Altered cell wall hydroxycinnamate composition impacts leaf- and canopy-level CO₂ uptake and water use in rice

Abstract

Cell wall properties play a major role in determining photosynthetic carbon uptake and water use through their impact on mesophyll conductance (CO₂ diffusion from substomatal cavities into photosynthetic mesophyll cells) and leaf hydraulic conductance (water movement from xylem, through leaf tissue, to stomata). Consequently, modification of cell wall (CW) properties might help improve photosynthesis and crop water use efficiency (WUE). We tested this using 2 independent transgenic rice (Oryza sativa) lines overexpressing the rice OsAT10 gene (encoding a “BAHD” CoA acyltransferase), which alters CW hydroxycinnamic acid content (more para-coumaric acid and less ferulic acid). Plants were grown under high and low water levels, and traits related to leaf anatomy, CW composition, gas exchange, hydraulics, plant biomass, and canopy-level water use were measured. Alteration of hydroxycinnamic acid content led to statistically significant decreases in mesophyll CW thickness (−14%) and increased mesophyll conductance (+120%) and photosynthesis (+22%). However, concomitant increases in stomatal conductance negated the increased photosynthesis, resulting in no change in intrinsic WUE (ratio of photosynthesis to stomatal conductance). Leaf hydraulic conductance was also unchanged; however, transgenic plants showed small but statistically significant increases in aboveground biomass (AGB) (+12.5%) and canopy-level WUE (+8.8%; ratio of AGB to water used) and performed better under low water levels than wild-type plants. Our results demonstrate that changes in CW composition, specifically hydroxycinnamic acid content, can increase mesophyll conductance and photosynthesis in C₃ cereal crops such as rice. However, attempts to improve photosynthetic WUE will need to enhance mesophyll conductance and photosynthesis while maintaining or decreasing stomatal conductance.

Introduction

Water availability is a key factor limiting productivity and plant survival across both natural and managed ecosystems. Ongoing climate change will exacerbate these water limitations, further imposing constraints on crop production. Therefore, any future increases in crop production must be accomplished under existing or even lower water availability. This can be achieved by increasing plant water use efficiency (WUE), defined as the amount of plant biomass produced per unit water used (Leakey et al. 2019). At the leaf level, this means increasing intrinsic WUE (WUE_i) or the ratio of net photosynthetic CO₂ uptake (A_net) to stomatal conductance to water vapor (g_sw). Attempts have been made to enhance WUE_i by lowering g_sw. However, as A_net is positively associated with g_sw, decreasing g_sw often decreases A_net (Leakey et al. 2019). Hence, manipulation of A_net for improved WUE_i must happen independent of g_sw. One possible means to achieve this is by enhancing mesophyll conductance to CO₂ (g_m) to supply a greater CO₂ partial pressure (pCO₂) at the site of Rubisco fixation for a given g_sw (Flexas et al. 2016; Leakey et al. 2019). In fact, g_m relates positively with A_net across diverse terrestrial plant groups (Flexas et al. 2012; Barbour and Kaiser 2016; Pathare, Koteyeva, et al. 2020) and has been identified as the third most important factor limiting A_net, besides stomatal and biochemical limitations (Gago et al. 2020). Also, because the CO₂ diffusion pathways related to g_m are not the same as the pathways of water transpired out of stomata, an increase in g_m is expected to increase A_net without a concurrent increase in g_sw. Consequently, modification of g_m through changes in leaf properties has been proposed as a key path for simultaneously improving crop A_net and WUE_i (Flexas et al. 2016; Leakey et al. 2019; Lundgren and Fleming 2020). However, g_m in C₃ species has been shown to coordinate with leaf hydraulic conductance (K_leaf, the movement of water from xylem, through leaf tissue to stomata) in part due to the common pathway of CO₂ and water movement inside leaves (Flexas, Niinemets, et al. 2013; Xiong et al. 2017) (but see (Loucos et al. 2017; Pathare, Sonawane, et al. 2020)). Studies also report a strong coupling of g_m and g_sw in C₃ species, the mechanisms of which remain unclear (Hanba et al. 2004; Giuliani et al. 2013; Barbour and Kaiser 2016). This suggests that increases in g_m may lead to simultaneous increases in K_leaf and g_sw, thus hampering our efforts to improve WUE_i through modification of g_m. Consequently, identification of leaf traits that enhance g_m, while maintaining K_leaf and g_sw, will be a critical step toward exploiting g_m for improving crop A_net and WUE_i.

Both g_m and K_leaf are complex traits influenced by several leaf structural, anatomical, and biochemical properties (Evans et al. 2009). Among these, mesophyll surface area exposed to intercellular air spaces (IAS) (S_mes), surface area of chloroplasts exposed to IAS (S_c), and mesophyll cell wall (CW) properties (Xiong and Flexas 2018; Pathare, Koteyeva, et al. 2020; Evans 2021; Flexas et al. 2021) have been identified as the strongest determinants of g_m across diverse plant groups. Greater S_mes is expected to increase g_m by increasing the surface area for diffusion of CO₂ inside photosynthetic mesophyll cells whereas greater S_c is expected to increase g_m by decreasing the path length for CO₂ diffusion inside the chloroplast (Evans et al. 2009). S_mes is also expected to play a role in mediating the coordination of g_m and K_leaf, primarily the outside-xylem conductance (K_ox), by increasing the surface area for evaporation of water (Xiong et al. 2017). Mesophyll CWs are another key leaf anatomical feature that exert a major influence on g_m and hence A_net that is equal to or even greater in magnitude than S_mes (Evans 2021; Flexas et al. 2021).

CWs are complex and dynamic biological structures that affect several critical plant functions (Keegstra 2010), including the exchange of and CO₂ and water inside leaves (Evans 2021; Flexas et al. 2021). The structure and composition of CWs vary greatly depending on the plant group (e.g. dicot versus monocot), cell types, and developmental stage (Sarkar et al. 2009). Photosynthetic mesophyll CWs are mainly composed of variable proportions of cellulose, several matric polysaccharides, structural proteins, and other minor components (Sarkar et al. 2009; Cosgrove and Jarvis 2012). The structure, organization, and interactions of these components result in a web of nanometric and micrometric pores that are filled with apoplastic liquid. These pores allow the movement of CO₂ and water between the IAS and mesophyll cells (Evans et al. 2009). Assuming an effective porosity of the CWs of 0.1 to 0.05, mesophyll CWs are expected to account for 25% to 50% of the total mesophyll resistance to CO₂ diffusion, respectively (Evans et al. 2009). This resistance (r_w) can be expressed as $r_{\mathrm{w}}=\frac{T_{\mathrm{cw}}\times \mathrm{\tau }}{D\times \rho }$ (Evans et al. 2009; Evans 2021), where, T_CW is the CW thickness, τ is the tortuosity of the liquid path through the walls, D is the diffusivity of CO₂ in apoplastic liquid, and ρ is the porosity of the wall. Previous studies demonstrate that thicker T_CW increases the apparent diffusion pathlength, thus decreasing g_m (Evans 2021; Flexas et al. 2021). In addition, a few recent studies based on diverse trees and herbaceous dicot species have proposed a role for CW components in determining g_m by affecting T_CW, porosity, and tortuosity (Weraduwage et al. 2016; Roig-Oliver et al. 2020; Flexas et al. 2021; Roig-Oliver et al. 2022). For instance, greater cellulose and hemicellulose content are expected to reduce g_m by increasing tortuosity. In contrast, a relatively higher pectin content and a greater pectin to cellulose and hemicellulose ratio is expected to increase CW hydrophilicity and effective porosity to CO₂, thus increasing g_m (Roig-Oliver et al. 2020; Flexas et al. 2021). Furthermore, lignin deposition is expected to decrease CW porosity and g_m (Roig-Oliver et al. 2020). In addition to the above major components, CW-bound phenolic compounds are also expected to affect g_m and A_net by influencing the chemical tortuosity or any chemical interactions of CO₂ with major CW components (Flexas et al. 2021). The above studies provide substantial information about how and why different CW components can affect CO₂ diffusion inside mesophyll cells and highlight their importance for improving crop g_m and A_net (Evans 2021; Flexas et al. 2021). However, most studies have focused on trees and herbaceous dicots, whereas impacts of different CW components on g_m and A_net have rarely been studied on grasses, which represent many of the crops (Hura et al. 2012; Ellsworth et al. 2018).

