 (36.4\%) tiller genes were not
responsive to {[}CO\textsubscript{2}{]} but to N application, whereas 13
out of 27 (48.1\%) tiller genes were not responsive to N but to
CO\textsubscript{2}. This further supported that the
{[}CO\textsubscript{2}{]} was more effective in specifically altering
tiller-related genes than the N application, contrary to their
differential influences to the global transcriptomic profile.

The relative FPKM changes of these tiller genes introduced by N
application (N10/N0), and by {[}CO\textsubscript{2}{]}
(eCO\textsubscript{2}/Ambient) in different tissues were shown in Table
2. N application caused more dramatic change in the leaf than in the
SAM: 14 (7 up, 7 down) and 12 (11 up, 1 down) tiller genes surpassed
two-fold threshold to be DEG under the ambient and the
eCO\textsubscript{2} conditions in the leaf, respectively. In the SAM,
these numbers were three (all down) and 0 for the ambient and
eCO\textsubscript{2}, respectively. Direct comparison of the
{[}CO\textsubscript{2}{]} effect showed that 16 (8 up and 8 down) and
seven (down) tiller-genes surpassed two-fold in leaf at N0 and N10,
respectively; while these values were seven (6 up and 1 down) and two
(up) in SAM. These indicated that either most of them were not
participating in the tillering response to eCO\textsubscript{2}, or the
scale of change did not need to surpass the two-fold to affect
tillering. This also implies that other genes might be involved in
tillering promotion in response to eCO\textsubscript{2}.

\section*{3.5 N transporters and glutamate receptors were differentially
altered in the leaf and
SAM}

{\label{n-transporters-and-glutamate-receptors-were-differentially-altered-in-the-leaf-and-sam}}

Since the N distribution showed dramatic change, we specifically
screened for the N metabolism-related gene expression in the
transcriptome profile. Of the 210 N-related genes, 164 had expression
(FPKM\textgreater{}0.1). Among them, 158 genes were significantly
impacted by the tissue type, 109 genes were affected by N application
rate, and 94 were altered by {[}CO\textsubscript{2}{]} (Table S2).
Apparently, eCO\textsubscript{2} exerted a less influential effect to
this specific category of genes than the N application\emph{.} Further
ascribing the putative functions of those being significantly affected
by {[}CO\textsubscript{2}{]} had revealed that 20 of the 94 (21.3\%)
were glutamate receptors and nitrate or ammonium transporters. In
contrast, only seven of 70 (10\%) CO\textsubscript{2} non-responsive
genes belonged to these groups. It seems that more N metabolite
receptors and transporters were specifically altered by
eCO\textsubscript{2}, suggesting that N redistribution/reallocation may
become more active under the eCO2. Among them, nine genes displayed
contrasting induction and suppression changes between the leaf and SAM
(Fig. 5). They were putative major facilitator family transporter
(NRT2.3a/b, LOC\_Os01g50820, Fig. 5A), ammonium transporters
(LOC\_Os01g61510, LOC\_Os02g34580, and LOC\_Os03g62200, Fig. 5B-D), high
affinity nitrate transporter (LOC\_Os04g40410, Fig. 5E) and peptide
transporter PTR2 (NRT1.1a/b, LOC\_Os10g40600, Fig. 5F). The others were
all putative glutamate receptors (LOC\_Os06g08910, LOC\_Os09g26144, and
LOC\_Os09g26160, Fig. 5G-H). These genes were likely the critical
contributor to the change of N distribution among organs in the
eCO\textsubscript{2} adaptation process in rice plant.

\section*{4. Discussion}

{\label{discussion}}

Significant effects of {[}CO\textsubscript{2}{]} and N application rate
and their interaction on tillering were founded in this study (Fig. 1).
Gradual N enrichment promoted tillering under both ambient
CO\textsubscript{2} and eCO\textsubscript{2} confirmed that N
availability was a constraint to the rice tillering process. However, N
promotion effect on tillering diminished at the N10 under the ambient
CO\textsubscript{2}, but still showed an enhancement effect at the N15
under the eCO\textsubscript{2}. These differential responses suggested
that N limitation was aggravated under the eCO\textsubscript{2}. This
was further corroborated by the declining N content in leaf and sheath
and the increment of carbohydrates under the eCO\textsubscript{2}. It
implies that more N input is needed to take advantage of the
CO\textsubscript{2} fertilization effect in future
eCO\textsubscript{2}condition in order to achieve more tillers. The
new-born tillers actually had comparable or higher N contents under the
eCO\textsubscript{2} condition than under the ambient
CO\textsubscript{2} (Fig. 2), whereas leaf and sheath showed a
significant decline of N content. The more dramatic decline of N content
in the sheath than in the leaf was consistent with previous findings
(Jilta et al, 1997; Huang et al, 2004). Therefore, there could be a
redistribution or reallocation of N favoring towards the SAM (new-born
tillers include novel SAM) under the eCO\textsubscript{2}. As the N
constraint is persistent in an unmanaged niche (Reich et al, 2006;
Mueller et al, 2013), the plants likely adapt to distribute more of
their acquired N to cell proliferating active zones rather than
maintaining the original distribution pattern. This implies that future
production requires more N input to take advantage of
eCO\textsubscript{2}, detail may vary in different species and
varieties.

