There were 14 predicted NtCCoAOMT genes that were searched from the Nicotiana tabacum genome in the NCBI database, of which nine genes were explicitly annotated ( Figure 1A ). The NtCCoAOMT family generally contains around 240 amino acids, all of which contain the caffeoyl-CoA O-methyltransferase domain (PLN02589 or PLN02781). A phylogenetic tree was constructed using seven AtCCoAOMTs from Arabidopsis thaliana and the above 14 NtCCoAOMTs. The results showed that LOC107826706, LOC107792528, LOC107795104, LOC107799225, and LOC107832128 were clustered together with AtCCoAOMT7 (At4g26220), whereas NtCCoAOMT1-6, 3L, 5L, 6L clade with AtCCoAOMT1 (At4g34050), which was confirmed to be the key member involved in the monolignol pathway (Do et al., 2007) ( Figure 1A ). Furthermore, we analyze the tissue expression level of these nine NtCCoAOMTs by qRT-PCR and found that NtCCoAOMT6 was the most highly expressed in tobacco roots and leaves ( Figures 1B, C ), suggesting its critical position in the monolignol biosynthesis. NtCCoAOMT6L shares 95.7% sequence identity and similar expression patterns with NtCCoAOMT6. Because the common tobacco is an allotetraploid formed by combining different chromosomes of two wild species N. sylvestris and N. tomentosiformis (Sierro et al., 2014), we compared NtCCoAOMT6 and NtCCoAOMT6L in these two wild species, respectively. It shows that NtCCoAOMT6 originates from N. tomentosiformis (99% identities), whereas NtCCoAOMT6L originates from N. sylvestris (100% identities), which means they are orthologous genes from different subgenomes. Thus, we created loss-of-function mutants for these two genes in Nicotiana tabacum.

We used CRISPR/Cas9 technology to generate a ccoaomt6 ccoaomt6l double mutant. A common sgRNA target (5′-TTGCCCGTAATCCAAAAGGCTGG-3′) in the two genes was selected ( Figure 2A ). The CRISPR construct was transformed into wild-type Nicotiana tabacum, and the homozygous double mutant was obtained. The double mutant contained a nucleotide insertion in the target sites of CCoAOMT6 and CCoAOMT6L, leading to frameshift mutation, separately ( Figure 2B ). Under natural conditions, compared with the wild type, the mutant has a longer squaring stage, darker green leaves, and vigorous growth and development ( Figure 2C ). Statistical analysis showed that the plant height, leaf length, and leaf width were significantly increased (p < 0.01) ( Figure 2D ).

CCoAOMT is the key methylation enzyme of caffeoyl-CoA, which produces feruloyl-CoA, and is the upstream component of G-lignin and S-lignin monomers. Therefore, we also detected the total lignin content and lignin monomer content in the wild type and the double mutant. The results showed that there was no significant difference in total lignin content between the mutant and the control; however, S-lignin content increased by around 27.5% and G-lignin content decreased by around 24.3% ( Figure 2E ), thus increasing the S/G ratio by nearly 68.4% ( Figure 2F ). The above results prove that tobacco CCoAOMT can control the type of lignin monomer by influencing the step of caffeoyl-CoA converting to feruloyl-CoA.

Furthermore, we detected the content of total sugar and reducing sugar that are important for plant growth and development. Compared with the wild type, the total sugar content in the double mutant leaves decreased by 11.0%, and the reducing sugar content decreased by 14.3% ( Figure 2G ), indicating that the decomposition of sugar may have promoted the synthesis of lignin monomers. In general, the mutation of CCoAOMT6 and CCoAOMT6L has improved the economic traits of tobacco.