Grass CWs differ substantially from dicots in structure and composition (Vogel 2008). The primary CWs of dicots, which we expect to be present in stomatal-facing mesophyll cells, mainly consist of a cellulose–xyloglucan network embedded in a pectin matrix. In contrast, grass primary walls are low in xyloglucan and pectin. Instead, glucuronoarabinoxylans (GAXs) and mixed-linkage glucan are the principal polymers that interact with cellulose microfibrils in grass primary CW (Cosgrove and Jarvis 2012). The extent of modification of the xylan backbone of GAXs with arabinose, glucuronic acid, and acetyl groups positively correlates with hydrogen bonding between cellulose and GAX, with greater modification leading to more interactions with cellulose (Gao et al. 2020; Duan et al. 2021). Another feature that distinguishes grass CWs from those of dicots is the incorporation of hydroxycinnamates like ferulic acid (FA) and para-coumaric acid (p-CA) onto arabinosyl units of GAX (Chandrakanth et al. 2023). FA, but not p-CA, is involved in cross-linking GAX polymers to one another and to the polyphenolic lignin. p-CA is also esterified to lignin present in secondary CW thickenings of leaf vascular bundles, and, at lower levels, FA has also been found on lignin of various species (Ralph 2010; Karlen et al. 2016). Considering their abilities to facilitate cross-linking between GAX chains and between GAX and lignin, the hydroxycinnamate moieties can affect CW properties, including the stiffness and structural integrity of CW and the aggregate property of cellulose enzymatic digestibility, which in turn may relate to wall porosity or “looseness” in grasses (Bartley et al. 2013; Tian et al. 2021). A recent study on rice (Oryza sativa) showed that mixed-linkage glucan, a polysaccharide unique to grasses, plays a key role in determining CW porosity, g_m, and A_net (Ellsworth et al. 2018). However, the impacts of most grass CW components on g_m and A_net remain unknown. A greater understanding of how different grass CW components affect the movement of CO₂ inside photosynthetic mesophyll cells will be critical for making targeted changes in CW properties to improve g_m and A_net of cereal crops.

Mesophyll CW properties are also expected to impact K_leaf or the water movement from the xylem through leaf tissue to stomata (Sack and Holbrook 2006; Buckley et al. 2015). K_leaf can be partitioned into 2 major components: inside- and outside-xylem pathways (K_x and K_ox, respectively). While K_x and traits (like vein density) influencing it are well characterized, K_ox remains poorly understood. The outside-xylem pathways of water movement are highly complex as water travels via apoplastic (CWs) and/or symplastic (cell-to-cell via plasmodesmata) routes and involve multiple leaf tissues including bundle sheath, mesophyll, and epidermis (Buckley et al. 2015). While the relative importance of apoplastic versus symplastic routes in determining K_ox remains controversial, studies suggest that the apoplastic route via CWs is a major contributor to water movement, influenced by CW properties like T_CW. For instance, using model simulations, Buckley et al. (2015) demonstrated that an increase in T_CW can increase K_ox because of decreased resistance in the CW associated with a greater cross-sectional volume for water movement. In addition to T_CW, CW composition is also expected to influence K_leaf (Sack and Holbrook 2006). However, despite the importance of K_leaf in determining A_net and g_sw, little is known about how CW properties influence K_leaf.

Toward understanding the influence of CW properties on g_m and K_leaf in a grass, we used 2 independent transgenic rice lines overexpressing the rice BAHD CoA acyltransferase gene, OsAT10, to test the impacts of CW modification on traits related to cellular-, leaf-, and plant-level CO₂ uptake and water use, including g_m, A_net, and K_leaf. Overexpression of the OsAT10 gene alters rice CW hydroxycinnamic acid content, resulting in greater p-CA and lower FA content and thus increasing degradability of rice biomass (Bartley et al. 2013). Previous studies on triticale (x Triticosecale Wittmack) genotypes also suggest the potential role of these hydroxycinnamic acids in determining productivity and drought tolerance (Hura et al. 2012). Here, we assessed whether modification of CW hydroxycinnamic acids content affects g_m and K_leaf and whether these cellular- and leaf-level changes scale to influence plant-level biomass and WUE. We demonstrated that altered CW hydroxycinnamate composition increased g_m and A_net but not K_leaf. Still, the transgenic plants observed an overall increase in tolerance to water restriction.

Results

To ensure that any observed effects were not due to other genetic modifications independent of AT10 overexpression, we used 2 distinct rice CW transgenics in this study: ZmUbi_pro-AT10 (hereafter, Ubi_pro-AT10 and OsAT10-D1). Overall, we observed similar compositional, physiological, and anatomical changes in Ubi_pro-AT10 transgenics compared with the activation-tagged OsAT10-D1 (OsAT10-Dominant 1). Grass Ubi promoters generally have strong foliar expression, whereas the native AT10 gene has low expression in leaves compared with other related BAHDs (Chandrakanth et al. 2023). Thus, we focused on the Ubi_pro-AT10 lines and measured a larger battery of traits, whereas, for OsAT10-D1, only important physiological and structural traits were measured. Key data for Ubi_pro-AT10 lines is included in the main document (Figs. 1 to 7 and Tables 1 and 2), while other additional data is given in supplement (Supplemental Figs. S1 and S2 and Table S1). For OsAT10-D1 transgenics, leaf anatomy data have been presented as the main figure (Fig. 1), while other data are given in the supplement (Supplemental Figs. S3 to S6 and Tables S2 and S3). Generally, physiological effects in the Ubi_pro-AT10 line were more pronounced than for the OsAT10-D1 line.

Figure 1.

Leaf CW composition for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Ferulic acid content on xylan (FA-Xylan), B) ferulic acid content on lignin (FA-Lignin), C)p-CA content on xylan (p-CA-Xylan), D)p-CA content on lignin (p-CA-Lignin), E) lignin content, F) cellulose content; and G) glucuronic acid content. P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 4 to 5) along with replicate points (small open circles).

Figure 2.

Leaf anatomical traits for rice genotypes: Ubi_pro-AT10 WT (ubi wt; open triangles) and transgenic (ubi_mut; filled triangles) and OsAt10-D1 WT (d1_wt; open triangles) and transgenic (d1_mut; filled triangles). A) Mesophyll CW thickness (T_CW), B) bundle sheath CW thickness (BS_CW), C) mesophyll surface area exposed to IAS (S_mes), D) chloroplast surface area exposed to IAS (S_c), E) leaf thickness, and F) IVD. Leaf anatomical traits were measured only for HW treatment. P values from pair-wise t test are shown. P ≤ 0.05 were considered statistically significant, whereas P ≤ 0.1 were considered as marginally significant. Values indicate mean ± 1 Se (n = 4 to 6) along with replicate points (light gray dots).

Figure 3.

Leaf physiological traits for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Mesophyll conductance to CO₂ (Δ¹³C-g_m), B) net CO₂ assimilation rates (A_net), C) stomatal conductance to water (g_sw), D) ratio of g_m to g_sw, E) ratio of leaf intercellular (CO₂) to (CO₂) in leaf chamber (C_i/C_a), and F) leaf-level WUE (WUE_i = A_net/g_sw). P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 5 to 6) along with replicate points (light gray dots).

