In this study, 10-year-old WPF and WPFI P. fortunei plants growing in the experimental field of Henan Agricultural University were used as experimental materials. Buds were collected from samples, with three biological replicates per sample. The collected buds were immediately frozen in liquid nitrogen and then stored at −80°C prior to subsequent analyses.

The phytoplasma in WPF and WPFI samples were determined following the method described by Yang et al. (2023) [7].

RNA sequencing (RNA-seq) data for the PfGRF genes in the WPF and WPFI plants were downloaded from the NCBI Sequence Read Archive (SRA) database (SRA accession numbers: SRR11787883, SRR11787894, SRR11787905, and SRR11787912–SRR1178792). A heatmap of PfGRF expression was generated using TBtools [26].

Methodological protocols for protein interaction validation via Y2H and BiFC, as described in Yang et al. (2023) [7]. All primers used are listed in Table S1.

The results showed that phytoplasmas were detected in WPFI by quantitative real-time PCR, while no phytoplasmas were found in WPF (Fig. S1).

A total of 764,557,918 clean reads were obtained from the RNA-seq analysis of the following six cDNA libraries: 97.90% (WPF-1), 97.77% (WPF-2), 97.78% (WPF-3), 98.01% (WPFI-1), 98.08% (WPFI-2), and 98.20% (WPFI-3) (Table S2). The size distribution of mapped reads for the identified mRNA sequences is presented in Fig. S2. More than 70% of the mapped mRNAs were 0–1500 nt long (Fig. S2a), with 1 being the most common number of exons (Fig. S2).

To determine the transcriptional changes in WPF and WPFI, we identified DEGs through comparisons (7036 and 4897 genes with increased and decreased expression levels, respectively, in response to PaWB) (Fig. 1A). The main enriched KEGG pathways among these genes were biosynthesis of secondary metabolites, plant hormone signal transduction, plant–pathogen interaction, and starch and sucrose metabolism. Hence, the genes contributing to these metabolic processes are likely closely related to the occurrence of PaWB (Fig. 1B).

A total of 161,080,220 clean reads were obtained for six miRNA libraries (Table S3). Clean and unique miRNA reads were mapped to the P. fortunei genome sequence. The number of miRNAs in the WPF and WPFI libraries was very similar, indicating that the phytoplasma infection had little effect on the classification of miRNAs in P. fortunei. The results of the correlation analysis of the six samples (Fig. S3a) reflected the high repeatability of the miRNA sequencing results, making them suitable for the downstream analysis.

According to the examination of the miRNAs in WPF and WPFI, 88 differentially expressed miRNAs were identified (38 up-regulated and 50 down-regulated) (Fig. 1C). On the basis of a KEGG analysis, the target genes were assigned to 34 pathways (Table S4), among which plant hormone signal transduction (ko04075) was the most common, followed by plant–pathogen interaction (ko04626), circadian rhythm–plant (ko04712), and ubiquitin mediated proteolysis (ko04120) (Fig. S3b).

A miRNA–mRNA regulatory network consisting of 254 miRNAs and 1517 mRNAs was established. The regulatory relationships of four miRNAs and eight mRNAs related to the infection of P. fortunei by phytoplasma are presented in Fig. 1D. The results of an association analysis showed that PfmiR396 was involved in the P. fortunei response to phytoplasma and affected PfGRF expression. The PfGRF gene family, which consists of plant-specific TF genes, has been found in A. thaliana, rice, and soybean. Notably, it influences plant resistance as well as the regulation of plant morphology, growth, and development [21,22,24]. Therefore, the PfGRF gene family was selected for subsequent analysis, which aimed to provide fundamental insights for the genetic improvement of Paulownia species and the breeding of new varieties with enhanced stress resistance.

16 PfGRF family members were identified and designated as PfGRF1 to PfGRF16 based on their chromosomal locations in P. fortunei (Fig. 2A,B, Table 1). All of these genes contained a QLQ domain and a WRC domain (Fig. 2A). The number of amino acids encoded by these PfGRF genes ranged from 148 to 620, with molecular weights ranging from 16.9 to 67.2 kDa. Their pI values ranged from 6.11 to 10.14. Among the proteins encoded by these 16 PfGRF genes, 13 were basic proteins (pI > 7), whereas three were acidic proteins (pI < 7). The subcellular localization results indicated that the PfGRF proteins were localized mainly in chloroplasts (Table 1).