Bacterial wilt and brown spot are the main diseases of tobacco plants, caused by Ralstonia solanacearum infection and Alternaria alternata infection, respectively. To further study the effect of the changes in lignin monomer composition caused by CCoAOMT6 and CCoAOMT6L knockout on tobacco disease resistance, Ralstonia solanacearum and Alternaria alternata strains were inoculated on the wild-type and double-mutant leaves, respectively, according to the methods of Guo et al. (2004)and Ma et al. (2017). The disease lesion diameter around the inoculation sites was measured after 72 h of inoculation. It was found that on the wild-type tobacco leaves, the bacterial wilt disease symptoms were obvious and the brown spot disease symptoms developed rapidly. For the mutant leaves, however, the bacterial wilt disease either did not appear or developed slowly, and the development of brown spot diseases disease symptoms is relatively slow ( Figures 3A–C ).

Similarly, we also investigated the inhibitory effect of transgenic tobacco leaf extract on the growth of Ralstonia solanacearum and Alternaria alternata, according to the protocol provided by Guo et al (Guo et al., 2004). The results showed that the ethanol extract of transgenic tobacco leaves indeed inhibited the growth of Ralstonia solanacearum and Alternaria alternata, whereas the extract of wild-type leaves did not ( Figures 3D–F ). This indicates that compounds in the mutant tobacco extract has a “chemical defense” response to Ralstonia solanacearum and Alternaria alternata pathogens. Taken together, it is proved that the knockout of CCoAOMT6 and CCoAOMT6L can enhance the disease resistance of tobacco.

To investigate the effects of S/G-lignin ratio changes on the whole plant metabolism, widely targeted metabolome sequencing was performed between the leaves and roots of the wild type and double mutants. A total of 266 metabolites were detected, including carbohydrates, amino acids and derivatives, organic acids and derivatives, lipids, terpenes and sterols, phenolic acids, nucleosides, flavonoid, and coumarin; different metabolites of roots and leaves were screened ( Tables S1 , S2 ).

Compared with the wild type, 56 metabolites were differently detected from leaves of the double mutant, i.e., 15 upregulated and 41 downregulated ( Figure S2A ). Among them, eight differential amino acid and derivatives (eight downregulated) were identified, namely, histidine, leucine, tyrosine, phenylalanine, targinine, methionine, arginine, and proline. Moreover, there were 17 differential metabolites in the fatty acid pathway, of which four were upregulated and 13 were downregulated. Also, six organic acids were identified and all of them downregulated in the mutant line. There were five differential terpenoids or steroids identified, including two downregulated (campesterol, scoparone) and upregulated (asiatic acid, epiandrosterone, soyasaponin Bb). It also included nine metabolites involved in the shikimate pathway (five downregulated and four upregulated), namely, cinnamic acid, 4-coumaric acid, coumaroylquinic acid, one coumarin, two lignin, and three flavonoids. Scopoletin belongs to coumarin, which was downregulated in the mutant. Lactose and alpha-D-glucose-1,6-diphosphate were involved in the carbohydrate metabolic pathway, both of which were upregulated in the mutant ( Figure 4 ).

Meanwhile, 32 differential metabolites were identified from the roots between the double mutant and the wild type, of which 11 were upregulated and 21 were downregulated ( Figure S2B ). Among them, three differential flavonoid and derivatives (two down and one up) were identified, namely cyanidin-3-O-galactoside, hyperoside, and plantamajoside. Also, two metabolites were found in the fatty acid pathway, of which one was upregulated and one was down-regulated. Moreover, seven organic acids were identified of which three were upregulated and four were downregulated and 13 differential metabolites in the shikimate pathway (nine down and four up) were identified, one coumarin (caffeoylquinic acid up), two anthocyanin (two down), and five flavonoids (two down and three up). Nicotine was also identified, which was downregulated in the mutant line. N,N-Bis (2-hydroxyethyl)dodecylamine was involved in carbohydrate metabolism, which was downregulated in the mutant line ( Figure 5 ).