To reveal the molecular mechanism underlying this change, we conducted
RNA-sequencing for a complete profile of transcriptomic alteration
between the SAM and leaf tissues under four combinations of
{[}CO\textsubscript{2}{]} and N application (Fig. 4). Though tissue
differences predominantly defined the transcription profile globally, N
application generally showed a more influential effect than the
{[}CO\textsubscript{2}{]}. This is reasonable as N has been proved to be
a major limiting factor in plant growth regulation (Xuan et al, 2017;
Zhang et al, 2019). For a specific group of the N metabolism related
genes, they displayed a consistency with the general transcriptomic
differences, i.e., N application overrode the {[}CO\textsubscript{2}{]}
influences as a more significant effector. However, when tiller-related
genes were specifically picked out, they were affected more by the
{[}CO\textsubscript{2}{]} than by the N application. We speculate that
the redistribution of N favoring the SAM under the
eCO\textsubscript{2}strengthened the impact on tiller-related gene
regulations.

Further check on the putative functions of those N metabolism related
genes altered by eCO\textsubscript{2} revealed that a major portion of N
receptors and transporters was inversely regulated in leaf and SAM.
Among them, the LOC\_Os10g40600 (Fig. 5F) encoded OsNRT1.1a/b, which
have been proved to function in nitrate sensing, transport and
remobilization (Hu et al, 2015; Fan et al, 2016; Wang et al, 2018b). In
addition, there might be other genes that regulating the N signaling and
transportation (Feng et al, 2011; Wang et al, 2019c) in response to
eCO\textsubscript{2}. As evidenced here, gene
\emph{OsNRT2.3a/b}(LOC\_Os01g50820, Fig. 5A) had a similar expression
level (FPKM) with\emph{OsNRT1.1a/b} , and two glutamate receptors
(LOC\_Os06g08910 and LOC\_Os09g26160, Fig. 5G and I, respectively) also
showed decent expression levels in both leaf and SAM, which were
differently regulated in the tissues by {[}CO\textsubscript{2}{]} (Fig.
5). This suggests that they were coordinately regulated in the procedure
of eCO\textsubscript{2} adaptation. There is an integrative
co-expression network that determines the N effect and NUE (Zhang et al,
2019.). Engineering one target gene could bring in certain effect on
crop yield improvement or NUE (Zeng et al, 2017; Xu et al, 2019).
However, stacking (pyramiding) more targets together, especially targets
in certain important network pathways may eventually create substantial
yield breakthrough. Our results here provided a list of potential
targets for such an approach.

Knowledge of the underlying mechanism of eCO\textsubscript{2} adaptation
in crops can provide us useful information for breeders in genetic
engineering (Schimel, 2006; Ainsworth and Ort, 2010; Fletcher, 2018).
Our preliminary results showed that the N sensor and transporter genes
play important roles in the response to eCO\textsubscript{2}. We believe
that our results can be applied to certain other plants, as under the
eCO\textsubscript{2} conditions, the element stoichiometry reveals a
generally consistent reduction of N content in most terrestrial plants,
but their growth and biomass accumulation are accelerated to a variating
degree (Reich et al, 2006; Norby et al, 2010; Deng et al, 2015). The
plausible reason probably lies in that plants redistribute N to the
division active zones such as SAM and new-branches in a different way.
This might be a universal adaptation strategy in most terrestrial
plants. Consequently, genetic engineering on these targets that
facilitate the N redistribution may improve the
CO\textsubscript{2}harness and yield in those plants.

In summary, eCO\textsubscript{2} increased carbohydrates in all organs
of rice plant and lowered N content in leaves and sheath, as expected.
However, the new-born tillers maintained N content comparable to or
higher than those under control CO\textsubscript{2} condition. The
redistribution among the organs was likely accomplished by coordinated
regulations of N metabolism receptor and transporter expression in leaf
and SAM. The preliminary understanding of the mechanism provides a group
of putative genes for further functional investigation and breeding
targets.

