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. 2024 Aug 21;36(11):4703–4715. doi: 10.1093/plcell/koae236

Integration of biological and information technologies to enhance plant autoluminescence

Jieyu Ge 1,#, Xuye Lang 2,3,#, Jiayi Ji 4,#, Chengyi Qu 5,#, He Qiao 6,7,#, Jingling Zhong 8, Daren Luo 9, Jin Hu 10, Hongyu Chen 11, Shun Wang 12, Tiange Wang 13, Shiquan Li 14, Wei Li 15,16, Peng Zheng 17,18, Jiming Xu 19, Hao Du 20,21,c,✉,d
PMCID: PMC11530770  PMID: 39167833

Abstract

Autoluminescent plants have been genetically modified to express the fungal bioluminescence pathway (FBP). However, a bottleneck in precursor production has limited the brightness of these luminescent plants. Here, we demonstrate the effectiveness of utilizing a computational model to guide a multiplex five-gene-silencing strategy by an artificial microRNA array to enhance caffeic acid (CA) and hispidin levels in plants. By combining loss-of-function-directed metabolic flux with a tyrosine-derived CA pathway, we achieved substantially enhanced bioluminescence levels. We successfully generated eFBP2 plants that emit considerably brighter bioluminescence for naked-eye reading by integrating all validated DNA modules. Our analysis revealed that the luminous energy conversion efficiency of the eFBP2 plants is currently very low, suggesting that luminescence intensity can be improved in future iterations. These findings highlight the potential to enhance plant luminescence through the integration of biological and information technologies.

Plants expressing an optimized fungal bioluminescence network exhibit 30-fold greater bioluminescence compared to the original pathway.

Introduction

Many living organisms have evolved the ability to produce visible bioluminescence through chemical reactions, which depends on the biosynthesis of the luminescence-emitting substrate luciferin (Tsarkova et al. 2016; Wainwright and Longo 2017; Fleiss and Sarkisyan 2019). However, not all exogenous luminescence systems can be genetically incorporated into plant genomes to create sustained autobioluminescent plants, due to the substrate not being self-sustainable and the toxicity of pathway intermediates (Kotlobay et al. 2018; Reuter et al. 2020). Researchers have identified fungal bioluminescence pathway (FBP) from bioluminescent mushrooms. This pathway converts caffeic acid (CA), a common intermediate across plant species, into an unstable high-energy intermediate using three enzymes: HispS (hispidin synthase), H3H (hispidin-3-hydroxylase), Luz (luciferase), releasing green light and producing caffeylpyruvic acid (Purtov et al. 2015; Kotlobay et al. 2018). To maintain the cycle, CPH (caffeylpyruvate hydrolase) enzyme converts caffeylpyruvic acid back to CA, allowing for self-sustained luminescence (Kotlobay et al. 2018). Thus, the FBP was quickly translated into the development of luminescent plants (Mitiouchkina et al. 2020; Zheng et al. 2023; Shakhova et al. 2024) and powerful tools (Khakhar et al. 2020; Calvache et al. 2024).

The precursors such as CA, caffeoyl CoA, and downstream metabolites of FBP are naturally integrated into the main biosynthesis pathways of lignin, anthocyanin, and flavonoids in plants (Tzin and Galili 2010; Muro-Villanueva et al. 2019; Dong and Lin 2021). Previous studies have suggested that plants have evolved numerous enzymes, which tend to convert abundant precursors into lignin, anthocyanin, and flavonoids, and the metabolic pathway from CA and caffeoyl CoA involves a series of enzymatic reactions in a complex network involving tens of enzyme families and hundreds of regulatory influences (Muro-Villanueva et al. 2019; Vanholme et al. 2019; Dong and Lin 2021). While research has focused on individual genes involved in CA biosynthesis and their impact on phenylpropanoid metabolism in various plant species, the combined effects of multiple gene and pathway perturbations have not been fully explored. Furthermore, with the rapidly growing number of sequenced plant transcriptomes and the advancements in gene regulation tools for model plants, the possibility of redesigning plant metabolic pathways has become more achievable (Clark et al. 2020; Lim et al. 2022). Through the use of information technology-guided metabolic modeling, researchers have been able to robust the synthesis of specific metabolites in industrial microbes (Smanski et al. 2016; Lv et al. 2022). Recent studies by Sulis et al. have demonstrated that using CRISPR editing to target computationally predicted multiple lignin biosynthetic genes in plants, leading to combined lignin modifications and improved wood properties (Sulis et al. 2023). However, these efforts have primarily focused on well-known pathways in model plants, whereas the potential application of transcriptome data from model plants in optimizing metabolic flux modifications for other plant species has remained elusive.

In this study, we successfully identified effective regulatory genes involved in CA metabolism in plants by analyzing transcriptomes from publicly available databases. We then developed a computational model to optimize the multiplex artificial microRNA (amiR) array for reducing the bypass flow of CA, caffeoyl CoA, and hispidin in plants. By integrating an extrinsic CA synthesis pathway from tyrosine with the amiR array strategy, we were able to enhance the metabolic flux to the optimized FBP pathway. These innovative strategies have alleviated a key constraint in the production of precursors lost in substrate competition, resulting in increased precursor capacity and brighter autoluminescent plants.

Results and discussion

Exploring cross-regulatory influences on the CA metabolic network through computational modeling and agroinfiltration verification

With the decreasing costs of sequencing and the increasing availability of transcriptome data in various databases, there is a growing interest in studies that focus on identifying key regulatory pathways through transcriptome data analysis (Cervantes-Pérez et al. 2022). To investigate the evolutionarily conserved CA (precursor for FBP and lignin) metabolic network in plants (Kotlobay et al. 2018; Vanholme et al. 2019; Mitiouchkina et al. 2020), we conducted standardized processing on 366 transcriptomes from poplar (Fig. 1A), a model plant for lignin pathway exploration (Fig. 1B), and categorized genes into 9 modules (ME0-ME8) based on their expression patterns (Supplementary Fig. S1A). Using the four well-studied CA metabolic genes F5H, CCR, CHI, and F3H, also involved in lignin synthesis (Fig. 1B), we found that F5H and CCR are in module ME0, while CHI and F3H are in module ME2 (Supplementary Fig. S1A and Supplementary Data Set 1). By importing genes from these modules into Cytoscape separately, we identified 6 potential candidate genes by intersecting CHI and F3H (Fig. 1C), as well as 43 potential related genes by intersecting F5H1, F5H2, and CCR (Fig. 1D). Combining these results, we generated a gene set of 49 potential genes involved in regulating CA metabolism (Supplementary Data Set 2).