Figure 4.

Biochemical traits for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Maximum carboxylation capacity of Rubisco (V_cmax), B) maximum electron transport rate (J_max), C) maximum photosynthetic capacity at saturating pCO₂ (A_max), and D) leaf N content. P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 5 to 6) along with replicate points (light gray dots).

Figure 5.

Relative limitations for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Stomatal (L_s), B) mesophyll (L_m), and C) biochemical (L_b) limitations to photosynthesis. P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 5 to 6) along with replicate points (light gray dots).

Figure 6.

Leaf hydraulic traits for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Leaf hydraulic conductance (K_leaf), B) leaf water potential, C) xylem conductance (K_x), and D) outside-xylem conductance (K_ox). P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 5 to 6) along with replicate points (light gray dots).

Figure 7.

Leaf- and plant-level traits for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (triangles) and LW (circles). A) Total AGB, B) total leaf or transpiring biomass, C) total stem biomass or nontranspiring biomass D) LMA, E) leaf width, F) total water used by plants during the experiment, and G) canopy-level WUE (WUE_canopy). P values from 1-way ANOVA and post hoc Tukey's test letters are shown. Different letters indicate statistically significant differences at P ≤ 0.05. Values indicate mean ± 1 Se (n = 5 to 6) along with replicate points (light gray dots).

Table 1.

Summary of 2-way ANOVA P values and F values for the leaf CW composition, physiology, and biochemical traits associated with CO₂ uptake and water use measured on the rice Ubi_pro-AT10 WT and transgenic lines at a timepoint of 50 to 60 d

Abbreviations	Traits	Genotype		Water		Genotype × water
		P value	F value	P value	F value	P value	F value
FA-Xylan	Ferulic acid on xylan	< 0.001	117.832	0.043	4.849	0.004	11.288
FA-Lignin	Ferulic acid on lignin	< 0.001	21.678	0.101	3.114	0.397	0.757
p-CA-Xylan	para-Coumaric acid on xylan	< 0.001	204.456	0.193	1.849	0.118	2.735
p-CA-Lignin	para-Coumaric acid on lignin	0.220	1.628	< 0.001	24.252	0.115	2.770
Lignin	Lignin	0.251	1.422	0.401	0.743	0.318	1.061
Cellulose	Cellulose	0.007	9.585	0.018	7.064	0.053	4.401
Glucuronic acid	Glucuronic acid	< 0.001	37.454	0.962	0.002	0.134	2.509
Cellulose/glucuronic acid	Ratio of cellulose to glucuronic acid content	< 0.001	39.504	0.232	1.564	0.352	0.926
Anet	Net CO2 assimilation rates	< 0.001	37.20	0.102	2.94	0.605	0.28
Δ13C-gm	Mesophyll conductance to CO2	0.001	16.66	0.003	11.44	0.315	1.06
gsw	Stomatal conductance to water	0.003	11.085	0.035	5.096	0.334	0.981
Δ13C-gm/gsw	Ratio of mesophyll to stomatal conductance	0.006	9.656	0.013	7.400	0.222	1.590
Ci/Ca	Ratio or intercellular to atmospheric CO2 partial pressure	0.365	0.861	0.150	2.243	0.657	0.204
WUEi	Intrinsic WUE (Anet/gsw)	0.303	1.12	0.111	2.78	0.619	0.26
Vcmax	Rates of maximum carboxylation	0.247	1.43	0.973	0.01	0.545	0.38
Jmax	Rates of electron transport	< 0.001	28.23	0.005	9.82	0.540	0.39
Amax	Maximum photosynthetic rates	< 0.001	22.14	0.017	6.80	0.560	0.35
Ls	Stomatal limitations to Anet	0.015	7.10	0.116	2.71	0.420	0.68
Lm	Mesophyll limitations to Anet	< 0.001	507.30	< 0.001	309.95	0.003	11.22
Lb	Biochemical limitations to Anet	< 0.001	175.17	< 0.001	63.96	< 0.001	33.31
Kleaf	Leaf hydraulic conductance	0.20	1.76	< 0.001	11.31	0.18	1.95
Kox	Outside-xylem conductance to water	0.09	3.27	0.01	7.83	0.60	0.29
Kx	Xylem conductance to water	0.12	2.62	0.10	3.08	0.23	1.53
Leaf water potential	Leaf water potential	0.49	0.50	0.97	0.00	0.15	2.22

P values ≤ 0.1 are highlighted in bold. P values ≤ 0.05 were considered as statistically significant, and P values ≤ 0.1 were considered as marginally significant.

Table 2.

Summary of 2-way ANOVA P values and F values for the plant-level traits measured on the rice Ubi_pro-AT10 WT and transgenic lines at a timepoint of 50 to 60 d

Abbreviations	Traits	Genotype		Water		Genotype × water
		P value	F value	P value	F value	P value	F value
AGB	Aboveground biomass	0.005	9.88	< 0.001	115.07	0.624	0.25
Leaf biomass	Biomass of transpiring surface	0.039	4.93	< 0.001	161.93	0.048	4.47
Stem biomass	Biomass of nontranspiring surfaces	0.007	9.17	< 0.001	80.87	0.984	0.00
Leaf width	Leaf width	< 0.001	83.51	< 0.001	136.18	0.189	1.84
LMA	Leaf mass per area	0.373	0.83	0.745	0.11	0.170	2.05
Leaf N content	Leaf nitrogen content	0.325	1.02	0.218	1.63	0.854	0.04
WUEcanopy	Canopy-level WUE	0.003	11.35	0.504	0.46	0.806	0.06
Total water used	Total water used during duration of experiment	0.621	0.25	< 0.001	140.72	0.478	0.52

P values ≤ 0.1 are highlighted in bold. P values ≤ 0.05 were considered as statistically significant, and P values ≤ 0.1 were considered as marginally significant.

Changes in CW composition

Since the CW composition of leaves of the developmental stage and growth conditions examined had not been reported previously for these transgenic lines, we evaluated the differences in leaf CW composition between the wild type (WT) and transgenics growing under high-water (HW) and low-water (LW) treatments (Figs. 1 and S1 and Tables 1 and S1). There was a significant genotype effect on FA and p-CA content (P < 0.001; Table 1 and Fig. 1, A to D) for the Ubi_pro-AT10 lines. The Ubi_pro-AT10 transgenics showed significantly lower FA-xylan and FA-lignin (∼40% and 27%, respectively; Fig. 1, A and B) but higher p-CA-xylan (∼100%; Fig. 1C) compared with WT. However, there was no significant genotype effect on p-CA-lignin content (Fig. 1D and Table 1). We also observed a statistically significant genotype × water effect (P = 0.004; Table 1) for FA-xylan, wherein Ubi_pro-AT10 transgenics showed greater decrease in FA-xylan content under HW treatment (∼44%) compared with LW treatment (∼30%). In terms of CW sugars, there was a statistically significant genotype effect on cellulose, glucuronic acid content, fucose, and galactose (P < 0.05; Tables 1 and S1), wherein Ubi_pro-AT10 transgenics exhibited lower glucuronic acid content (∼50%; Fig. 1G) but higher cellulose (∼14%; Fig. 1F), fucose (∼22%; Supplemental Fig. S1C), and galactose content (∼8%; Supplemental Fig. S1D) compared with WT. However, there was also a significant genotype × water effect on cellulose and fucose (P < 0.05; Tables 1 and S1), indicating that the higher cellulose and fucose content in Ubi_pro-AT10 transgenics compared with WT was largely driven by a significantly higher cellulose (∼20%) and fucose (∼75%) observed only under the LW treatment. In contrast, Ubi_pro-AT10 transgenics did not differ significantly from WT in terms of lignin content (P > 0.1; Table 1 and Fig. 1E) and other CW sugars measured in the current study (P > 0.1; Supplemental Table S1 and Fig. S1).

