Point mutations that boost aromatic amino acid production and CO2 assimilation in plants

Abstract

Aromatic compounds having unusual stability provide high-value chemicals and considerable promise for carbon storage. Terrestrial plants can convert atmospheric CO₂ into diverse and abundant aromatic compounds. However, it is unclear how plants control the shikimate pathway that connects the photosynthetic carbon fixation with the biosynthesis of aromatic amino acids, the major precursors of plant aromatic natural products. This study identified suppressor of tyra2 (sota) mutations that deregulate the first step in the plant shikimate pathway by alleviating multiple effector-mediated feedback regulation in Arabidopsis thaliana. The sota mutant plants showed hyperaccumulation of aromatic amino acids accompanied by up to a 30% increase in net CO₂ assimilation. The identified mutations can be used to enhance plant-based, sustainable conversion of atmospheric CO₂ to high-energy and high-value aromatic compounds.

Fig. 1.. Multiple suppressor of tyra2 (sota) mutations rescued the tyra2 growth inhibition and enhanced tyrosine (Tyr) and phenylalanine (Phe) accumulation.

(A) A simplified diagram of the shikimate and AAA biosynthetic pathways. DHS, 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase; E4P, erythrose-4-phosphate; PEP, phosphoenolpyruvate; TyrA, TyrA arogenate dehydrogenase. (B) Plant pictures of 4-week-old Col-0 wild-type (WT), tyra2, and two representative sota mutants of Arabidopsis thaliana. The remaining sota mutant plants are shown in fig. S1. (C) Soluble metabolite profiling and shoot area of the 3-week-old Col-0, tyra2, and sota mutants. Dark and light green bars represent that each sota mutant line showed Col-0–like fully mature green leaves and tyra2-like reticulated leaves, respectively. All the metabolic sota mutants exhibited significantly larger shoot area than tyra2 [one-way analysis of variance (ANOVA) with Dunnett’s multiple comparisons test, P < 0.001]. Data are means ± SEM (n = 4 independent plant samples). (D) Relative amounts of Tyr and Phe against Col-0 shown in (C) were plotted for metabolic sota (red circles), response sota (blue triangles), and tyra2 (a black square). (E) Plant pictures of representative complementation lines at T₂ generation that were generated by introducing either WT DHS (e.g., DHS1^WT) or sota-mutated DHS (e.g., DHS1^B4) genes, driven by the respective endogenous promoter, into the Arabidopsis tyra2 background. Scale bars, 1 cm. The remaining lines are shown in fig. S6.

Fig. 2.. The sota mutations biochemically deregulate the effector-mediated DHS negative feedback inhibition.

(A) A structural model of A. thaliana DHS2 (AtDHS2, purple) generated from the P. aeruginosa DHS (PaDHS, white) with Trp (magenta) bound. Residues corresponding to the sota mutations mapped onto the AtDHS2 model are highlighted in yellow. The entire model is shown in fig. S7. (B) Selected regions of the amino acid sequence alignment of PaDHS and AtDHS enzymes, with the positions of the sota mutations indicated by blue, red, green arrows for AtDHS1, AtDHS2, and AtDHS3, respectively. The entire alignment is shown in fig. S8. (C) Enzymatic assay of DHS2 WT (DHS2^WT) and DHS2 with a sota mutation (DHS2^A4, DHS2^A11, and DHS2^F1) in the presence of Tyr, Trp, or mixture of all AAAs at 1 mM. ****P ≤ 0.0001; significant differences by one-way ANOVA with Dunnett’s multiple comparisons test against the corresponding DHS2^WT samples. Data are means ± SEM (n = 3). (D) Screening of AAAs and AAA-derived metabolites as potential inhibitors of DHS1^WT and DHS2^WT. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001 denote significant differences by one-way ANOVA with Dunnett’s test against the corresponding “No effector” samples. Data are means ± SEM (n = 3). The dotted horizontal lines separate four sets of independent experiments. (E and F) IC₅₀ curves of WT and sota mutant enzymes of DHS1 (left) and DHS2 (right) with varied concentrations of HGA (E) and indole-3-pyruvate (IPA) (F). Data are means ± SEM (n = 3). (G) Plant picture (left) and fresh weight measurement (right) of 3-week-old Col-0, sotaB4, and sotaA4 mutants (Col-0 background) on the media containing ILA at 0, 250, 500, or 1000 μM. *P ≤ 0.05 and **P ≤ 0.01 denote significant differences by one-way ANOVA with Dunnett’s test against the corresponding Col-0 samples (n = 12 to 16 independent plant samples).

Fig. 3.. Increased carbon flux elevates the levels of AAAs but not all AAA-derived compounds in the sota mutants.

(A) ¹³CO₂ labeling experiment of Col-0, sotaB4, and sotaA4 (tyra2 background), followed by quantification of ¹³C-labeled Tyr and Phe by GC-MS and ¹³C-labeled Trp and shikimate by liquid chromatography (LC)–MS. Data are means ± SEM (n = 3 independent biological samples except for 0-hour time point having two replicates). (B) Targeted metabolomics analysis of AAAs and AAA-derived metabolites in 4-week-old Col-0, sotaB4, and sotaA4 (Col-0 background) grown on soil (also see fig. S17 for data of the sota mutants in the tyra2 background). Actual values are shown in table S3. Different letters indicate statistically significant differences among genotypes (one-way ANOVA with Tukey-Kramer test, P < 0.05). Data are means ± SEM (n = 5 to 6 independent plant samples). K3GR7R, kaempferol-3-O-(2″-O-rhamnosyl)glucoside-7-O-rhamnoside; PAL, Phe ammonia lyase. (C) The correlations between the levels of AAAs and their representative derivates shown in (B). The correlations of Phe versus phenyllactate or phenylacetate are shown in fig. S21.

Fig. 4.. Carbon fixation is accelerated to support high AAA production in the sota mutants.

(A to C) The levels of AAA and shikimate (A), starch (B), and glucose and sucrose (C) of Col-0, sotaB4, and sotaA4 (Col-0 background) harvested at the indicated time points under the 12-hour light /12-hour dark cycle (white/black bars above each graph). Starch is expressed as micromoles of glucose (Glc) equivalents per gram FW. *P ≤ 0.05 and ****P ≤ 0.0001; significant differences by one-way ANOVA with Dunnett’s multiple comparisons test against the corresponding Col-0 samples. Data are means ± SEM (n = 4 to 6 independent plant samples). (D and E) The response curves of CO₂ assimilation rate (A) to light intensity and CO₂ concentration in intercellular air spaces (C_i) of Arabidopsis Col-0 and the sota mutants (Col-0 background). Data are means ± SEM (n = 5 to 6 independent plant samples). (F) The sota mutations eliminate or attenuate feedback regulation by certain effector molecules (open diamonds) of AtDHS1/3 and AtDHS2 (blue and red lines, respectively).