\section*{Acknowledgements}

{\label{acknowledgements}}

\emph{Not applicable} .

\section*{Funding}

{\label{funding}}

\emph{This work was financially supported by China National Natural
Science Foundation (31571608), Natural Science Fund for Colleges and
Universities in Jiangsu Province (15KJA210003), Jiangsu Provincial
Natural Science Foundation (BK20151311), Jiangsu Provincial Agriculture
Innovation Program (CX(16)1001), Jiangsu Provincial Fund for Application
of Research Achievement (BA2016173) and the open funds of the Key
Laboratory of Plant Functional Genomics of the Ministry of education
(ML201903).}

\section*{Conflict of Interest
Statement}

{\label{conflict-of-interest-statement}}

\emph{All authors have no conflict of interest to declare.}

\section*{Author Contributions}

{\label{author-contributions}}

\emph{JZ, YY, JH and YW: Conceptualization; JZ, YG, JW, ZW, ML, CC, YZ,
XZ and GD: Investigation, Data curation and Analysis, Visualization and
Validation; JZ and YY: Original draft; YY, DH and ZY: Review and
Editing.}

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\section*{Table 1}

{\label{table-1}}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
\textbf{Table 1. The significance level of tiller-related gene affected
by {[}CO2{]}, N rate and} \textbf{organ.} & \textbf{Table 1. The
significance level of tiller-related gene affected by {[}CO2{]}, N rate
and} \textbf{organ.} & \textbf{Table 1. The significance level of
tiller-related gene affected by {[}CO2{]}, N rate and} \textbf{organ.} &
\textbf{Table 1. The significance level of tiller-related gene affected
by {[}CO2{]}, N rate and} \textbf{organ.} & \textbf{Table 1. The
significance level of tiller-related gene affected by {[}CO2{]}, N rate
and} \textbf{organ.} & \textbf{Table 1. The significance level of
tiller-related gene affected by {[}CO2{]}, N rate and} \textbf{organ.} &
\textbf{Table 1. The significance level of tiller-related gene affected
by {[}CO2{]}, N rate and} \textbf{organ.} & \textbf{Table 1. The
significance level of tiller-related gene affected by {[}CO2{]}, N rate
and} \textbf{organ.} & \textbf{Table 1. The significance level of
tiller-related gene affected by {[}CO2{]}, N rate and} \textbf{organ.} &
&\tabularnewline
\midrule
\endhead
Gene name & LOC\_ID\# & Significancy level at \emph{P} value &
Significancy level at \emph{P} value & Significancy level at \emph{P}
value & Significancy level at \emph{P} value & Significancy level at
\emph{P} value & Significancy level at \emph{P} value & Significancy
level at \emph{P} value & Significancy level at \emph{P} value &
Significancy level at \emph{P} value\tabularnewline
& & {[}CO2{]} & N rate & Organ & {[}CO2{]}*N rate & {[}CO2{]} * Organ &
N * Organ & {[}CO2{]} * N * Organ & {[}CO2{]} * N * Organ
&\tabularnewline
RFL & Os04g51000 & 0.000 & 0.034 & 0.000 & 0.005 & 0.000 & 0.030 & 0.005
& 0.005 &\tabularnewline
OsRPK1 & Os05g40770 & 0.000 & 0.000 & 0.000 & 0.001 & 0.000 & 0.000 &
0.001 & 0.001 &\tabularnewline
DLT & Os06g03710 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000
& 0.000 &\tabularnewline
BES1/BZR1/OsBZR1 & Os07g39220 & 0.015 & 0.001 & 0.000 & 0.000 & 0.000 &
0.001 & 0.001 & 0.001 &\tabularnewline
D11 & Os04g39430 & 0.001 & 0.000 & 0.000 & 0.008 & 0.028 & 0.000 & 0.280
& 0.280 &\tabularnewline
CGMC\_GSK.4 & Os02g14130 & 0.034 & 0.000 & 0.000 & 0.005 & 0.046 & 0.090
& 0.249 & 0.249 &\tabularnewline
D2 & Os01g10040 & 0.003 & 0.000 & 0.000 & 0.000 & 0.052 & 0.019 & 0.000
& 0.000 &\tabularnewline
TAL & Os01g70170 & 0.003 & 0.000 & 0.000 & 0.004 & 0.950 & 0.014 & 0.198
& 0.198 &\tabularnewline
LAX1 & Os01g61480 & 0.014 & 0.013 & 0.000 & 0.058 & 0.021 & 0.020 &
0.039 & 0.039 &\tabularnewline
BRD1 & Os03g40540 & 0.022 & 0.005 & 0.000 & 0.157 & 0.006 & 0.000 &
0.133 & 0.133 &\tabularnewline
OsSPL14/IPA1/WFP & Os08g39890 & 0.015 & 0.000 & 0.000 & 0.206 & 0.016 &
0.000 & 0.205 & 0.205 &\tabularnewline
qPN1 & Os01g70550 & 0.000 & 0.000 & 0.000 & 0.636 & 0.000 & 0.000 &
0.388 & 0.388 &\tabularnewline
OsNAC2 & Os08g06140 & 0.000 & 0.028 & 0.000 & 0.124 & 0.000 & 0.006 &
0.105 & 0.105 &\tabularnewline