Figure 1.

Figure 1.

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Identification of multiplex genes strategy to improve the content of caffeic acid in plants. A) The processing and analysis of poplar RNA-Seq raw data. 366 RNA-Seq is downloaded from NCBI with the GEO accession number GSE78953. The processing of each RNA-Seq library includes the following steps: first, using trim-galore to remove adapters and low-quality RNA-Seq reads, followed by validation using FastQC; second, processing the data to obtain gene expression abundance using Hisat2 and FeatureCounts; and finally, conducting co-expression analysis using WGCNA on the data. B) Summary of the metabolic pathways of caffeic acid in plants. PAL, phenylalanine ammonia lyase; C4H, cinnamic acid 4-hydroxylase; 4CL, 4-hydroxycinnamate:CoA ligase; C3H, coumarate 3-hydroxylase; C3′H, p-coumaroyl shikimate 3′ hydroxylase; C4H, cinnamic acid 4-hydroxylase; CAD, cinnamyl alcohol dehydrogenase; CCoAOMT, caffeoyl CoA 3-O-methyltransferase; CCR, cinnamoyl-CoA reductase; CHI, chalcone isomerase; CHS, chalcone synthase; COMT, caffeate/5-hydroxyferulate 3-O-methyltransferase; DFR, fihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F5H, ferulate 5-hydroxylase; FLS, flavonol synthase; HCT, 4-hydroxycinnamoyl CoA:shikimate/quinate hydroxycinnamoyltransferase; C3′H, 4-coumaroyl shikimate/quinate 3′-hydroxylase; CSE, caffeoyl shikimate esterase. The four genes CCR, F3H, CHI and F5H serve as the reference gene. C) The Venn diagram of F3H and CHI genes from co-expression analysis in poplar. D) The Venn diagram of F5H and CCR genes from co-expression analysis in poplar. E) The correlation between relative expression range and standard deviation of 49 genes in poplar. The x-axis is the standard deviation of the normalized expression level by the mean value of each gene across all RNA-Seq data, and the y-axis is the expression range of each gene across all RNA-Seq data. F) The correlation between the percentage of caffeic acid content in tobacco change and the reciprocal of the reaction steps. The x-axis is the reciprocal of reaction steps between caffeic acid and substrate catalyzed by each gene, and the y-axis is the percentage change of caffeic acid in tobacco leaves after inhibition of corresponding gene expression. The size of the area of each point is equal to the product of the horizontal and vertical coordinates, indicating the size of the effect of inhibiting this gene on caffeic acid yield. The function of “reaction steps” is to calibrate the caffeic acid content in the instantaneous conversion test results. The higher influence weight indicates more caffeic acid increased after the inhibition of target genes in tobacco. CA (caffeic acid). G) The influence weight of different four-gene engineering combination strategies. The influence weight of the strategies is the summary of each gene's influence weight in tobacco.

To narrow down the pool of regulated genes, we calculated the range and variation of expression levels for 49 genes (Supplementary Fig. S1B and C). The results indicate that most genes exhibit relatively low levels of expression variation and narrow expression range, suggesting that their regulatory impact on CA metabolism may be suboptimal (Fig. 1E). Consequently, only genes with a relative expression range exceeding 2,500 and a standard deviation greater than 0.5 were considered, resulting in a selection of ten genes that met the criteria. Subsequently, we conducted a KEGG analysis to explore the functions and metabolic pathways of these ten genes, ultimately excluding three genes with unclear functions or pathways. The remaining seven genes, COMT (CA O-methyltransferase), CCoAOMT (caffeoyl CoA O-methyltransferase), GlyA (glycine hydroxymethyltransferase), CHS (chalcone synthetase), MYB (MYB family transcription factors), ANR (anthocyanidin reductase), and 4CL (4-coumarate CoA ligase), displayed strong association with CA.

To test the computational model strategies for their modulation of CA content in planta, we utilized Agrobacterium tumefaciens-mediated transient expression of artificial microRNA (amiR) in Nicotiana. benthamiana leaves to silence target genes (Supplementary Fig. S2A and B). Then, we investigated the impact of gene expression inhibition on CA production (Supplementary Fig. S2C and D). Furthermore, we investigated the relationship between the metabolic pathways of these genes and CA metabolism, as shown in Supplementary Fig. S2E. We also analyzed how the suppression of these genes might impact the accumulation of CA, considering their respective positions within the metabolic pathway. It is noteworthy that four genes 4CL, COMT, CCoAOMT, and MYB11 influence highly (Fig. 1F), indicating a substantial impact on CA production, whereas the influence weight for the remaining genes was comparatively low. To achieve the most effective gene combination strategy, we utilized these weight of influence on CA to forecast the outcome of various engineered strategies involving single-gene (Supplementary Fig. S2F), dual-gene (Supplementary Fig. S2G), three-gene (Supplementary Fig. S2H) and four-gene (Fig. 1G) on CA yield. The findings confirmed that the four-gene strategy incorporating 4CL, COMT, CCoAOMT, and MYB11 achieved the highest weight of influence on CA. Given the low influence weight of other genes and the minimal incremental benefit of adding more genes, strategies involving four or more genes were deemed unnecessary.

Multiplex metabolic flux regulation strategies for precursor improvement

Our previous study demonstrated that eFBP BY-2 cells fed with 1-mm CA effectively increased luminescence, and stress factors such as cold and UV exposure further enhanced the light emission by inducing the synthesis of CA (Zheng et al. 2023). Additionally, another study indicated that infused leaves of glowing plants with CA and hispidin precursors could enhance light emission, with hispidin showing faster and stronger intensification of bioluminescence (Mitiouchkina et al. 2020). These findings suggest that a higher metabolic flux of CA and hispidin leads to stronger bioluminescence. Building on successful computational model strategies for modulating CA content in plants, we sought to investigate the impact of silencing specific genes involved in metabolizing caffeoyl CoA and its derivatives, thus accumulating more hispidin. Studies have shown that the CHS enzyme utilizes acetate units from malonyl CoA for chain elongation and utilizes p-coumaryl CoA as the natural starter molecule to produce naringenin chalcone. The enzyme exhibits versatility in accepting a variety of nonphysiological substrates, including aliphatic and aromatic CoA thioesters (Yahyaa et al. 2017), leading to the production of structurally and chemically distinct nonnatural polyketides, thus diverting the hispidin pathway. Since hispidin is not naturally present in wild-type (WT) tobacco (Nicotiana tabacum) plants, we utilized agrobacterium-mediated transient expression of amiR to target five specific genes (Nt4CL, NtCOMT, NtCCoAOMT, NtCHS, and NtMYB11) in eFBP plants. This was done to investigate the effect on hispidin levels after silencing these target genes. Reverse transcription quantitative PCR (RT-qPCR) and subsequent LC/MS analysis revealed a significant increase in CA content upon silencing the four target genes NtCOMT, NtCCoAOMT, NtCHS, and NtMYB11(Supplementary Fig. S3A and B), while silencing the genes Nt4CL, NtCOMT, NtCCoAOMT, and NtMYB11 resulted in the accumulation of more hispidin (Supplementary Fig. S3C). These results suggested a potentially effective strategy for enhancing bioluminescence in plants.