Similar differences as in Ubi_pro-AT10 transgenics were observed between OsAT10-D1 and WT lines for the important CW components, that is, FA, p-CA, and cellulose (Supplemental Fig. S3 and Table S2). There were statistically significant genotype effects on FA, p-CA, and cellulose content (P < 0.05; Supplemental Table S2), as the OsAT10-D1 transgenics showed significantly lower FA-xylan and FA-lignin (∼53% and 44%, respectively; Supplemental Fig. S3, A and B) but higher p-CA-xylan (∼180%; Supplemental Fig. S3C) compared with WT. In contrast, OsAT10-D1 transgenics exhibited significantly higher cellulose content (∼16%) compared with WT (Supplemental Fig. S3F) under both HW and LW treatments. However, in contrast to Ubi_pro-AT10 transgenics, OsAT10-D1 transgenics did not show any significant change in glucuronic acid content (P > 0.1; Supplemental Table S2 and Fig. S3G). Also, in contrast to the increase in fucose content in Ubi_pro-AT10 transgenics, OsAT10-D1 transgenics showed a significant decrease in fucose content (20%, P < 0.01; Supplemental Table S3 and Fig. S4C). We did not observe a statistically significant genotype effect on lignin (Supplemental Fig. S3E) and any other CW sugars for OsAT10-D1 lines (P > 0.05; Supplemental Tables S2 and S3 and Fig. S4).

Changes in leaf anatomical traits

We assessed changes in key leaf anatomical traits in the WT and transgenic plants growing under HW treatment. Both Ubi_pro-AT10 and OsAT10-D1 transgenics exhibited lower mesophyll CW thickness (∼13% to 15%) compared with WT plants, with the differences in CW thickness between WT and transgenics being statistically significant for Ubi_pro-AT10 (P < 0.05; Fig. 2A) and marginally significant for OsAT10-D1 genotypes (P < 0.1; Fig. 2A). In contrast, we did not observe statistically significant differences in BS_CW between WT and transgenics in both Ubi_pro-AT10 and OsAT10-D1 lines (Fig. 2B). Furthermore, we observed a marginally significant decrease in S_mes (12%) in Ubi_pro-AT10 transgenics compared with WT (P = 0.062) but not in the OsAT10-D1 transgenics (Fig. 2C). Other leaf anatomical traits, like S_c, leaf thickness, and interveinal distance (IVD), did not differ significantly between the WT and transgenic plants for both Ubi_pro-AT10 and OsAT10-D1 lines (P > 0.1; Fig. 2, D to F).

Impacts of modification of CW composition on leaf physiology and biochemistry

Both Ubi_pro-AT10 and OsAT10-D1 transgenics differed significantly from WT plants for several key leaf-level physiological traits as indicated by the significant genotype effects in a 2-way ANOVA (Tables 1 to 2 and S1). Specifically, Ubi_pro-AT10 transgenics exhibited a significantly greater g_m (120%; Fig. 3A) and A_net (22%; Fig. 3B) compared with WT (P < 0.001; Table 1). Transgenics also showed significantly greater g_sw (40%; Fig. 3C) compared with WT (P < 0.01; Table 1). The magnitude of increase in g_sw in transgenics was less compared with the 120% increase in g_m but greater compared with the 22% increase in A_net. Consequently, though Ubi_pro-AT10 transgenics exhibited a significantly greater g_m/g_s ratio (110%, P < 0.05; Table 1 and Fig. 3D) compared with WT, WUE_i (that is, A_net/g_sw) remained unchanged (P > 0.1; Table 1 and Fig. 3F). Also, we did not observe a significant genotype effect on C_i/C_a ratio (P > 0.1; Table 1 and Fig. 3E). In terms of biochemical traits, Ubi_pro-AT10 transgenics exhibited significantly greater J_max (20%; Fig. 4B) and A_max (14%; Fig. 4C) compared with WT (P < 0.01; Table 1). In contrast, transgenics and WT exhibited similar V_cmax and leaf nitrogen (N) content (P > 0.1; Table 1 and Fig. 4, A and D). There was a statistically significant genotype effect on stomatal, mesophyll, and biochemical limitations (P < 0.01; Table 1) as Ubi_pro-AT10 transgenics exhibited lower relative stomatal (10%; Fig. 5A) and mesophyll limitations (60%; Fig. 5B) but greater biochemical limitations to A_net (50%; Fig. 5C) compared with WT. There were no significant genotype effects on leaf hydraulic traits, that is, K_leaf, leaf water potential, and K_x (P > 0.1; Table 1 and Fig. 6, A to C). However, K_ox tended to be lower (12%) in Ubi_pro-AT10 transgenics compared with WT (P = 0.09; Table 1 and Fig. 6D), largely due to lower values under LW conditions.

We did not observe a statistically significant genotype × water interaction effect on almost all the physiological and biochemical traits except for mesophyll and biochemical limitations to A_net (P < 0.01; Table 1). Particularly, the decrease in mesophyll limitations in Ubi_pro-AT10 transgenics compared with WT was more pronounced under lower water treatment (62%) compared with HW treatment (45%; Fig. 5B). In contrast, the increase in biochemical limitations in Ubi_pro-AT10 transgenics compared with WT was more pronounced under LW treatment (100%) compared with HW treatment (30%; Fig. 5C).

Similar, but lower in magnitude, trends to those of Ubi_pro-AT10 lines were observed for key physiological traits in the OsAT10-D1 lines. Particularly, we observed a significant genotype effect on key physiological traits wherein OsAT10-D1 transgenics exhibited significantly greater g_m (65%), A_net (14%), g_sw (20%), and g_m/g_sw (100%) ratios compared with WT (P < 0.05; Supplemental Table S2 and Fig. S5, A to D). In contrast, we did not observe a significant genotype effect on C_i/C_a ratio and WUE_i (Supplemental Fig. S5, E and F). Also, we did not observe a significant genotype × water interaction effect on any of the physiological traits measured for OsAT10-D1 lines (P > 0.1; Supplemental Table S2).

Impacts of modification of CW composition on plant-level traits

Ubi_pro-AT10 transgenics differed significantly from WT for key leaf and whole-plant traits as indicated by a statistically significant genotype effects (P < 0.05; Table 2 and Fig. 7). Across the water treatments, Ubi_pro-AT10 transgenics showed significantly greater aboveground biomass (AGB) (12.5%; Fig. 7A), leaf biomass (7.74%; Fig. 7B), stem biomass (14.7%; Fig. 7C), and leaf width (14.3%; Fig. 7E) compared with WT. However, transgenics did not differ from WT in terms of leaf mass per area (LMA) and leaf N content (Fig. 7, D and F) nor in terms of total water used during the growth period (Fig. 7F). Consequently, there was a greater WUE_canopy (AGB/total water used) in the transgenics compared with WT (8.8%; Fig. 7G). We did not observe significant genotype × water interaction effect for most leaf and whole-plant traits (P > 0.1; Table 2) except leaf biomass (P = 0.048; Table 2) which was significantly higher in Ubi_pro-AT10 transgenics than WT under lower water treatment (14.7%) but not under HW treatment.

For OsAT10-D1 lines, we observed marginally significant genotype effects only on WUE_canopy (P = 0.073; Supplemental Table S2 and Fig. S6F), wherein OsAT10-D1 transgenics exhibited greater WUE_canopy (4%) compared with WT. For other key traits like AGB, leaf biomass, stem biomass, and total water used, we observed a marginally significant genotype × water effect (P < 0.1, Supplemental Table S2), wherein the OsAT10-D1 transgenics showed greater AGB (10.5%; Supplemental Fig. S6A), leaf biomass (7.5%; Supplemental Fig. S6B), and stem biomass (12.4%; Supplemental Fig. S6C), compared with WT only under LW treatment.