GID2/14-3-3 & Os02g36974 & 0.011 & 0.013 & 0.000 & 0.119 & 0.125 & 0.094
& 0.009 & 0.009 &\tabularnewline
slr1 & Os03g49990 & 0.037 & 0.000 & 0.000 & 0.114 & 0.565 & 0.270 &
0.015 & 0.015 &\tabularnewline
sdg/GID1 & Os05g33730 & 0.007 & 0.006 & 0.000 & 0.742 & 0.898 & 0.002 &
0.517 & 0.517 &\tabularnewline
MOC1 & Os06g40780 & 0.015 & 0.676 & 0.016 & 0.624 & 0.087 & 0.024 &
0.118 & 0.118 &\tabularnewline
OsWOX4 & Os04g55590 & 0.000 & 0.293 & 0.000 & 0.019 & 0.000 & 0.259 &
0.016 & 0.016 &\tabularnewline
Lazy1 (La1) & Os11g29840 & 0.000 & 0.147 & 0.000 & 0.000 & 0.000 & 0.153
& 0.000 & 0.000 &\tabularnewline
D14/D88/HTD2 & Os03g10620 & 0.000 & 0.906 & 0.000 & 0.000 & 0.000 &
0.888 & 0.008 & 0.008 &\tabularnewline
D3/OsFBL27 /AtMAX2/AtOER9 & Os06g06050 & 0.000 & 0.227 & 0.038 & 0.001 &
0.000 & 0.227 & 0.231 & 0.231 &\tabularnewline
LIC & Os06g49080 & 0.002 & 0.741 & 0.000 & 0.012 & 0.407 & 0.541 & 0.903
& 0.903 &\tabularnewline
OsEXP4 & Os05g39990 & 0.047 & 0.307 & 0.000 & 0.006 & 0.051 & 0.314 &
0.009 & 0.009 &\tabularnewline
BRI1/D61 & Os01g52050 & 0.002 & 0.263 & 0.000 & 0.001 & 0.090 & 0.002 &
0.005 & 0.005 &\tabularnewline
D10 & Os01g54270 & 0.006 & 0.534 & 0.000 & 0.181 & 0.005 & 0.623 & 0.192
& 0.192 &\tabularnewline
OsPIN1 & Os02g07630 & 0.036 & 0.181 & 0.000 & 0.661 & 0.030 & 0.963 &
0.575 & 0.575 &\tabularnewline
OsBIN2 & Os01g10840 & 0.045 & 0.414 & 0.000 & 0.215 & 0.025 & 0.001 &
0.012 & 0.012 &\tabularnewline
APC/C(TE) & Os03g03150 & 0.010 & 0.236 & 0.000 & 0.239 & 0.059 & 0.008 &
0.000 & 0.000 &\tabularnewline
CRCT & Os05g51690 & 0.003 & 0.119 & 0.361 & 0.040 & 0.012 & 0.027 &
0.003 & 0.003 &\tabularnewline
D17/HTD1 & Os04g46470 & 0.069 & 0.000 & 0.000 & 0.004 & 0.027 & 0.000 &
0.033 & 0.033 &\tabularnewline
BAK1 & Os08g07760 & 0.356 & 0.000 & 0.000 & 0.015 & 0.005 & 0.001 &
0.079 & 0.079 &\tabularnewline
OsCDC27 & Os06g41750 & 0.443 & 0.002 & 0.000 & 0.354 & 0.018 & 0.000 &
0.403 & 0.403 &\tabularnewline
RCN1/OsABCG5 & Os03g17350 & 0.089 & 0.000 & 0.000 & 0.161 & 0.140 &
0.002 & 0.356 & 0.356 &\tabularnewline
OsHAP2E & Os03g29760 & 0.737 & 0.001 & 0.000 & 0.862 & 0.564 & 0.003 &
0.112 & 0.112 &\tabularnewline
IBH1 & Os04g56500 & 0.862 & 0.016 & 0.000 & 0.126 & 0.760 & 0.133 &
0.927 & 0.927 &\tabularnewline
SD1 & Os01g66100 & 0.260 & 0.000 & 0.000 & 0.078 & 0.323 & 0.137 & 0.235
& 0.235 &\tabularnewline
CGMC\_GSK.8 & Os06g35530 & 0.179 & 0.000 & 0.000 & 0.692 & 0.500 & 0.407
& 0.489 & 0.489 &\tabularnewline
LAX2 & Os04g32510 & 0.052 & 0.247 & 0.000 & 0.343 & 0.009 & 0.339 &
0.414 & 0.414 &\tabularnewline
OsMADS57 & Os02g49480 & 0.724 & 0.829 & 0.000 & 0.501 & 0.047 & 0.108 &
0.016 & 0.016 &\tabularnewline
LRK1 & Os02g05980 & 0.152 & 0.816 & 0.000 & 0.753 & 0.050 & 0.830 &
0.014 & 0.014 &\tabularnewline
ILI1 & Os04g54900 & 0.336 & 0.301 & 0.030 & 0.749 & 0.336 & 0.301 &
0.749 & 0.749 &\tabularnewline
GDH7 & Os07g15770 & 0.817 & 0.357 & 0.000 & 0.312 & 0.817 & 0.357 &
0.312 & 0.312 &\tabularnewline
OsPIN2 & Os06g44970 & 0.088 & 0.912 & 0.000 & 0.841 & 0.055 & 0.721 &
0.963 & 0.963 &\tabularnewline
BKI1 & Os09g28550 & 0.677 & 0.179 & 0.001 & 0.223 & 0.594 & 0.078 &
0.341 & 0.341 &\tabularnewline
OsTB1/fc1 & Os03g49880 & 0.173 & 0.843 & 0.000 & 0.303 & 0.213 & 0.775 &
0.365 & 0.365 &\tabularnewline
GA2ox1 & Os05g06670 & 0.344 & 0.264 & 0.000 & 0.922 & 0.368 & 0.264 &
0.921 & 0.921 &\tabularnewline
OsAPC10 & Os05g50360 & 0.156 & 0.831 & 0.000 & 0.822 & 0.103 & 0.856 &
0.487 & 0.487 &\tabularnewline
D63 & Os08g01110 & 0.111 & 0.908 & 0.000 & 0.568 & 0.260 & 0.281 & 0.203
& 0.203 &\tabularnewline
DEP1 & Os09g26999 & 0.247 & 0.159 & 0.000 & 0.774 & 0.113 & 0.370 &
0.641 & 0.641 &\tabularnewline
OsFEN-1 & Os05g46270 & 0.598 & 0.543 & 0.000 & 0.690 & 0.316 & 0.665 &
0.203 & 0.203 &\tabularnewline
OsH1 & Os03g51690 & 0.266 & 0.761 & 0.000 & 0.306 & 0.236 & 0.816 &
0.297 & 0.297 &\tabularnewline
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the factors. &
Note: Probability level equal or less than 0.05 are displayed in red.
{[}CO2{]}*N rate, {[}CO2{]}*Organ, N rate*Organ, and {[}CO2{]}*N
rate*Organ denote the respective interaction effect of the
factors.\tabularnewline
\bottomrule
\end{longtable}