When comparing the effectiveness of two common gene-silencing technologies, amiR and CRISPRi (CRISPR interference), in eukaryotic organisms, the CRISPRi system utilized the transcriptional repression domain KRAB to individually repress multiple target genes (Li et al. 2014, 2020; Alerasool et al. 2020). However, this inhibition was not as effective for multiple redundant gene families as it was with amiR strategy. Neither endogenous miRs nor amiRs can silence a target gene with more than five mismatches. Our data demonstrate that the amiR technology enables up to four nucleotide mismatches in target sequences (Llave et al. 2002; Schwab et al. 2006; Li et al. 2013), showcasing its advantage in inactivating redundant genes within a gene family, which is a common phenomenon in plant genomes (Supplementary Fig. S4A). To harness the biggest advantages of the amiR system, the ability to target multiple genes families at once, our modular cloning system offered vectors for silencing multiple genes simultaneously. Taking inspiration from the efficient generation of multiple Cas9-associated guide RNAs in plant protoplasts using the Csy4-processing system (Cermák et al. 2017), also the successful application of endogenous tRNA-processing system to generate multiple amiRs (Zhang et al. 2018), we investigated whether a hybrid approach could be used to produce five amiRs for co-silencing CA and hispidin metabolic genes families (Nt4CLs, NtCOMTs, NtCCoAOMTs, NtCHSs, and NtMYB11) in tobacco (Fig. 2A). Our results demonstrated successful mRNA repression of the target genes by agroinfiltration assay in eFBP plants. This led to a significant increase in the accumulation of CA by 1.7 fold (Fig. 2B) and hispidin by 2.0 fold (Fig. 2C) in tobacco (Nicotiana benthamiana) leaves. Notably, MYB transcription factor, including NtMYB11, plays a crucial role in regulating the production of phenylpropanoid-derived compounds such as lignin, flavonoids, and anthocyanins (Liu et al. 2015). Further investigation through RT-qPCR analysis and subsequent physiological analysis revealed that silencing NtMYB11 effectively downregulates the expression of phenylpropanoid metabolic-related genes, including F3H, FLS, CHS, and CCR (Fig. 2D and Supplementary Fig. S3D). This led to an increased flow of CA and hispidin precursors for FBP (Supplementary Fig. S3C). These findings shed light on the mechanism by which NtMYB11 functions in the regulation of CA and hispidin accumulation in tobacco (N. benthamiana) plants.

Figure 2.

Figure 2.

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Optimization the strategies of accumulation caffeic acid and hispidin in plants. A) Schematic diagram of pre-amiR (precursor artificial microRNA) arrays and in vivo processing by the endogenous Csy4-processing system. HPLC analysis of amiR array strategies for accumulation of B) CA (caffeic acid) and C) hispidin contents in eFBP tobacco. The data from three individual experiments involving different plants were compared using t-tests on the same plants. Grey circles represent EV, and pink circles represent amiRs. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). D) The transcript level of F3H, FLS, CHS, and CCR by RT-qPCR in leaves from eFBP tobacco agroinfiltrated with EV (empty vector) and amiR-MYB11, respectively. The data from three individual experiments using different plants, grey circles representing EV, black circles representing amiR-MYB11. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). E) Suggested reaction catalyzed by TAL, HpaB and HpaC to convert tyrosine to caffeic acid. TAL, tyrosine ammonia lyase; HpaB, 4-hydroxyphenylacetate 3-monooxygenase; HpaC, 4-hydroxyphenylacetate 3-monooxygenase, reductase component. F) Introduction of the THH (TAL, HpaB and HpaC) pathway increased CA production in N. benthamiana. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01).

CA is commonly derived from phenylalanine or tyrosine in organisms via the shikimate pathway (Maeda and Dudareva 2012). Plants have the ability to convert phenylalanine to CA, but do not possess the necessary enzyme to efficiently convert tyrosine directly to CA. Based on a study by Jones et al. (2016), we decided to introduce a heterologous pathway for CA synthesis. This involved incorporating tyrosine ammonia lyase (TAL) from Rhodotorula glutinis and bacterium-derived h4-hydroxyphenylacetate 3-monooxygenase components (HpaB, HpaC) to efficiently convert tyrosine to CA (Fig. 2E). To test the feasibility of incorporating a microbial-derived pathway for CA synthesis from tyrosine into plants, we utilized a modular cloning system to construct the necessary genes. Through an agroinfiltration assay, we confirmed that the production of CA from tyrosine could be effectively transferred to plants, resulting in a 1.6-fold increase in CA production (Fig. 2F). This suggested that the microbe-derived CA synthesis pathway can be successfully integrated into the plant genome.

Cross-species silencing of multiple pathways using amiR arrays

To evaluate the combined effect of improved CA accumulation in plants, we grouped them into two sets: eFBP1 (FBP, TAL, HpaB, and HpaC) and eFBP2 (FBP, TAL, HpaB, HpaC, and amiRs targeting 4CLs/COMTs/CCoAOMTs/CHSs/MYB11) (Fig. 3A). In agroinfiltration experiments, both sets resulted in stronger luminescence compared to FBP, with eFBP2 performing twice as well as eFBP1 (Fig. 3B). Furthermore, we analyzed the levels of CA and hispidin in tobacco leaves expressing FBP, eFBP1, and eFBP2 modules respectively. LC–MS/MS data showed that eFBP1 produced 2.2 times more CA and 2.0 times more hispidin compared to FBP (Fig. 3C), while eFBP2 accumulated 1.6 times more CA and 1.5 times more hispidin than eFBP1 (Fig. 3C). These results further provided physiological evidence of the significant impact of microbial-derived tyrosine pathways and amiR arrays on enhancing plant bioluminescence.

Figure 3.

Figure 3.