Discussion

CW properties have been proposed as key targets for improving crop carbon assimilation (A_net) and WUE_i (Lundgren and Fleming 2020; Evans 2021; Flexas et al. 2021; Pathare et al. 2022). However, little is known about how CW components affect g_m, A_net, and water use in grasses—which comprise a majority of crops. Here, we provide evidence that changes in CW hydroxycinnamic acid content (greater p-CA and lower FA) decrease CW thickness, thus enhancing g_m and A_net, without having a detrimental impact on leaf hydraulics and plant growth, even under LW stress.

Changes in CW composition alter CW thickness and g_m

Many recent studies report strong relationships of g_m with CW composition across diverse groups of trees and herbaceous dicot species, highlighting the importance of CWs for improving g_m and A_net. However, there are only a few studies using transgenics to investigate the impacts of modification of CW composition on g_m and A_net in monocot grass species (Ellsworth et al. 2018). Specifically, Ellsworth et al. (2018) used rice plants with disruptions in CW mixed-linkage glucan (a polysaccharide unique to grasses) caused due to mutation in the cellulose synthase-like F-6 gene (CslF6) to assess the impacts of CW properties on g_m and other leaf physiological traits. The cslF6 knockout plants exhibited substantially thinner CWs compared with WT but showed a substantial reduction in g_m (83%) and A_net (30% to 40%). This discrepancy between CW thickness and g_m/A_net was attributed to lower CW effective porosity and increased effective path length. However, a significant portion of the reduction in g_m in the cslF6 rice plants was also attributed to pleiotropic effects affecting other anatomical traits related to g_m, like S_c, which was lower in the transgenics.

In the current study, we explored how modification of CW hydroxycinnamic acids affects mesophyll CW thickness, g_m, and other traits related to CO₂ uptake and water use. Hydroxycinnamates on arabinoxylan are another unique component of CWs of grasses and other recently evolved monocots, modification of which affects biomass properties (Bartley et al. 2013; Tian et al. 2021; Chandrakanth et al. 2023). Our data suggest that overexpression of OsAT10, which results in lower FA and greater p-CA content, leads to thinner CWs in the transgenics compared with WT plants. However, changes in FA and p-CA did not alter other anatomical traits known to influence g_m in rice like S_mes, S_c, leaf thickness, and LMA (Fig. 1) (Giuliani et al. 2013; Xiong and Flexas 2018). Thus, in contrast to the results with cslf6 disruption (Ellsworth et al. 2018), changes in g_m and A_net of the rice CW transgenics in the current study can be directly attributed to changes in CW thickness. That the reduction in CW thickness (∼14%) is less compared with the observed increase in g_m (∼120%) suggests that hydroxycinnamic acid content alterations may influence other CW properties that affect g_m, like porosity and tortuosity. Such changes might be realized through CW polymer rearrangements and changes in relative abundance of CW components, including cellulose, lignin, and pectin. Here, we did not observe significant changes in lignin or in estimated pectin content in our rice CW transgenics (Figs. 1 and S1). Furthermore, though both Ubi_pro-AT10 and OsAT10-D1 transgenics also exhibit higher cellulose content (Figs. 1 and S3), this increase was largely a result of a decrease in cellulose in WT under LW treatment, making the effect difficult to connect to the significant in increase in g_m. On the other hand, since FA units of grass GAX are associated with cross-linking between GAX polymers and between GAX with lignin, a decrease in FA content in rice transgenics could result in “loosening” of CWs and thus an increase in CW porosity (Chandrakanth et al. 2023). A recent review also suggests the potential role of hydroxycinnamic acids in determining g_m through changes in chemical tortuosity of CWs (or the chemical interactions between CO₂ and CWs components), though future research is essential to test this further (Flexas et al. 2021).

In addition to the decrease in FA substitutions on xylan, we also observed a substantial decrease in glucuronic acid content in the Ubi_pro-AT10 transgenics. We hypothesize that a decrease in glucuronic acid substitutions on xylan could result in an increase in 3-fold helical xylan, relative to 2-fold helical xylan, the former of which packs less closely with cellulose based on recent solid-state NMR data (Gao et al. 2020; Duan et al. 2021) and thus may increase CW porosity. Furthermore, changes in relative abundance of crystalline and amorphous cellulose might also lead to differences in CW porosity and thickness. Though the mechanisms involved remain to be tested, our results show that changes in CW hydroxycinnamic acid content (increased p-CA and decreased FA) in rice increase g_m and hence the supply of CO₂ to Rubisco.

CW transgenics show greater g_m and A_net but not WUE_i

Higher g_m in the rice CW transgenics was associated with a concurrent increase in A_net (Fig. 3), thus supporting the previous reports that g_m is an important factor determining A_net in C₃ species (Flexas et al. 2012; Barbour and Kaiser 2016; Gago et al. 2020). However, the magnitude of the increase in A_net (22%) was low compared with increases in g_m (120%). This is because, besides diffusion of CO₂, biochemical capacity is an important factor limiting A_net (Gago et al. 2020). Here, we did not observe any significant differences in biochemical capacity (as indicated by leaf N content and V_cmax) between the transgenics and WT. However, transgenics did show lower diffusional but greater biochemical limitations compared with WT (Fig. 5). This suggests that the lesser magnitude of increase in A_net in the transgenics despite greater increases in g_m could be attributed to enhanced biochemical limitations in transgenics compared with WT. Alleviation of these biochemical limitations, for instance, through changes in Rubisco properties and kinetics (Flexas et al. 2016; Carmo-Silva and Sharwood 2023), could further increase A_net in the OsAT10-overexpression lines.

Though the rice transgenics exhibited greater g_m and A_net compared with WT, we did not observe any concurrent increases in WUE_i (Fig. 3F). Because the CO₂ diffusion pathway related to g_m is not the same as the pathway of water transpired out of the leaf through stomata, an increase in g_m is expected to increase CO₂ concentrations at the site of fixation without a concurrent increase in g_sw, thus increasing both A_net and WUE_i (Flexas et al. 2016). While there is some previous evidence supporting this (Sade et al. 2009; Flexas, Scoffoni, et al. 2013), studies on C₃ species also report coordinated responses of g_m and g_sw such that WUE_i remains unchanged or even decreases (Giuliani et al. 2013; Barbour and Kaiser 2016; Tomeo and Rosenthal 2017). Such coordinated responses of g_m and g_sw were also observed in plants specifically engineered to alter g_m by changing CW composition or aquaporin expression (Hanba et al. 2004; Roig-Oliver et al. 2021). For instance, modification of CW composition in Arabidopsis (Arabidopsis thaliana) led to simultaneous decreases in g_m, g_sw, and A_net, resulting in no differences in WUE_i between transgenics and WT plants (Roig-Oliver et al. 2021). We observed the same pattern in the current study, with increases in g_m in the transgenics also being associated with a concomitant increase in g_sw. Together, these results suggest that manipulation of g_m can increase A_net but concomitant changes in g_sw can make a simultaneous increase in A_net and WUE_i difficult. Thus, to achieve the goal of greater A_net and WUE_i, biotechnological manipulations must induce increases in g_m while maintaining or even decreasing g_sw (Leakey et al. 2019). Furthermore, our study supports previous reports (Roig-Oliver et al. 2021) that changes in CW properties can affect g_sw. Whether such relationships observed between CWs and g_sw result from direct effects of CW properties on stomata (Jones et al. 2003) or an indirect effect of coadjustment between g_m and g_sw (Flexas et al. 2012) needs to be investigated.