To better analyze the potential roles of CCoAOMT6 and CCoAOMT6L in primary and secondary metabolic regulation, we conducted transcriptome profiling of the leaves and roots in the double mutant and the wild type. A total of 248 DEGs (differentially expressed genes) were detected in the leaf data, which were composed of 226 genes that were upregulated and 22 genes that were downregulated ( Figures S3A, B ). Moreover, 332 DEGs were detected in the root data, composed of 149 upregulated genes and 183 downregulated genes ( Figures S3A, C ). Only 38 DEGs were identified in both leaves and root, suggesting that the functions of CCoAOMT6 and CCoAOMT6L in different tissues were slightly different ( Figure S3A ).

All the DEGs were annotated according to GO, KEGG, TF, COG, uniProt, InterPro, and WoLF PSORT. The GO terms “protein folding,” “protein complex oligomerization,” “response to hydrogen peroxide,” and “response to reactive oxygen species” are the most highly enriched in both leave and root data ( Figure S4 ). KEGG analysis of the leaf data showed that “protein processing in endoplasmic reticulum,” “vitamin B6 metabolism,” “stilbenoid, diarylheptanoid and gingerol biosynthesis,” “sesquiterpenoid and triterpenoid biosynthesis,” and “flavonoid biosynthesis” were significantly enriched ( Figure S5A ). Meanwhile, KEGG analysis of the root data showed that “protein processing in endoplasmic reticulum,” “galactose metabolism,” “diterpenoid biosynthesis,” “ascorbate and aldarate metabolism,” and “stilbenoid, diarylheptanoid and gingerol biosynthesis” were significantly enriched ( Figure S5B ).

In the leaf data shown in Figure 4 , among primary metabolite pathway biosynthesis enzyme genes, the expression of transcripts coding for carbohydrate such as galactinol synthase 2-like (LOC107783847), galactinol-sucrose galactosyltransferase-like (LOC107810250), and 1,4-alpha-glucan-branching enzyme 3 (LOC107781849) were upregulated and mannan endo-1,4-beta-mannosidase 7-like (LOC107777916) and probable isoaspartyl peptidase/L-asparaginase 3 (LOC107795479) were reduced. Fatty acid elongation gene LOC107809103 in the fatty acid pathway was upregulated ( Figure 4 ). Among flavonoid biosynthesis enzyme genes, the transcripts coding for feruloyl CoA ortho-hydroxylase 1-like (LOC107796566), caffeoyl-CoA O-methyltransferase 6-like (LOC107772621), agmatine coumaroyltransferase-2-like (LOC107794911), caffeoyl-CoA O-methyltransferase 6 (LOC107810774), and trans-resveratrol-di-O-methyltransferase-like (LOC107785995) were downregulated whereas the transcript coding for agmatine coumaroyltransferase-2-like (LOC107761506) was upregulated ( Figure 4 ). In addition, seven genes involving the terpenoid/steroid biosynthesis enzyme gene were detected by transcriptome profiling. Dehydrodolichyl diphosphate synthase 6-like (LOC107814919), 3-hydroxy-3-methylglutaryl-coenzyme A reductase-like (LOC107770799), ent-copalyl diphosphate synthase, chloroplastic-like (LOC107809496), 5-epiaristolochene 1,3-dihydroxylase (LOC107759261), alpha-farnesene synthase-like (LOC107779472), and probable terpene synthase 3 (LOC107819330) were downregulated, while the transcript coding for gibberellin 2-beta-dioxygenase 1-like (LOC107813437) was upregulated ( Figure 4 ).