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eFBP2 effectively generates stronger autoluminescence across the genus of Nicotiana. A) Schematic illustrations of assembled DNA modules eFBP1 and eFBP2 for enhancing bioluminescence, T, terminator. B) Bioluminescent intensity analysis of infiltrated N. benthamiana leaves with FBP, eFBP1, and eFBP2 modules after 72 h, respectively. Scale bars, 1 cm. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). C) Analysis of the content of caffeic acid and hispidin from infiltrated leaves after 72 h. Error bars indicate means ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). D) Measurement of average photons from infiltrated Nicotiana plants (N. alata, N. tabacum) leaves with FBP and eFBP2 modules after 72 h respectively. Scale bars, 1 cm. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). E) Total amount of caffeic acid and hispidin in above leaves. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). RT-qPCR assay of amiR targets of multi-species in the genus Nicotiana as F)N. tabacum, G)N. alata, and H)N. benthamiana. Error bars indicating means ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01), and the statistical analyses described apply to panels F–H).

The amiR has the ability to recognize fewer than five mismatched target sequences, resulting in more efficient targeted silencing (Schwab et al. 2006; Teotia et al. 2016). This feature has enabled us to simultaneously target several sequence-conserved genes and produce enhanced bioluminescence within various Nicotiana species such as N. tabacum, N. alata, and N. benthamiana (Fig. 3D and Supplementary Fig. S4A and B). Subsequent metabolite analysis revealed a significant increase of CA and hispidin across all three Nicotiana species as a result of silencing these targets (Fig. 3C and E). Further RT-qPCR analysis of agroinfiltrated leaves showed a reduction in the transcript levels of 4CLs, COMTs, CCoAOMTs, CHSs, and MYB11 after injecting eFBP2 module compared to the FBP module (Fig. 3F–H). Since genes within the same genus or family tend to have similar sequences, using amiR arrays can be a powerful tool for engineering multiplex pathways in plants of the same genus.

Generation of enhanced bioluminescent plant

To further characterize eFBP2, we utilized Agrobacterium-mediated transformation to create transgenic tobacco (N. tabacum) lines expressing eFBP2 (Supplementary Fig. S5A–C). The enhancement of eFBP2 was confirmed through screening with a photographic instrument equipped with a CCD camera (Supplementary Fig. S5B and C) and RT-qPCR assay to confirm successful gene expression (Supplementary Fig. S5D and E). The eFBP2 flowers exhibited a maximum intensity of 1.21 × 1012 photons/min/cm2 across three independent lines, surpassing our previously reported eFBP plants (Zheng et al. 2023) by a factor of four (Fig. 4A–C). This remarkable increase in photon emission is more than 30 times greater than that observed in the original FBP plant from the seminal study (Mitiouchkina et al. 2020). Further analysis of metabolism revealed that the elevated intensity observed in eFBP2 plants can be linked to notably higher concentrations of CA and hispidin, in comparison to eFBP plants (Fig. 4D). In comparison to eFBP, the eFBP2 flowers displayed remarkably bright bioluminescence that was visible to the naked eye without the need for dark adaptation. This allowed us to capture light-emitting plant images in the dark using consumer-grade cameras with a 10-second exposure (Supplementary Fig. S6A–C), and the autoluminescence from the glowing plants was immediately visible in a dark room using a commercial video camera (Supplementary Videos S1 and S2). Interestingly, the bright light emitted from the flowering eFBP2 tobaccos (N. tabacum) was significant enough to illuminate their surroundings and clearly visualize small words in the dark (Fig. 4E and Supplementary Fig. S6A). The brightness is close to the plants infused with luciferin and luciferase via nanoparticles (Kwak et al. 2017), but their approach is currently expensive and not self-sustainable. Our glowing plants almost meet the consumer demand for night decorations at home.

Figure 4.

Figure 4.

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Creation of enhanced bioluminescent plant. A) Representative bioluminescent image of eFBP and eFBP2 transgenic lines. WT as negative control. eFBP lines were described previously (Zheng et al. 2023). Scale bars, 1 cm. B) Statistical analysis of average photons emission from leaves and flowers of eFBP and eFBP2 transgenic lines. ns, No signal. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). C) Photos were taken for eFBP2, eFBP, and WT lines at 30 DAG in ambient light with 1/200 s exposure and in the dark with 10-sec exposure, respectively. Scale bars, 2 cm. D) LC–MS/MS analysis of caffeic acid and hispidin contents in leaves from eFBP and eFBP2 transgenic seedlings. ns, No signal. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). E) The performance of tobacco constitutively expressing eFBP2 at 80 DAG, captured with a Nikon D750 camera and 10-s shutter speed (see Materials and methods), Imaging was performed at room temperature. Scale bars, 3 cm. F) Total lignin content in leaves of eFBP and eFBP2 transgenic lines. Values are mean ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (**P ≤ 0.01). G) Phloroglucinol staining in cross-sections of WT and transgenic lines (eFBP and eFBP2). Scale bars, 0.1 cm. H) Average content of anthocyanin and flavonoids in eFBP and eFBP2 lines. Error bars indicate means ± SD (n = 3). Statistical significance was assessed using two-tailed t-tests (*P ≤ 0.05, **P ≤ 0.01). I) The ECE (energy conversion efficiency) of eFBP2 transgenic lines. The wavelength of green light emitted by FBP is λ1 = 520 nm, the wavelength of red light emitted by gas exchange system is λ2 = 620 nm. h, Planck constant, c, the speed of light. N1, photons emission from eFBP2 transgenic plants. N2, photons assimilated by eFBP2 transgenic plants.

However, the enhanced glowing plants exhibited significantly reduced plant height, while there were no changes in flowering time or pollen fertility (Supplementary Fig. S6B–G). This suggested that manipulating the extrinsic metabolic pathway has a significant effect on plant development, particularly in relation to plant height. Previous studies have demonstrated the involvement of CA metabolic genes, including 4CLs, COMTs, CCoAOMTs, and CHSs, along with the regulatory gene MYB11, in catalytic reactions of the traditional phenylpropane pathway, which is necessary for maintaining normal levels of lignins, flavonoids, and anthocyanin contents (Maeda and Dudareva 2012; Ma and Constabel 2019; Muro-Villanueva et al. 2019). To investigate whether these metabolites are affected in plants, physiological analysis revealed a significant reduction in the accumulation of lignins, flavonoids, and anthocyanins (Fig. 4F–H), suggesting that redirecting metabolic flux to increase FBP precursors has a detrimental effect on the developmental process of plants.