Changes in CW properties do not alter K_leaf

In the current study, we also investigated how modification of CW properties and thickness affects the water transport inside leaves (K_leaf) and the coordination of K_leaf with g_m, g_sw, and A_net. Just like g_m, K_leaf is extremely complex and influenced by several leaf anatomical features (Buckley et al. 2015). However, unlike g_m, the impacts of different leaf anatomical features on K_leaf and primarily K_ox are less studied. The rice CW transgenics in the current study showed lower CW thickness and altered CW composition without concurrent changes in other anatomical traits known to influence K_leaf like IVD, thickness, bundle sheath CW thickness, S_mes, and stomatal density (Fig. 2). This provided us with the unique opportunity to test the impacts of modification of mesophyll CWs on K_leaf and K_ox. Despite a significant decrease in mesophyll CW thickness, we observed no changes in K_leaf and K_ox in the transgenics. The apoplastic pathway via mesophyll CWs is often assumed to be the major pathway of water movement outside the xylem, based on which it has been hypothesized that thinner CWs would lead to lower resistance to water movement by decreasing the outside-xylem apoplast pathlength, thus leading to lower K_leaf and K_ox (Buckley et al. 2015). Our results contrast these expectations and suggest that mesophyll CW thickness may not be the only factor determining K_leaf and K_ox in rice. Instead, symplastic water movement could have major influences on K_ox, as suggested previously (Sade et al. 2014; Buckley et al. 2015).

Similar to the coupling between g_m and g_sw, previous studies also demonstrate a coupling of g_m and g_sw with K_leaf. For example, in diverse C₃ species and under different environmental conditions, g_sw and K_leaf were positively correlated, presumably because of the common pathway of movement shared by water and CO₂ via stomata and cuticle (Boyer 2015; Scoffoni et al. 2016) and via the mesophyll tissue inside leaves (Flexas, Niinemets, et al. 2013; Xiong et al. 2017). Such coordinated changes in g_m, g_sw, and K_leaf have also been demonstrated in studies that use transgenics to modify these conductances (Sade et al. 2014). However, in the current study, we did not observe changes in K_leaf and K_ox despite changes in g_sw, g_m, and A_net. Such decoupling of leaf hydraulic and photosynthetic traits has been reported previously for some environmental conditions (Li et al. 2015) and plant species (Pathare, Koteyeva, et al. 2020) and implies that improving crop g_m, A_net, and WUE_i, while maintaining K_leaf, is possible.

Changes in CW properties affect AGB and WUE_canopy

CWs play a major role in maintaining structural integrity and drought tolerance capacity of plants (Keegstra 2010). Consequently, one could expect that any CW-related improvement in g_m and A_net will have detrimental effects on plant tolerance to water stress and/or any other fundamental functions performed by CWs in plants. Here, in addition to the greater g_m and A_net, rice CW transgenics also exhibited a small (12.5%) but statistically significant increase in AGB that was more prominent under LW treatment (Fig. 7). The increase in AGB was not associated with a concurrent increase in canopy water use, because of which we also observed a significant increase in WUE_canopy in CW transgenics. Our results thus demonstrate that modification of CW hydroxycinnamic acid content improves g_m and A_net without having a detrimental effect on plant growth, even under LW stress.

Conclusions

In summary, our results demonstrate that alteration of CW hydroxycinnamic acid content (greater p-CA and lower FA) decreased mesophyll CW thickness and potentially altered CW porosity and tortuosity, thus increasing g_m and A_net in rice. However, a concomitant increase in g_sw canceled out the expected benefit of increased A_net, resulting in a lack of changes in WUE_i. Despite no changes in WUE_i, rice transgenics exhibited greater AGB and WUE_canopy, with a greater increase under LW availability. The alteration of rice CW hydroxycinnamic acid content and mesophyll CW thickness did not influence leaf hydraulic traits. Overall, our results indicate that modification of CW hydroxycinnamic acid content, a unique and important feature of grass CWs, can influence mesophyll CW thickness and leaf- and plant-level CO₂ uptake and water use without having detrimental impacts on plant growth even under LW stress. Integrating such increases in g_m and A_net, through changes in CW hydroxycinnamic acid content, with efforts to increase photosynthetic efficiency (Carmo-Silva and Sharwood 2023) and decrease g_sw (Leakey et al. 2019) could help achieve substantial increases in photosynthesis, biomass, and WUE under both well-watered and water-limited conditions in monocot C₃ crops.

Materials and methods

Plant material and growth conditions

Previously characterized rice (O. sativa subsp. japonica) and transgenics (ZmUbi_pro-AT10 and OsAT10- D1) overexpressing the gene OsAT10 (LOC_Os06g39390) were used in the current study (Bartley et al. 2013). The ZmUbi_pro-AT10 line (Line 7 to 1), in which the rice AT10 coding sequence is driven by the maize (Zea mays) Ubiquitin1 promoter, and a negative segregant (Line 7 to 5) are in the ‘Kitaake’ cultivar, and the activation-tagged construct (OsAT10-D1, Line 21) and a negative segregant (Line 17) are in the ‘Dongjin’ cultivar. Overexpression of OsAT10 in rice was previously reported to decrease FA, and increase p-CA, and glucose released through enzymatic digestibility of rice, without noticeable impacts on the health and vegetative development of transgenic plants. Also, alterations in lignin content and composition were reported previously, though an increase in CW glucose content was noted (Bartley et al. 2013).

Seeds for negative segregant, WT plants, and transgenics were germinated on wet filter paper in a Petri plate for 4 d and then transplanted to trays containing Sunshine mix LC-1 soil (Sun Gro Horticulture, Agawam, MA, USA) mixed with turface (ratio of 3:1 in volume). Ten days after germination, seedlings were transplanted into 3 L free-drainage pots in climate-controlled growth chambers (Biochambers, #GRC-36, Winnipeg, MB, Canada). The photoperiod was 14 h, including 2 h ramps at the beginning and end of the light period. Temperatures during light and dark phase were maintained at 26 and 22 °C, respectively, and relative humidity levels were at 70%. The light was emitted by 400 W metal halide and high-pressure sodium lamps with a maximum photosynthetic photon flux density (PPFD) of c. 1,000 µmol photons m⁻² s⁻¹.

This experiment consisted of 2 water levels, HW or 90% of pot capacity and LW or 50% of pot capacity, and 4 lines (2 WT and 2 transgenics) with 6 replicates for each treatment. Before transplanting, all the pots were weighed, then filled with potting mix, and weighed again to get empty pot and pot plus potting mix weights. These pots were saturated with water and allowed to drain overnight and weighed the next morning to get the 100% pot capacity weight. Ten-day-old seedlings were then transplanted to pots (1 seedling in each pot). Water treatments were initiated by withholding irrigation until the pots reached the desired 90% and 50% pot capacity weights for HW and LW treatments, respectively (which were achieved in 4 d for HW pots and 11 d for LW pots). For the remainder of the experiment, pots were maintained at HW and LW conditions by weighing each day and watering to the target weights. Pots and their lateral holes were covered with plastic to minimize evaporation. Daily transpiration was measured as the daily difference in pot weight minus soil evaporation (measured as the difference in weight of a covered pot without a plant in each treatment group). Total water used (g) was calculated at the end of the experiment by summing the daily transpiration values. Pots were randomly moved around each day when pots were weighed and watered.