\section*{Table 2}

{\label{table-2}}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\textbf{Table 2. The relative FPKM change of tiller-related genes in
response to {[}CO2{]} and N rate.} & \textbf{Table 2. The relative FPKM
change of tiller-related genes in response to {[}CO2{]} and N rate.} &
\textbf{Table 2. The relative FPKM change of tiller-related genes in
response to {[}CO2{]} and N rate.} & \textbf{Table 2. The relative FPKM
change of tiller-related genes in response to {[}CO2{]} and N rate.} &
\textbf{Table 2. The relative FPKM change of tiller-related genes in
response to {[}CO2{]} and N rate.} & \textbf{Table 2. The relative FPKM
change of tiller-related genes in response to {[}CO2{]} and N rate.} &
\textbf{Table 2. The relative FPKM change of tiller-related genes in
response to {[}CO2{]} and N rate.} & \textbf{Table 2. The relative FPKM
change of tiller-related genes in response to {[}CO2{]} and N rate.} &
\textbf{Table 2. The relative FPKM change of tiller-related genes in
response to {[}CO2{]} and N rate.} & & & &\tabularnewline
\midrule
\endhead
Gene name & LOC\_ID\# & Ambient & Ambient & eCO2 & eCO2 & Leaf & Leaf &
Leaf & SAM & SAM & SAM & SAM\tabularnewline
& & Leaf & SAM & Leaf & SAM & SAM & N0 & N10 & N10 & N0 & N10
&\tabularnewline
& & N10/N0 & N10/N0 & N10/N0 & N10/N0 & N10/N0 & eCO2/Amb & eCO2/Amb &
eCO2/Amb & eCO2/Amb & eCO2/Amb &\tabularnewline
RFL & Os04g51000 & & 0.64 & & 1.13 & 1.13 & & & & 0.45 & 0.80
&\tabularnewline
OsRPK1 & Os05g40770 & 3.46 & 0.15 & 5.98 & 1.03 & 1.03 & 2.49 & 4.29 &
4.29 & 0.85 & 5.73 &\tabularnewline
DLT & Os06g03710 & 2.45 & 0.60 & 1.21 & 0.85 & 0.85 & 3.95 & 1.94 & 1.94
& 0.72 & 1.01 &\tabularnewline
BES1/BZR1/OsBZR1 & Os07g39220 & 0.94 & 0.69 & 1.10 & 1.00 & 1.00 & 1.42
& 1.66 & 1.66 & 0.72 & 1.03 &\tabularnewline
D11 & Os04g39430 & 0.70 & 0.75 & 1.74 & 0.87 & 0.87 & 0.43 & 1.07 & 1.07
& 0.82 & 0.95 &\tabularnewline
CGMC\_GSK.4 & Os02g14130 & 0.86 & 0.90 & 0.94 & 0.97 & 0.97 & 0.87 &
0.94 & 0.94 & 0.97 & 1.04 &\tabularnewline
D2 & Os01g10040 & 0.36 & 0.42 & 0.75 & 1.42 & 1.42 & 0.59 & 1.22 & 1.22
& 0.48 & 1.64 &\tabularnewline
TAL & Os01g70170 & 2.15 & 1.35 & 4.36 & 1.42 & 1.42 & 0.79 & 1.61 & 1.61
& 1.03 & 1.09 &\tabularnewline
LAX1 & Os01g61480 & & 1.55 & & 1.04 & 1.04 & & & & 1.54 & 1.04
&\tabularnewline
BRD1 & Os03g40540 & 0.80 & 1.33 & 0.81 & 1.11 & 1.11 & 0.90 & 0.91 &
0.91 & 1.27 & 1.06 &\tabularnewline
OsSPL14/IPA1/WFP & Os08g39890 & 1.28 & 1.44 & 1.21 & 1.24 & 1.24 & 2.08
& 1.97 & 1.97 & 1.23 & 1.06 &\tabularnewline
qPN1 & Os01g70550 & 1.62 & 1.17 & 1.80 & 1.44 & 1.44 & 0.66 & 0.73 &