Before the versatile application of robust FBP was achieved, the main question was whether photosynthetic plants could derive enough energy from sunlight to produce bioluminescent output. Plants can only absorb about 45% of total solar energy, with most of the energy being lost in the reflectivity of leaves and the absorption spectrum of chlorophyll. This results in plants being able to theoretically use between 3% to 6% of total solar radiation, with the energy being stored as chemical energy in biomass. However, approximately half of this energy is typically lost through photoprotection and respiration processes, leaving only around 1.5–3% available for use (Grace et al. 1995; Rosati and Dejong 2003; Zhu et al. 2012). To estimate the energy conversion efficiency (ECE) of eFBP2 plants in light conversion, a simplified method was established assuming that all leaves in the canopy have the same ECE on a net photosynthesis basis. Using a portable gas exchange system with an incident red light spectrum at 620 nm, the photons assimilated by eFBP2 transgenic plants were measured. The calculated ECE using the model in Fig. 4I was found to be extremely low, approximately at 3.95 × 10−3% (Fig. 4I). Additionally, the net photosynthesis (Pn), stomatal conductance (Gs), transpiration rate (Tr), and quantum efficiency of CO2 assimilation (PhiCO2) of eFBP2 plants were not statistically different from those obtained from eFBP and WT plants (Supplementary Fig. S6H–K). These data suggested that the strategies used to boost bioluminescence have limited influence on the photosynthesis of autoluminescent plants. Given the low luminescence intensity and ECE, we can infer that the phenotypic penalties observed in eFBP2 plants may be a result of the unintended silencing of target genes. The intensity of the eFBP2 plants only consumes a tiny percentage of assimilated energy, indicating that there is great potential for enhancing the bioluminescence system by optimizing metabolic flux.

Conclusion

Building on previous research that has effectively used computational and experimental methods to study and enhance metabolic flux in microbial and mammalian cells (Song et al. 2014; Jenior et al. 2020; Khana et al. 2022), we have developed a transcription-metabolism model that includes cross-regulatory to explore the predicted impact of CA biosynthesis in Nicotiana base on the published data in Populus trichocarpa (Fig. 1). Previous studies have successfully established the validated agroinfiltration method to verify the heterologous metabolic pathways in Nicotiana (Nett et al. 2020; Reed et al. 2023; Jiang et al. 2024). Our model predicted that through multi-amiRNA approach we can significantly accumulate more CA (Figs. 2B and 4D), while reducing lignins, anthocyanins, and flavonoids (Figs. 4F–H and 5), with the guidance of the computational model, we apply amiR array strategy to successfully silence five-gene families in multiple species in Nicotiana (Figs. 3B–H and 5 and Supplementary Fig. S4A and B). Which strategy demonstrates a more potent capability in regulating intricate pathways within multiplex gene families when compared to CRISPRi tools. The results provide insights into the potential of optimization in multiplex metabolic pathways for plant improvement. Moreover, the introduction of a heterologous pathway for synthesizing CA from tyrosine, utilizing the TAL, HpaB, and HpaC genes, serves to enhance the availability of precursor (Figs. 2E and F, 4D, and 5), the data demonstrate successful implementation of a bacteria-derived CA pathway (Jones et al. 2016) transplanted into a plant, resulting in the reconstruction of a shorter and more efficient tyrosine-derived CA pathway in the plant (Fig. 5). Future work will involve experimentally testing and validation of the computational predictions, thus developing a systematic optimization for multi objectives to obtain a desired set of traits, while reducing negative impacts to plants.

Figure 5.

Figure 5.

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Integration of the FBP into engineered caffeic acid and hispidin metabolic network in plants. Biochemical reactions of fungal luciferin biosynthesis and recycling coupled with sustained caffeic acid in plants. Black solid arrows indicate a direct reaction, while grey dashed arrows represent multiple steps. Enzymes from microbes are highlighted in orange font, while compounds increased in the engineered network are in red font. PAL, phenylalanine ammonia lyase; C4H, cinnamic acid 4-hydroxylase; 4CL, 4-hydroxycinnamate:CoA ligase; TAL, tyrosine ammonia lyase from Rhodotorula glutinis; HpaB, 4-hydroxyphenylacetate 3-monooxygenase; HpaC, 4-hydroxyphenylacetate 3-monooxygenase, reductase component; C3H, coumarate 3-hydroxylase; C4H, cinnamic acid 4-hydroxylase; CAD, cinnamyl alcohol dehydrogenase; CCoAOMT, caffeoyl CoA 3-O-methyltransferase; CCR, cinnamoyl-CoA reductase; CHI, chalcone isomerase; CHS, chalcone synthase; COMT, caffeate/5-hydroxyferulate 3-O-methyltransferase; DFR, fihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F5H, ferulate 5-hydroxylase; FLS, flavonol synthase; HCT, 4-hydroxycinnamoyl CoA:shikimate/quinate hydroxycinnamoyltransferase; C3′H, 4-coumaroyl shikimate/quinate 3′-hydroxylase; CSE, caffeoyl shikimate esterase; HispS, hispidin synthase; NPGA, 4′-phosphopantetheinyl transferase; H3H, hispidin-3-hydroxylase; Luz, luciferase; CPH, caffeoylpyruvate hydrolase; ATP, adenosine triphosphate; Mal-CoA, malonyl CoA; CoA, co-enzyme A; NAD(P)H, nicotinamide adenine dinucleotide (phosphate). The green down arrows indicate silencing by amiRs.

The intricate regulatory network in plants orchestrates energy distribution to determine the ceiling of bioluminescent lighting, autoluminescent plants could derive a tiny amount of energy from sunlight to give light output. Photoautotrophic plants are able to theoretically use 3% to 6% total solar radiation (Grace et al. 1995; Rosati and Dejong 2003). It has been estimated that glowing plants must divert approximately 0.3% of their stored energy to generate bright enough autoluminescence to match traditional streetlights (Reeve et al. 2014). Based on the photosynthetic efficiency and luminous energy output of eFBP2 plant (Fig. 4I), our efforts to increase light intensity fourfold (Fig. 4A–C and Supplementary Fig. S6A and B) still leave two orders of magnitude to achieve the desired brightness as traditional streetlights. However, this hypothesis is based on simplified energy efficiency. In reality, plants use stored energy not only to survive but also for growth, development as well as stress adaptation. Plants collectively produce a wide variety of metabolites, estimated to be more than ten thousand, with many of these compounds playing essential roles in environmental adaptation (Alseekh et al. 2021; Wang et al. 2022). In engineered auto bioluminescence plants, our goal is to divert a portion of this energy to light production by optimizing metabolic flux in eFBP2 plants (Fig. 5). With an increasing amount of transcriptome and metabolome data available in public databases, computational predictions can be used to optimize metabolic flux. Most recently, Shakhova et al. (2024) reported that using optimization of H3H and Luz-catalyzed steps, they succeeded in enhancing bioluminescence by one to two orders of magnitude to the original FBP plant released in their previous study, suggesting the prominent improvement in enzyme engineering. Altogether, through the optimization of metabolic flux to accumulate more precursors, and using directed evolution to improve core enzymes in the FBP cycle, with the two strategies ever improving, increasingly amazing bioluminescent systems will be available. These achievements could herald a bright and exciting future in developing advanced imaging tools and brighter glowing plants.