Gas exchange and mesophyll conductance measurements

Simultaneous measurements of net photosynthetic rates (A_net; µmol m⁻² s⁻¹), stomatal conductance to water vapor (g_sw; mol water m⁻² s⁻¹), intercellular pCO₂ (C_i and Pa), and photosynthetic C isotope discrimination was conducted using a Li-6800 portable photosynthesis system (LI-6800, Li-Cor, Lincoln, USA) coupled to a tunable diode laser absorption spectroscope (TDLAS, model TGA 200A; Campbell Scientific, Logan, UT, USA). Measurements were conducted 45 to 52 d after germination on the topmost fully expanded leaves around mid-day (9:30 AM to 2 PM) for each line and water treatment. Multiple nonoverlapping leaves were placed across the Li-6800 chamber and were allowed to adjust for at least 30 min or until stable values of A_net and g_sw were achieved. The CO₂ sample was maintained at pCO₂ of ∼ 37 Pa, leaf temperature at 26 °C, and relative humidity at 55% to 60%. Data for isotopologs of CO₂ and H₂O and physiological parameters (A_net, g_sw, and C_i) were collected and averaged over the next 20 to 30 min for c. 8 to 12 cycles of TDLAS with the Li-6800 set to log data only when the TDLAS analyzed the Li-6800 sample line. Five to 6 biological replicates per treatment were measured. Leaf-level intrinsic WUE (WUE_i, μmol CO₂ mol⁻¹ water) was calculated as A_net/g_sw. Mesophyll conductance to CO₂ (g_m, µmol CO₂ m⁻² s⁻¹ Pa⁻¹) was estimated using the ¹³C method for C₃ species as detailed in previous studies (Ellsworth et al. 2018; Sonawane and Cousins 2019).

After measurement of physiological parameters and isotopologs, photosynthetic CO₂ response curves (A_net-C_i curves) were measured on the same leaves to determine photosynthetic capacities. A_net-C_i response curves were measured with 13 different steps of pCO₂ (5, 10, 14, 19, 23, 28, 37, 51, 65, 93, 111, 139, and 167 Pa) while maintaining saturating light conditions (photon flux density of 1500 μmol photons m⁻² s⁻¹), 55% to 65% relative humidity, and leaf temperatures of 26 °C and allowing a stabilization time of 2 to 3 min after each step change in pCO₂. During the A_net-C_i measurements, pCO₂ in the cuvette was controlled in the Li-COR sample line. Five to 6 replicate plants per treatment condition were used to measure A_net-C_i curves. To obtain kinetic coefficients associated with rates of maximum carboxylation (V_cmax; μmol CO₂ m⁻² s⁻¹) and electron transport (J_max; μmol electron m⁻² s⁻¹), we fitted A_net-C_i curves using the biochemical model of photosynthesis for C₃ species (Farquhar et al. 1980) and the “fitacis” function from the plantecophys R-package (Duursma 2015). While deriving V_cmax and J_max, we used g_m values estimated in the current study for each line and treatment condition and values of Rubisco Michaelis–Menten constants for CO₂ (K_c = 239 μbar) and O₂ (K_o = 266 μbar) as reported previously for rice (von Caemmerer 2000). Maximum photosynthetic rates (A_max, μmol CO₂ m⁻² s⁻¹) were measured at saturating light of ∼ 1,200 μmol photons m⁻² s⁻¹ and pCO₂ of 1,500 μmol CO₂ mol⁻¹ air. After gas exchange measurements, leaves were harvested immediately and processed to analyze CW composition, leaf N content, and leaf anatomy.

Estimation of stomatal, mesophyll, and biochemical limitations to photosynthesis

We estimated limitations to light-saturated CO₂ assimilation rates that primarily occur through stomatal restrictions to the diffusion of CO₂ into intracellular leaf spaces (L_s), mesophyll restrictions to the diffusion of CO₂ into the site of fixation by Rubisco (L_m), and due to the biochemistry of CO₂ fixation at the chloroplast (L_b) as described previously (Jones 1985; Grassi and Magnani 2005). In this approach, the total photosynthetic limitation is divided into the relative limitations of stomata (L_s), mesophyll (L_m), and biochemistry (L_b):

$$L_{\mathrm{s}}={\displaystyle \frac{\frac{g_{\mathrm{t}}}{g_{\mathrm{sc}}}\times \frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}{g_{\mathrm{t}}+\frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}.}$$

(1)

$$L_{\mathrm{m}}={\displaystyle \frac{\frac{g_{\mathrm{t}}}{g_{\mathrm{m}}}\times \frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}{g_{\mathrm{t}}+\frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}.}$$

(2)

$$L_{\mathrm{b}}={\displaystyle \frac{g_{\mathrm{t}}\times \frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}{g_{\mathrm{t}}+\frac{\mathrm{\partial }A}{\mathrm{\partial }{C}_{\mathrm{c}}}}.}$$

(3)

where $g_{\mathrm{t}}$ is the total conductance to CO₂ from the leaf surface to the sites of fixation at Rubisco:

$$g_{\mathrm{t}}={\displaystyle \frac{1}{\frac{1}{g_{\mathrm{sc}}}+\frac{1}{g_{\mathrm{m}}}}}$$

(4)

and ∂A/∂C_c is the partial derivative of net CO₂ assimilation (A_net) for the relative change in CO₂ concentration at the Rubisco (C_c) derived from the initial slope of A_net-C_c curve as described earlier (Pathare et al. 2017). For this, we used plantecophys R- package (Duursma 2015) to estimate C_i at the transition and compensation points of A_net-C_i curve, and respective C_c values were calculated using measured g_m.

Analysis of leaf CW composition, N content, and anatomical traits

For CW composition and N content analysis, newly expanded, fully emerged leaves used for gas exchange measurements were harvested for WT and transgenic lines exposed to HW and LW treatments. Leaf samples were dried in a hot air oven at 55 °C for 72 h. Analysis of CW components was performed as indicated previously for rice (Bartley et al. 2013; Karlen et al. 2016) in order to derive values for FA (µg/mg), p-CA (µg/mg), lignin (%), cellulose (µg/mg), and several other CW-associated sugars (µg/mg), that is, monosaccharides (arabinose, fucose, galactose, galacturonic acid, glucuronic acid, glucose, mannose, rhamnose, and xylose) and polysaccharides (pectin). Briefly, leaves were milled, and destarched alcohol insoluble residues (dsAIR) were prepared. This CW prep was treated first with weak acid (50 mM trifluoroacetate) to release matrix polysaccharide-associated phenolates, and then both the CW pellet and acid supernatant were treated with base to cleave the esterified FA and p-CA, which were quantified by HPLC. FA and p-CA content in supernatant (FA-xylan and p-CA-xylan) and pellet (FA-lignin and p-CA-lignin) has been given separately in Figures 1 and S3 and Tables 1 and S2, respectively. Cellulose and other monosaccharides were quantified using the trifluoroacetic acid (TFA) hydrolysis of dsAIR as detailed in Bartley et al. (2013). Lignin was quantified via acetyl bromide solubilization followed by quantification on a 96-well plate (Bartley et al. 2013). Pectin content (µg/mg) was estimated as a sum of galacturonic acid, galactose, and rhamnose (Mohnen 2008).

Leaf N content was measured using a Eurovector elemental analyzer and expressed on a leaf area basis (N_area; g m⁻²). LMA (mg cm⁻²) was also determined using the gas exchange leaves as a ratio of dry leaf mass to leaf area.