0.73 & 0.94 & 1.16 &\tabularnewline
OsNAC2 & Os08g06140 & 2.11 & 0.97 & 1.10 & 0.97 & 0.97 & 2.82 & 1.47 &
1.47 & 1.02 & 1.03 &\tabularnewline
GID2/14-3-3 & Os02g36974 & 1.00 & 1.04 & 1.33 & 0.99 & 0.99 & 0.99 &
1.32 & 1.32 & 1.04 & 1.00 &\tabularnewline
slr1 & Os03g49990 & 1.58 & 1.02 & 1.33 & 1.23 & 1.23 & 0.91 & 0.76 &
0.76 & 0.85 & 1.03 &\tabularnewline
sdg/GID1 & Os05g33730 & 0.92 & 0.79 & 1.16 & 0.80 & 0.80 & 1.28 & 1.61 &
1.61 & 1.11 & 1.11 &\tabularnewline
MOC1 & Os06g40780 & 2.52 & 0.53 & 1.39 & 0.81 & 0.81 & 1.12 & 0.62 &
0.62 & 0.38 & 0.58 &\tabularnewline
OsWOX4 & Os04g55590 & & 1.12 & & 0.82 & 0.82 & & & & 1.62 & 1.19
&\tabularnewline
Lazy1 (La1) & Os11g29840 & 0.53 & 0.64 & 3.59 & 1.49 & 1.49 & 0.16 &
1.09 & 1.09 & 0.41 & 0.96 &\tabularnewline
D14/D88/HTD2 & Os03g10620 & 0.78 & 0.50 & 2.04 & 1.95 & 1.95 & 0.22 &
0.56 & 0.56 & 0.35 & 1.37 &\tabularnewline
D3/OsFBL27 /AtMAX2/AtOER9 & Os06g06050 & 0.71 & 0.86 & 1.31 & 1.17 &
1.17 & 0.36 & 0.66 & 0.66 & 0.82 & 1.12 &\tabularnewline
LIC & Os06g49080 & 1.24 & 1.07 & 0.87 & 0.91 & 0.91 & 0.88 & 0.62 & 0.62
& 1.00 & 0.86 &\tabularnewline
OsEXP4 & Os05g39990 & 0.43 & 0.72 & 2.29 & 1.21 & 1.21 & 0.38 & 2.01 &
2.01 & 0.65 & 1.09 &\tabularnewline
BRI1/D61 & Os01g52050 & 1.65 & 1.02 & 1.27 & 0.89 & 0.89 & 1.49 & 1.14 &
1.14 & 1.13 & 0.98 &\tabularnewline
D10 & Os01g54270 & 1.43 & 0.90 & 3.29 & 1.20 & 1.20 & 0.35 & 0.80 & 0.80
& 1.23 & 1.64 &\tabularnewline
OsPIN1 & Os02g07630 & 0.96 & 0.95 & 0.95 & 0.99 & 0.99 & 0.86 & 0.84 &
0.84 & 0.98 & 1.02 &\tabularnewline
OsBIN2 & Os01g10840 & 1.12 & 0.99 & 1.36 & 0.91 & 0.91 & 0.88 & 1.07 &
1.07 & 1.09 & 1.01 &\tabularnewline
APC/C(TE) & Os03g03150 & 1.06 & 1.07 & 1.35 & 0.88 & 0.88 & 1.10 & 1.40
& 1.40 & 1.12 & 0.92 &\tabularnewline
CRCT & Os05g51690 & 1.14 & 0.97 & 0.59 & 1.14 & 1.14 & 1.99 & 1.04 &
1.04 & 0.96 & 1.13 &\tabularnewline
D17/HTD1 & Os04g46470 & 0.48 & 0.69 & 0.97 & 0.89 & 0.89 & 0.86 & 1.74 &
1.74 & 0.80 & 1.04 &\tabularnewline
BAK1 & Os08g07760 & 0.95 & 0.86 & 1.03 & 0.95 & 0.95 & 1.07 & 1.17 &
1.17 & 0.91 & 1.01 &\tabularnewline
OsCDC27 & Os06g41750 & 1.44 & 0.98 & 1.69 & 0.99 & 0.99 & 1.00 & 1.17 &
1.17 & 0.94 & 0.94 &\tabularnewline
RCN1/OsABCG5 & Os03g17350 & 1.88 & 1.55 & 3.82 & 1.91 & 1.91 & 0.71 &
1.45 & 1.45 & 1.08 & 1.33 &\tabularnewline
OsHAP2E & Os03g29760 & 1.95 & 0.95 & 1.53 & 1.09 & 1.09 & 1.18 & 0.93 &
0.93 & 0.90 & 1.03 &\tabularnewline
IBH1/OsIBH1 & Os04g56500 & 0.55 & 0.32 & 0.77 & 1.14 & 1.14 & 0.86 &
1.22 & 1.22 & 0.73 & 2.63 &\tabularnewline
SD1 & Os01g66100 & 0.22 & 0.56 & 0.09 & 0.90 & 0.90 & 0.73 & 0.32 & 0.32