Materials and methods

Co-expression analysis of transcriptome data

The 366 transcriptome raw data were obtained from the National Center for Biotechnology Information (NCBI) with the GEO accession number GSE78953 (Matthews et al. 2020). Each transcriptome underwent a series of processing steps as published study (Li et al. 2024a). Initially, FastQC (v. 0.12.1) software within the R package was used to detect adapters in each RNA-Seq read. Subsequently, adapters and low-quality reads were removed from the RNA-Seq data using Trim-galore (v. 0.6.10), followed by a reassessment of each read using FastQC. Next, the RNA-Seq data that passed the quality control criteria set by FastQC were aligned to the genome of poplar (P. trichocarpa) (Taxonomy ID: 3694) using Hisat2 (v. 2.0.5) and the expression levels were quantified by FeatureCounts (v. 2.0.3) software to generate an expression abundance data table for each transcriptome. The abundance data obtained from counting for each transcriptome were then standardized using TPM and transformed using log2(x + 1) (Wagner et al. 2012). After standardizing the transcriptome data, co-expression analysis was performed using the WGCNA software in R. To ensure accuracy in subsequent analyses, genes showing the top 50% variance in relative expression levels were selected for further investigation. Finally, co-expression analysis graphs were generated to visualize genes in different modules, and modules for gene expression analysis were chosen for further examination.

Mining of regulatory genes

The module containing key genes was selected for analysis. The genes within this module were imported into Cytoscape to identify potential regulatory genes connected to the key genes in the network. Using Venn diagrams, distinct key genes were overlapped to create a set of potential regulatory genes. To further refine the potential regulatory gene set, a detailed analysis was conducted on each gene. This analysis focused on assessing the range of expression abundance and the extent of expression change for each gene. The expression abundance range was determined by subtracting the minimum value from the maximum value of the gene's expression level across 366 transcriptome data samples. Additionally, the degree of expression change was calculated by determining the standard deviation of each gene's expression level after it was standardized by the mean value.

Quantification of gene influence on CA production

To assess the impact of individual genes on CA yield, we employed a metric based on the relative increase in CA levels and the gene's positional proximity within the CA biosynthetic pathway (Küken et al. 2019). Specifically, the influence weight assigned to each gene was calculated by multiplying the relative increase in CA concentration attributable to that gene by the inverse of the number of metabolic steps between the gene product and the final CA product. This calculation was performed for each gene under investigation. For each proposed combination of gene modifications, the weight of influence on CA was assigned based on the sum of influence weights of all genes included in the combination. The rationale behind this scoring system was to quantitatively estimate the combined effect of multiple gene modifications on CA production, with a higher influence weight indicating a potentially greater enhancement in CA yield due to the synergistic effects of inhibiting the selected gene combination.

Vector construction

The coding sequences of various genes were optimized for expression in N. tabacum and synthesized by GenScript Biotech Corporation. Specifically, the NnHispS, NnH3H, NnLuz and NnCPH genes from Neonothopanus nambi, as well as the AnNPGA gene from Aspergillus. nidulans, and TAL, HpaB, HpaC, and Csy4 genes were included in this study. In our previous study, we developed the FBP construct, which consists of the following genes: NnHispS, NnH3H, NnLuz, NnCPH, and AnNPGA (Zheng et al. 2023). Overlap PCR was utilized to produce HpaB-P2A-HpaC and TAL-P2A-Csy4 fragments. The donor vectors, pYL322d1 and pYL322d2, were constructed using traditional restriction enzyme-based methods for the genes NnHispS, NnCPH, NnH3H, AnNPGA, NnLuz, TAL-P2A-Csy4, and HpaB-P2A-HpaC. Subsequently, these fragments were sequentially inserted into the binary vector pYLTAC380GW to create the construct eFBP1, which was developed based on the previously reported TransGene Stacking II system (Zhu et al. 2017).

Using the WMD3 (Web microRNA designer 3) website developed by Schwab et al. (Schwab et al. 2006), four suitable primer sequences were automatically designed for Nt4CL1, NtCOMT, NtCCoAOMT, and NtCHS. These primers were then used to synthesize the corresponding artificial miRNAs. The pRS300 template was utilized, and the resulting products were amplified through two rounds of overlap PCR. They were subsequently ligated into the Xba I/Sac I-digested pCAMBIA1300-P35S vector using conventional restriction enzyme-based techniques. This led to the creation of pCAMBIA1300-amiR-4CLs, pCAMBIA1300-amiR-MYB11, pCAMBIA1300-amiR-COMTs, pCAMBIA1300-amiR-CCoAOMTs, and pCAMBIA1300-amiR-CHSs. Following verification, inhibitory amiRs were chosen and linked together via reverse PCR into the pYLMF-H vector, resulting in the formation of the pYLMF-H-amiR array. The amiR array was then inserted into the selectable marker/marker-excision cassette within the marker-free donor vector pYLMF-H through reverse PCR. The pYLMF-H vector contains resistance to hygromycin. Next, we employed the Gateway BP reaction using the Gateway BP Clonase II Enzyme Mix kit (catalog no.11789-020, Invitrogen) protocol to recombine the constructed 380GW with the amiR array-containing pYLMF-H, resulting in the generation of eFBP2. The resulting constructs underwent analysis and identification through Not I digestion, and the correctness of all plasmids' sequences was confirmed through Sanger and Illumina sequencing before use. The primers utilized in the plasmid constructions can be found in Supplementary Data Set 3, while the vectors and detailed sequences are listed in Supplementary Data Set 4.