In the root data shown in Figure 5 , among primary metabolite pathway biosynthesis enzyme genes, the expressions of transcripts coding for carbohydrate such as galactinol synthase 1-like (LOC107812124), bifunctional UDP-glucose 4-epimerase and UDP-xylose 4-epimerase 1-like (LOC107775041), bifunctional UDP-glucose 4-epimerase and UDP-xylose 4-epimerase 1-like (LOC107781891), acid beta-fructofuranosidase-like (LOC107807993), beta-amylase 1, chloroplastic-like (LOC107832261), alpha-amylase-like (LOC107769668), and alpha-galactosidase 3-like (LOC107798945) were upregulated and acid beta-fructofuranosidase-like (LOC107807993) was reduced. 1-(5-Phosphoribosyl)-5-[(5-phosphoribosylamino)methylideneamino] imidazole-4-carboxamide isomerase (LOC107778749) and homogentisate 1,2-dioxygenase-like (LOC107764203) in the amino acid pathway were down- and upregulated, respectively ( Figure 5 ). Among flavonoid biosynthesis enzyme genes, the transcripts coding for caffeoyl-CoA O-methyltransferase 6 (LOC107810774) and caffeoyl-CoA O-methyltransferase 6-like (LOC107772621) were downregulated whereas the transcript coding for a4-coumarate-CoA ligase 2-like (LOC107769758) and peroxidase P7-like (LOC107782926) were upregulated ( Figure 5 ). In addition, four genes involving terpenoid/steroid biosynthesis enzyme genes were detected by transcriptome profiling. The expression of transcripts coding for 8-hydroxygeraniol dehydrogenase-like (LOC107772996), gibberellin 2-beta-dioxygenase 1-like (LOC107782042), gibberellin 2-beta-dioxygenase 1-like (LOC107821505), and gibberellin 2-beta-dioxygenase 1-like (LOC107790388) were upregulated ( Figure 5 ).

The above bacteriostatic experiments showed that there were some antimicrobial agents in transgenic tobacco leaves. Because phenolic acids, coumarins, terpenoids, and other compounds have good biological activities, we screened six compounds such as icariside F2, epiandrosterone, soyasaponin Bb, 3-hydroxycinnamic acid, esculin, and 4-coumaroylcholine from upregulated metabolites (the threshold is 1.5-fold). Aminoacyl-tRNA synthetases (aaRSs) play a key role in protein biosynthesis, and the inhibition of aaRSs affects cell growth (Pisano et al., 2019). The tyrosyl-tRNA synthetase 3D structures of Ralstonia solanacearum and Alternaria alternata were obtained from UniProt (https://www.uniprot.org/). Mupirocin, which target to tyrosyl-tRNA synthetase, is a good inhibitor of pathogens as a reference molecule (Francklyn and Mullen, 2019). Molecular docking results are shown in Table 1 and Figure 6 . From Table 1 , it can be found that the binding energies of A with tyrosyl-tRNA synthetase of Alternaria alternata and Ralstonia solanacearum are −15.469 and −14.748 kcal/mol, respectively, which are better than those of reference molecules mupirocin’s −9.540 and −8.186 kcal/mol, respectively. As shown in Figure 6 , soyasaponin Bb can interact with tyrosyl-tRNA synthetase of Alternaria alternata residues ASP-100, THR-102, HIS-110, ARG-144, TYR-244, ASP-269, GLY-267, and ARG-144 form nine hydrogen bonds. It forms three hydrophobic interactions with ASP-100 and LYS-322. Moreover, soyasaponin Bb can form six hydrogen bonds with residues ARG-87, ARG-294, and ARG-326 and form four hydrophobic interactions with residues PHE-293, ARG-294, and ALA-325 in tyrosyl-tRNA synthetase of Ralstonia solanacearum ( Table 1 and Figure 6 ). It indicates that soyasaponin Bb may have a good inhibitory effect on Ralstonia solanacearum and Alternaria alternata and upregulation of soyasaponin Bb may be a chemical reason for the improvement of disease resistance in the mutant tobacco. Of course, other upregulated metabolites have high binding energy with tyrosyl-tRNA synthetase (<−5 kcal/mol), which may also enhance the tobacco resistance ( Table 1 ). It may also be that the synergism of upregulated metabolites improves the disease resistance of tobacco.