& 0.81 & 1.31 &\tabularnewline
CGMC\_GSK.8 & Os06g35530 & 1.31 & 1.13 & 1.44 & 1.12 & 1.12 & 0.98 &
1.08 & 1.08 & 1.04 & 1.02 &\tabularnewline
LAX2 & Os04g32510 & 0.39 & 0.65 & 0.94 & 0.92 & 0.92 & 2.22 & 5.36 &
5.36 & 0.40 & 0.57 &\tabularnewline
OsMADS57 & Os02g49480 & 0.67 & 1.08 & 6.19 & 0.80 & 0.80 & 3.19 & 29.26
& 29.26 & 1.07 & 0.79 &\tabularnewline
LRK1 & Os02g05980 & 1.10 & 0.64 & 0.90 & 1.79 & 1.79 & 0.98 & 0.81 &
0.81 & 0.67 & 1.87 &\tabularnewline
ILI1 & Os04g54900 & & 1.56 & & & & & & & 0.00 & 0.68 &\tabularnewline
GDH7 & Os07g15770 & 0.47 & & 1.04 & & & 0.66 & 1.45 & 1.45 & &
&\tabularnewline
OsPIN2 & Os06g44970 & & 0.96 & & 0.97 & 0.97 & & & & 0.76 & 0.77
&\tabularnewline
BKI1 & Os09g28550 & 0.86 & 0.62 & 1.28 & 0.67 & 0.67 & 0.75 & 1.11 &
1.11 & 0.98 & 1.07 &\tabularnewline
OsTB1/fc1 & Os03g49880 & 0.16 & 0.90 & & 1.23 & 1.23 & 0.00 & 1.04 &
1.04 & 0.69 & 0.95 &\tabularnewline
GA2ox1 & Os05g06670 & & 1.14 & 1.04 & 1.11 & 1.11 & & & & 1.12 & 1.08
&\tabularnewline
OsAPC10 & Os05g50360 & 0.90 & 1.02 & 1.09 & 0.95 & 0.95 & 0.95 & 1.16 &
1.16 & 0.92 & 0.86 &\tabularnewline
D63 & Os08g01110 & 0.93 & 1.08 & 0.99 & 0.98 & 0.98 & 0.85 & 0.91 & 0.91
& 1.04 & 0.94 &\tabularnewline
DEP1 & Os09g26999 & 2.34 & 1.10 & 2.29 & 1.06 & 1.06 & 2.06 & 2.02 &
2.02 & 0.93 & 0.89 &\tabularnewline
OsFEN-1 & Os05g46270 & 1.03 & 1.05 & 2.16 & 0.96 & 0.96 & 0.41 & 0.85 &
0.85 & 1.05 & 0.97 &\tabularnewline
OsH1 & Os03g51690 & 5.71 & 1.06 & 2.38 & 0.97 & 0.97 & 6.16 & 2.57 &
2.57 & 0.99 & 0.91 &\tabularnewline
Note: Red and blue text denote up or down-regulated fold change surpass
two-fold to be DEG, respectively. & Note: Red and blue text denote up or
down-regulated fold change surpass two-fold to be DEG, respectively. &
Note: Red and blue text denote up or down-regulated fold change surpass
two-fold to be DEG, respectively. & Note: Red and blue text denote up or
down-regulated fold change surpass two-fold to be DEG, respectively. &
Note: Red and blue text denote up or down-regulated fold change surpass
two-fold to be DEG, respectively. & Note: Red and blue text denote up or
down-regulated fold change surpass two-fold to be DEG, respectively. &
Note: Red and blue text denote up or down-regulated fold change surpass
two-fold to be DEG, respectively. & Note: Red and blue text denote up or
down-regulated fold change surpass two-fold to be DEG, respectively. &
Note: Red and blue text denote up or down-regulated fold change surpass
two-fold to be DEG, respectively. & & & &\tabularnewline
\bottomrule
\end{longtable}