Leaf anatomical traits associated with g_m and K_leaf were measured on WT and transgenic lines only for HW treatment on topmost fully expanded leaves (45 to 52 d after germination). Light and transmission and scanning electron microscopy techniques were used to measure leaf thickness (µm), length of mesophyll CWs (µm) exposed to IAS (µm²), mesophyll CW thickness (T_CW; µm), and bundle sheath CW thickness (BS_CW; µm). The details of sample preparation for light and electron microscopy, measurements, and calculations are presented in our previous study (Pathare, Sonawane, et al. 2020). Briefly, light microscopy images of leaf cross-sections were used to measure the length of mesophyll CWs exposed to IAS using 10 to 15 different fields of view for each leaf (n = 4 to 5) taken at 50× and 100× magnifications. The mesophyll surface area exposed to IAS (S_mes; µm²µm⁻²) was calculated from measurements of the total length of mesophyll CWs exposed to IAS and width of section analyzed using the equation from Evans et al. (1994) with curvature correction factor (F) of 1.34. In contrast, surface area of chloroplasts exposed to IAS (S_c; µm²µm⁻²) was calculated as indicated in equation 5 using the light microscopy images taken randomly at 10 to 15 fields of view for each leaf (n = 4 to 5).

$$S_{\mathrm{c}}={\displaystyle \frac{\mathrm{Total}\mathrm{length}\mathrm{of}\mathrm{mesophyll}\mathrm{CW}\mathrm{exposed}\mathrm{to}\mathrm{IAS}\mathrm{covered}\mathrm{by}\mathrm{chloroplasts}}{\mathrm{Total}\mathrm{length}\mathrm{of}\mathrm{mesophyll}\mathrm{CW}\mathrm{exposed}\mathrm{to}\mathrm{IAS}}\times S_{\mathrm{mes}}.}$$

(5)

T_CW and BS_CW were measured from transmission electron microscopy (TEM) micrographs using at least 15 images for each leaf (n = 4 to 5). Light microscopy images of leaf cross-sections were used to determine leaf thickness (µm) as the average values measured over the vascular bundles and the bulliform cells (n = 4 to 5). For IVD (µm), the distance from the center of 1 vein to the other was measured.

Measurement of leaf hydraulic parameters

Measurements to obtain leaf water potential (Ψ_leaf; MPa), leaf hydraulic conductance (K_leaf; mmol m⁻² s⁻¹ MPa⁻¹), leaf hydraulic conductance inside the xylem (K_x; mmol m⁻² s⁻¹ MPa⁻¹), and outside-xylem conductance to water (K_ox; mmol m⁻² s⁻¹ MPa⁻¹) were made on the youngest fully expanded leaf from an individual plant as described previously (Sonawane et al. 2021) only for the Ubi_pro-AT10 and corresponding WT lines. The K_leaf and K_x measurements were performed at leaf temperatures of 26 °C. During K_leaf measurements, the incident light on the leaf was 1,200 μmol photons m⁻² s⁻¹. About 5 or 6 plants for each Ubi_pro-AT10 line and treatment combination were used (n = 5 to 6).

Measurement of biomass and WUE_canopy

Sixty days after germination, all plants were harvested and separated into the stem (nontranspiring tissue) and leaves (transpiring tissue) and dried in a ventilated drying oven at 55 °C for a week before weighing to measure the AGB (AGB = stem biomass + leaf biomass; g) (n = 5 to 6). Leaf width (cm) was measured at the time of harvest on 10 different leaves per plant (n = 5 to 6). AGB and total water used during the duration of the experiment were used to calculate canopy-level WUE (WUE_canopy = g dry AGB/g water used).

Statistical analyses

Statistical analyses and data visualization were performed using R software (v 4.1.0, R Foundation for Statistical Computing, Vienna, Austria). Normality and equal variances were tested, and when necessary, square root or log transformations were used to improve the data homoscedasticity. Two-way ANOVA was performed for all the measured traits (except leaf anatomical traits) with line and water-level as the main effects using the aov function in R (R Core Team, 2018). Results of 2-way ANOVA are given in Tables 1 and 2 and Supplemental Table S1 for Ubi_pro-AT10 lines and Supplemental Tables S2 and S3 for OsAt10-D1 lines. One-way ANOVA with post hoc Tukey's test was used to examine differences in measured traits among the 4 line and water treatment combinations, that is, WT and transgenic subjected to HW treatment and WT and transgenic subjected to LW treatments. Results for post hoc Tukey's test are given in Figures 1, 3 to 7, and S1 for Ubi_pro-AT10 lines and Supplemental Figures S3 to S6 for OSAT10-D1 lines. For the 1-way ANOVA, P values ≤ 0.05 were considered statistically significant and P values ≤ 0.1 as marginally significant.

Accession numbers

The GenBank ID for OsAT10 is NP_001408565.1.

Acknowledgments

We are also grateful to the Core Facility Center “Cell and Molecular Technologies in Plant Science” of Komarov Botanical Institute (St. Petersburg, Russia) and Franceschi Microscopy and Imaging Center at Washington State University (Pullman, USA) for the use of its facilities and staff assistance. We would also like to thank Charles A. Cody for help in plant growth management. We are grateful to the reviewers for their valuable inputs that have helped us improve this manuscript significantly. The authors declare that they have no conflicts of interest.

Author contributions

V.S.P., L.E.B., and A.B.C. designed the experiment. V.S.P., R.P., B.V.S., A.J.A., and N.K. performed the measurements and analyzed the data. V.S.P. led the writing with constructive inputs from all authors. All authors approved the final version of the manuscript.

Supplemental data

The following materials are available in the online version of this article.

Supplemental Table S1. Summary of 2-way ANOVA P values and F values for the leaf CW sugar content variables, not listed in Table 1, measured on the rice Ubipro-AT10 WT and transgenic lines at a timepoint of 50 to 60 d.

Supplemental Table S2. Summary of 2-way ANOVA P values and F values for the leaf CW composition, physiology, and plant-level traits associated with CO₂ uptake and water use measured on the rice OsAT10-D1 WT and transgenic lines at a timepoint of 50 to 60 d.

Supplemental Table S3. Summary of 2-way ANOVA P values and F values for the leaf CW sugar content variables, not listed in Supplemental Table S2, measured on the rice OsAt10-D1 WT and transgenic lines at a timepoint of 50 to 60 d.

Supplemental Figure S1. Leaf CW sugar content for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) measured under HW (blue triangles) and LW (gray circles).

Supplemental Figure S2. Net CO₂ assimilation rates (A_net) in response to intercellular pCO₂ (C_i), that is, A_net-C_i response curves, for Ubi_pro-AT10 WT (ubi_wt, open symbols) and transgenic (ubi_mut, filled symbols) were measured under HW (blue triangles) and LW (gray circles).

Supplemental Figure S3. Leaf CW composition for OsAT10-D1 WT (d1_wt, open symbols) and transgenic (d1_mut, filled symbols) measured under HW (red triangles) and LW (orange circles).

Supplemental Figure S4. Leaf CW sugar content for OsAT10-D1 WT (d1_wt, open symbols) and transgenic (d1_mut, filled symbols) measured under HW (red triangles) and LW (orange circles).

Supplemental Figure S5. Photosynthetic CO₂ uptake and water use for OsAT10-D1 WT (d1_wt, open symbols) and transgenic (d1_mut, filled symbols) measured under HW (red triangles) and LW (orange circles).

Supplemental Figure S6. Leaf- and plant-level traits for OsAT10-D1 WT (d1_wt, open symbols) and transgenic (d1_mut, filled symbols) measured under HW (red triangles) and LW (orange circles).

Funding

This work was supported by the Division of Chemical Sciences, Geosciences, and Biosciences, Office of Biological and Environmental Research in the DOE Office of Science (DE-SC0018277 and DE-SC0023160), National Science Foundation (Major Research Instrumentation grant no. 0923562), and USDA-NIFA (Hatch project #1015621).

Data availability

Data available on request from the authors.

Dive Curated Terms

The following phenotypic, genotypic, and functional terms are of significance to the work described in this paper:

• leaf AmiGo: PO:0025034

• mesophyll AmiGo: PO:0006070

• water CHEBI: CHEBI:15377

Author notes

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/General-Instructions) is Varsha S. Pathare.

Conflict of interest statement. The authors declare no conflict of interests.