To identify potential members of the CCoAOMT gene family in Nicotiana tabacum, we performed NCBI database searches. Seven Arabidopsis CCoAOMT sequences were used as queries in BLAST searches against the Nicotiana tabacum genome sequence (https://www.ncbi.nlm.nih.gov/genome/?term=Nicotiana+tabacum). The exons and introns were analyzed and calculated by IMEter v2.0 (Wu et al., 2023). SMART toolkit (http://smart.embl-heidelberg.de/) was used for protein domain analysis.

Full-length protein sequences of 14 tobacco CCoAOMTs and 7 Arabidopsis CCoAOMTs were used for building the phylogenetic tree. Multiple-sequence alignments were performed using Muscle 3.52. A neighbor-joining (NJ) tree is reconstructed using the MEGA program version 7.0. Support for each node was tested with 1,000 bootstrap replicates.

The total RNA of the wild type and the double mutant was isolated respectively from finely ground tissue samples of leaves and roots of tobacco growing in the open field in Kunming (2021), China. We used the BGI-NGS-TQ-RNA-005 A0 Kit (BGI) for the extractions. The purity and integrity of the RNA were evaluated through gel electrophoresis and the ratios of A₂₆₀/A₂₃₀ and A₂₆₀/A₂₈₀. The qualified RNA is used for subsequent reverse transcription and transcriptome library construction. We used the TransScript II First-Strand cDNA Synthesis Kit (TransGen) for cDNA reverse transcription.

To analyze gene expression levels, a qRT-PCR assay was completed with SYBR Green (Takara) and a 7500 Real-Time PCR system (Life Technologies).The reaction mixture was 20 μL, including SYBR Premix Ex Taq 10 μL, 10 μM reverse-specific primer 0.4 μL, and 0.1 μM cDNA templates 2 μL. Actin was used as a reference gene. The 2^−△△CT method was used to analyze the relative gene expression (Livak and Schmittgen, 2001). The experiment was repeated three times, and the primers are listed in Table S3 .

The binary vector pORE-Cas9 was used as backbone like that previously described (Gao et al., 2015; Gao et al., 2018), which contains the CRISPR/Cas9 components. The 25-bp oligos (F: GATTGTTGCCCGTAATCCAAAAGGC; R: AAACGCCTTTTGGATTACGGGCAAC) with GAAT and AAAC overhands, respectively, were annealed and then inserted into the BsaI site of the pORE-Cas9 binary vector by T7 ligase.

The pORE-Cas9 binary vectors containing the specific guide RNA and Cas9 expression cassettes were transformed into Agrobacterium tumefaciens LBA4404 by the freeze–thaw method. The positive clones were then used to produce target gene mutant tobacco plants with the leaf discs method (Horsch et al., 1985). Kanamycin-resistant seedlings were obtained, and mutants were identified.

The total lignin contents of dried tobacco leave and root tissues were quantitatively determined by using the Klason method (Eudes et al., 2006; Ma et al., 2017). Lignin monomer composition was measured using thioacidolysis (Zhao et al., 2010; Ma et al., 2017).

Determination methods of total sugar and reducing sugar followed protocols of Tobacco Trade Standards (China National Tobacco Quality Supervision&TestCenter, 2002).

Extracted RNA was assessed for quality and quantity using an Agilent 2100 Bioanalyzer (Agilent Technologies), and mRNA library preparation refers to the construction process BGI-NGS-JK-RNA-001 (https://www.yuque.com/yangyulan-ayaeq/oupzan/gu8ls7). The amplified libraries were sequenced on a DNBSEQ sequencing machine at BGI company according to Qi et al (Qin et al., 2020). The sequencing data were filtered with SOAPnuke (Li et al., 2008). Bowtie2 (Langmead and Salzberg, 2012) was applied to align the clean reads to the gene set. The gene expression level was calculated by RSEM (v1.3.1) (Li and Dewey, 2011). Differential expression analysis was performed using the DESeq2 (v1.4.5) (Love et al., 2014) with log₂FC ≥1 and Q value ≤0.05. All DEGs were annotated according to GO, KEGG, KOG, Swiss-Prot, and TrEMBL databases. There are six replicates in the leaf and root groups between WT and the mutant.