Transient expression for in vitro assays

The constructs of pre-amiR and FBP plasmids were individually transformed into the A. tumefaciens strain EHA105. Bacterial clones were cultured in a 50-mL glass flask at 28 °C until reaching an OD600 of 0.8–1.0. Subsequently, the bacteria were washed with infection buffer containing 10 mm MgCl2, 10 mm MES, and 150 μM As at pH6.0, which was then resuspended in the same buffer to an OD600 of 0.8–1.0 and allowed to stand for 2–3 h before injection. For infiltration, 1 mL of the final culture was used to infiltrate the underside of 5-week-old solanaceae plants including N. tabacum, N. alata, and N. benthamiana. After infiltration, leaves were harvested 3 days later for further assays such as RT-qPCR, bioluminescence signal imaging, or metabolite measurement. To quantify the relative increase in CA content subsequent to gene expression inhibition, the following calculation was employed: the difference between the average CA content post-inhibition and the average CA content of the WT was divided by the average CA content of the WT. Additionally, the relative inhibitory efficiency attributed to each gene was determined by calculating the difference between the average gene expression in the WT and the average gene expression following inhibition, which was then divided by the average gene expression of the WT. This approach allowed for the assessment of changes in CA concentration and gene expression levels in response to gene expression inhibition.

Plant transformation and growth

The eFBP2 plasmid was transferred into A. tumefaciens strain EHA105. The bacteria were cultured in flasks on a shaker overnight at 28 °C in LB medium containing 25 mg/L rifampicin and 50 mg/L kanamycin sulfate. They were grown in flasks at 200 rpm and 28 °C until they reached an OD600 of 0.8, after which they were collected by centrifugation. The bacteria were then resuspended in liquid MS0 medium to an OD600 of 0.6 for tobacco (N. tabacum) transformation and incubated still for 2 h at room temperature. For the tobacco transformation, fully expanded leaves from 4-week-old tobacco plants (N. tabacum cv. Zhongyan 100) were utilized. The leaves were prepared by removing the midrib and edge, and cutting the lamina into small pieces approximately 1 cm2 in size. The detailed information of transformation and eFBP tobacco lines used in this study were described previously (Zheng et al. 2023).

Seedling plants are initially grown in a growth chamber for 30 days post-germination under conditions of 8,000-lx light intensity and a temperature photoperiod of 22 °C in the dark for 12 h and 25 °C in the light for 12 h. After 30 days, the seedlings are transferred to a larger glass greenhouse with a temperature photoperiod of 25 °C in the dark for 12 h and 30°C in the light for 12 h, with an increased light intensity of 12,000 lux.

Gene expression analysis

All leaves were flash frozen in liquid nitrogen and then homogenized using RNA isolater Total RNA Extraction Reagent (Vazyme Biotech). The first-stranded cDNA was synthesized from 1 µg of RNA using MonScript™ RTIII Super Mix with dsDNase (Two-Step) from Monad Biotech, China, following the manufacturer's instructions. For RT-qPCR analysis, gene transcript levels were quantified using Taq SYBR Green qPCR Premix from Yugong Biolabs with gene-specific primers on a LightCycler480 II Real-Time PCR machine from Roche. The thermal cycling program included an initial denaturation at 95 °C for 1 min, followed by 40 cycles of 95 °C for 10 s, 60 °C for 20 s, and 72 °C for 20 s, detailed information in our previous study (Li et al. 2024b). Each sample was analyzed in at least three biological replicates. NtEF1a was used as the reference gene for normalizing gene expression, and all primer sequences are listed in Supplementary Data Set 3.

HPLC analysis

To measure the amount of CA, 0.1 g of lyophilized powder was first weighed and then transferred to 5 mL of extraction buffer consisting of 70% (v/v) methanol. The resulting mixture was centrifuged at 12,000 rpm for 5 min to collect the supernatant, which was then diluted with 50% (v/v) methanol. Following filtration through 0.22 μm filters, the samples were analyzed using an HPLC system (Agilent 1200) with a Pntulips QS-C18 PLUS column (4.6 mm × 250 mm, 5 μm, Puningtech). The HPLC analysis utilized a gradient program with solvent A [water with 0.1% (v/v) formic acid] and solvent B (acetonitrile) as the mobile phase. The gradient started at 90% (v/v) solvent A and 10% (v/v) solvent B, then the concentration of solvent B was increased to 30% (v/v) over 15 min, reaching 50% (v/v) at 30 min. The flow rate was maintained at 1 mL/min, and the column temperature was held constant at 35 °C. CA was detected at 11.5 min using a standard from Sangon Biotech. Additionally, a range of dilutions of analytical standards were employed to create a standard curve for the quantification of CA concentration.

LC–MS/MS analysis

The leaf shoots that were 50 days old were harvested and immediately frozen in liquid nitrogen. They were then lyophilized in 50-mL Falcon tubes. Approximately 100 mg of the lyophilized powder was measured and transferred to 5 mL extraction buffer containing 70% (v/v) methanol. The extracts were then ultrasonicated in a water bath for 30 min, followed by centrifugation at 13,000 × g for 15 min. The supernatants were filtered through a PVDF syringe filter with a pore size of 0.45 μm and transferred to glass vials for LC/MS analysis. Analytical standards of hispidin were purchased from Sigma-Aldrich and used to set up a standard curve and calculate the concentration of targeted metabolites, following procedures described in a previous study (Zheng et al. 2023). Data collection and processing were done using Analyst 1.6.3 Software.

Physiological measurements

For the lignin assay, we began by preparing 2 mg of dry fine powder from 10-day-old seedlings. This powder was then extracted with 1.5 mL of 80% (v/v) ethanol, followed by centrifugation and washing with ethanol to isolate the precipitate. The precipitate was then dried at 65 °C and acetylated using 750 μL of reagent I from the lignin assay kit (BL893B, Biosharp) at 70 °C for 30 min. The acetylated lignin was then combined with 300 μL of reagent II and 450 μL of acetic acid to create the assay mixture. The absorbance of the mixture was measured at a wavelength of 280 nm using a spectrophotometer at 25 °C. The lignin content was calculated using the formula: lignin (mg/g weight) = [ΔA + 0.0029/17.853] × V1/W × 4, ΔA represents the absorbency.