\section*{Figure legends}

{\label{figure-legends}}

\textbf{Figure 1} . Tiller number at leaf-age 4 and 6, tiller occurrence
percentage, and biomass accumulation in response to N application rate
and CO\textsubscript{2} concentration.

Error bars are standard deviations; Bars with different letters mean
they are significantly different at p[?]0.05.

\textbf{Figure 2} . The responses of N content in leaf, sheath, and
new-born tillers to N application rate and CO\textsubscript{2}conditions
(A, B and C), and their relative change under eCO\textsubscript{2} to
ambient CO\textsubscript{2} condition.

Error bars are standard deviations; Bars with different letters mean
they are significantly different at p[?]0.05

\textbf{Figure 3} . Carbohydrates change in response to N application
rate and CO\textsubscript{2} condition.

L1-L5 represent leaf positions 1-5, respectively; SU and SD represent up
and down part of sheath, respectively; tiller were new-born ones at
leaf-age 6; Error bars are standard deviations; Bars with different
letters mean they are significantly different at p[?]0.05.

\textbf{Figure 4.} The number of differentially expressed genes (DEG)
and Venn diagrams of different comparisons.

A and C represent ambient and enriched CO\textsubscript{2},
respectively; L3 and M stand for leaf number 3 and SAM respectively; 0
and 10 represent N application rate N0 and N10, respectively.

\textbf{Figure 5} . The gene expression (FPKM) in leaf and SAM in
response to N application and CO\textsubscript{2} condition.

L for leaf, M for SAM, Ambient and eCO\textsubscript{2} stand for
ambient and enriched CO\textsubscript{2} treatments, respectively. Error
bars are standard deviations; Bars with different letters mean they are
significantly different at p[?]0.05.

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