Resistance against pathogens test. The leaves were the fourth leaf from the top, taken from the 2-month planted WT tobacco and double-mutant tobacco. The wound sites were scratched by the toothpick back of the leaf, evenly distributed on both sides of the main leaf vein. The challenged leaves were put into a 150-mm culture dish with water-soaked filter paper at the bottom and incubated at 28°C for 72 h, then the photoperiod was set as 16-h light and 8-h darkness.

Tobacco leaf extract inhibition test. 100 mg of MU and WT tobacco leaf-lyophilized powder was ultrasonically extracted for 40 min by 10 mL of 95% ethanol and filtered, and MU and WT extracts were obtained after filtration. The 6-mm-diameter filter paper was put into the MU extract, WT extract, 95% ethanol, and 1 mg/mL cefotaxime sodium ethanol solutions and soaked for 10 min. 100 μL of Alternaria alternata and Ralstonia solanacearum bacterial fluids was taken and coated in PDA and LB plate culture medium, respectively, then kept in a 28°C incubator for 24 h.

20.0 mg (accurate to 0.1 mg) sample powder was weighed, and 4 mL of methanol–water (v:v = 8:2) solution was added, shaken on a vortex mixer at 2,000 r/min for 30 s, sonicated in an ice-water bath for 30 min, centrifuged at 13,000 r/min for 15 min at 4°C, and passed through a 0.22-μm organic filter membrane, ready for analysis.

High-performance liquid chromatography (LC-20A, Shimadzu) tandem time-of-flight mass spectrometry (AB SCIEX 5600+, Shimadzu), and XSelect HSS T3 (4.6 × 150 mm, 3.5 µm) column chromatography were performed for the metabolomic analysis. The positive ionization mode mobile phase was 0.1% formic acid/water (A) and acetonitrile (B), and the negative ionization mode mobile phase was 5 mM ammonium formate (A) and acetonitrile (B). The mobile phase gradient elution program was: 0.00 min–3.00 min, 10%B; 3.00 min–21.00 min, 10%–95%B; 21.00 min–28.00 min, 95%B; 28.00 min–28.10 min, 10%B; 28.10 min–34.00 min, 10%B. The QTOF/MS scanning mode is an information-dependent (IDA) full-scan mode, the mass scanning range is m/z 50–1,000, the source voltages of the positive and negative ionization modes are 5,500 V and 4,500 V, respectively, and the curtain gas, atomizer (gas1), and heating gas (gas2) flow pressures are 25 psi, 50 psi, and 50 psi, respectively. The collision energies (CE) were 30 V and −30 V, respectively. Qualitative analysis of metabolites was based on the local database (Jiangsu Academy of Agricultural Sciences) and the public database of metabolite information, including NIST, METLIN (https://metlin.scripps.edu), and HMDB (https://hmdb.ca/). Metabolites with significant differences were set with thresholds of variable importance in projection (VIP) ≥1 and fold change ≥1 or ≤0.5. Eight biological replicates of each sample were performed in each group.

In this study, AutoDock Vina (Vina, version 1.1.2) is running with a semi-flexible docking method, with docking accuracy up to 78%. PyMOL (version 4.3.0) software (https://pymol.org/) was used for separation of original ligand and protein structure, dehydration, and removal of organic matter. AutoDockTools (http://mgltools.scripps.edu/downloads) is used for hydrogenation, checking the charge, designating the atom type as AD4 type, calculating the Gasteiger, and constructing the docking grid box of the protein structure. In addition, the chemical composition (small-molecule ligand) should be determined as the root, and the reversible bond of the ligand should be selected in AutoDockTools.