To quantify flavonoids, 0.03 g of dry fine powder from 10-day-old seedlings was mixed with 1.5 mL of 60% (v/v) ethanol and extracted at 60 °C for 2 h. The mixture was then centrifuged at 12,000 × g for 10 min, and the resulting supernatant was placed on ice. Next, 50 μL of the extract was combined with 15 μL of reagent I and incubated for 6 min. This was followed by the addition of 30 μL of reagent II, another 6-min incubation, and finally 105 μL of reagent III, then incubated for 15 min, as per the manufacturer's instructions (BL867B, Biosharp). The absorbance of the sample was measured at 510 nm using a spectrophotometer at 25 °C. The total flavonoid content was calculated using the formula: total flavonoid content (mg/g DW) = [(ΔA + 0.0049)/1.6277 × 0.05 mL]/(1.5 mL/0.05 mL × 0.03 g) × dilution factor. For anthocyanin assay, begin by placing 0.3 g of fresh leaf from 10-day-old seedlings into 1 mL of a solution consisting of 1% HCl–methanol (v/v). Shake the mixture at 50 rpm for 18 h at room temperature. After this incubation period, centrifuge the solution at 14,000 rpm for 1 min and transfer 0.4 mL of the supernatant into a new solution containing 0.6 mL of 1% HCl–methanol (v/v). Next, measure the absorbance of the supernatant at 530 and 657 nm. To determine the anthocyanin content, use the formula ΔA = (A530 − 0.25 × A657) per gram of leaf tissue.

To analyze pollen fertility, mature anthers from WT, eFBP, and eFBP2 plants were placed in a 1% (w/v) iodine and potassium iodide (I2–KI) solution at room temperature. This experiment was conducted with three biological replicates. The stained pollen grains were then observed and photographed under a Nikon SMZ800 microscope.

Measurement of photosynthesis-related indicators

Gas exchange parameters were measured on the fifth leaf using a portable gas exchange system (LI-6800, LI-COR Biosciences, Lincoln, NE, USA) with a 5-cm diameter fluorescent leaf chamber. The gas exchange constants, including net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs), were measured under red LED light with a photosynthetic photon flux density of 150 μmol m−2 s−1 and 400 ppm ambient CO2. Leaf temperature and relative humidity were maintained at 25 °C and 60%, respectively. The quantum efficiency of CO2 assimilation (PhiCO2) was calculated according to the manufacturer's instructions. Each experiment included three biological replicates and a total of three independent lines were conducted.

Histological analysis

During the growth period, histological staining was performed on tobacco (N. tabacum) samples with three biological replicates. The procedure involved sampling fresh stems, cutting into slices. The slices were then stained with phloroglucinol solution for 2 min, followed by soaking the slides in 50% (v/v) HCl and examined under an optical light microscope.

Plant imaging

Plant bioluminescence signal imaging and photon dose calculations were conducted using the NIGHTSHADE LB985, a German-made instrument featuring a highly sensitive back-thinned CCD camera at its core. Samples were positioned internally at a height of approximately 40 cm from the top of a light-protected dark box. Bioluminescent images were captured with a 10-second exposure, followed by the selection of regions for photon calculation. The data were then exported for further analysis. Ambient light images were taken post-luminescence measurements, with all other settings remaining at their default values. For dark condition photos of Fig. 4C and E and Supplementary Fig. S6A–C, we utilized a Nikon D750 camera with AF-S17-35 mm F2.8D ED-IF, set at ISO 2000, F6.3, and a 10-second shutter speed. Supplementary Videos S1 and S2 were recorded using a Sony Alpha 1 camera with a Sony GM 50 mm f1.2 lens, at 1/30 f1.2 and iso32000.

Statistical analyses

Quantitative data shown in this article represent mean ± standard deviation (SD) from at least three biological replicates. GraphPad Prism 9 software was used for plotting and graphing and Excel was used for statistical analysis. Statistical significance was assessed using an unpaired two-tailed Student's t-test when comparing two groups. The exact P values are indicated in the figures.

Accession numbers

Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers:_ NnHispS, QJQ48095.1, NnCPH, QJQ48093.1; NnH3H, QJQ48094.1; AnNPGA, QJQ48097.1; NnLuz, QJQ48096.1; Csy4, 6NE0_L; TAL, KX671121.1; HpaB, BCT02680.1; HpaC, WP_001195556.1.

Supplementary Material

koae236_Supplementary_Data
koae236_supplementary_data.zip (72.6MB, zip)

Acknowledgments

We thank Jen Sheen for critical suggestions to improve the manuscript. Prof. Jianfeng Li (State Key Laboratory of Biocontrol, Guangdong Provincial Key Laboratory of Plant Resources, MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, China) for providing pUC119-AtU6-26pro-2BsaI-gRNA vector. We thank Prof. Yaoguang Liu and Prof. Qinlong Zhu (State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China) for providing TransGene Stacking II (TGSII) system. Huang Yongliang (Westlake Education Foundation, Hangzhou, China) for taking videos of glowing plants. Prof. Min Ren (Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China) for providing genetic material Nicotiana tabacum cv. Zhongyan 100.

Contributor Information

Jieyu Ge, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Xuye Lang, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.

Jiayi Ji, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Chengyi Qu, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

He Qiao, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.

Jingling Zhong, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Daren Luo, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Jin Hu, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Hongyu Chen, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Shun Wang, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Tiange Wang, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Shiquan Li, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.

Wei Li, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China.

Peng Zheng, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China.

Jiming Xu, State Key Laboratory of Plant Environmental Resilience, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Hao Du, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China.

Author contributions

J.G., J.J. and H.D. initiated the project and designed all experiments, J.G., J.J., C.Q., H.D., J.Z., J.H., D.L., H.C., S.W., T.W., S.L., J.X., W.L. and P.Z. performed experiments. X.L. and H.Q. performed bioinformatics analyses. H.D. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Supplementary data

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

Supplementary Figure S1. Mining of regulatory genes across 366 transcriptomes.

Supplementary Figure S2. Identification of amiR strategies to improve the biosynthesis of caffeic acid in tobacco.

Supplementary Figure S3. amiR strategy to silence multiplex targets in plants.

Supplementary Figure S4. Identifying the function of amiR array strategies across Nicotiana genera.

Supplementary Figure S5. Identification of the eFBP2 module and transgenic tobacco lines.

Supplementary Figure S6. Performance and photosynthetic parameters of eFBP and eFBP2 lines.

Supplementary Data Set 1. ME genes list.

Supplementary Data Set 2. The summary of regulatory genes.

Supplementary Data Set 3. Primers used in this study.

Supplementary Data Set 4. Vectors and sequences used in this study.

Supplementary Video S1. Immediate visualization of the autoluminescence of eFBP2 seedlings in the dark room.

Supplementary Video S2. Video-rate luminescence imaging of transgenic seedlings expressing eFBP2.

Funding

This work was financially supported by the Key Research and Development Program of Zhejiang (2020C02002), Zhejiang University Global Partnership Fund, and Fundamental Research Funds for the Central Universities (K20200168).

Data availability

The data underlying this article are available in the article and in its online supplementary material.

Dive Curated Terms

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

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