Chromosomal maps were constructed to visualize the locations of PfGRF genes on each chromosome. Sixteen PfGRF genes were mapped to 10 chromosomes (Fig. 2B), while the remaining three PfGRF genes were mapped to unassembled scaffolds. The PfGRF genes were unevenly distributed among the chromosomes, with eight on each of chromosomes 7 and 18, two on each of chromosomes 2, 9, and 16, and only one on each of chromosomes 4, 15, and 19. There were some apparent regional enrichment in the distribution of PfGRF genes on chromosomes. For example, eight PfGRF genes were located at the end of chromosome 7 and the beginning of chromosome 18.

To examine PfGRF transcriptional regulation, the cis-acting elements in PfGRF promoters were identified. The PfGRF promoter regions were revealed to contain light-responsive, phytohormone-responsive, and stress-responsive cis-acting elements as well as cis-acting elements involved in the regulation of plant growth processes (Fig. 2C). Specifically, a lot of the PfGRF promoters contained methyl jasmonate or salicylic acid-responsive cis-acting elements, which may be involved in biotic stress responses. These findings suggest that PfGRF genes may participate in the response to phytoplasma infection leading to PaWB.

A phylogenetic tree was constructed for the GRF genes in P. fortunei, A. thaliana, and O. sativa. The genes were clustered into six groups (Fig. 3A). The duplicated gene pairs PfGRF5/PfGRF11 were clustered in group I and were evolutionarily closest to AtGRF1 and AtGRF2, implying that their functions may be similar to those of AtGRF1 and AtGRF2. In addition, PfGRF1 was clustered with AtGRF5, whereas PfGRF3 was clustered with AtGRF6, suggesting that they may have similar functions. The duplicated gene pair PfGRF1/PfGRF3 was clustered with AtGRF6 in group II, implying its functions may be similar to those of AtGRF6. Both PfGRF8 and PfGRF12 were clustered with OsGRF3, OsGRF4, and OsGRF5 in group III. Group IV contained PfGRF6, PfGRF9, and PfGRF13, which were distantly related to the GRF genes in O. sativa.

Collinearity analyses of P. fortunei, A. thaliana, and O. sativa were performed. Fifteen duplicated gene pairs involving 10 PfGRF genes and A. thaliana GRF genes were detected (Fig. 3C) as well as 9 duplicated gene pairs involving 7 PfGRF genes and O. sativa GRF genes (Fig. 3B). The results of collinearity analyses indicated that PfGRF genes were evolutionarily closer to AtGRF genes than to OsGRF genes.

To further characterize the PfGRF gene family, a conserved motif analysis was performed, which identified 10 distinct motifs in the 16 PfGRF genes, among which motifs 1 and 2 were present in all family members (Fig. 4). Motifs 4–7 were specifically detected in PfGRF1/2/3/4/8/14/15/16. PfGRF genes in the same branch of the phylogenetic tree had a similar motif composition. In contrast, PfGRF genes in different branches differed regarding their motifs. This suggests that the differences in the conserved motifs may be a key factor associated with the functional diversity among PfGRFs.

The gene structure analysis indicated that 12 of the 16 PfGRF genes lacked untranslated regions (Fig. 4A). Introns can affect gene stability, with genes containing many introns forming variable spliceosomes during transcription. The number of introns in the PfGRF genes ranged from 0 to 5 (Fig. 4A). More specifically, PfGRF2, PfGRF3, and PfGRF16 had no introns, whereas PfGRF11 had five introns, suggesting it may be unstable during transcription. Both PfGRF2 and PfGRF16 lacked untranslated regions, but had a number of introns, indicating these genes may also be unstable during transcription.

To further explore the roles of PfGRF genes in abiotic stress responses, expression levels of PfGRFs under salt and drought treatments were analyzed using transcriptome data (Fig. 5A,B). After the salt treatment, PfGRF1/6/7/8/9/10/13 expression levels were up-regulated (compared with the corresponding control level), suggesting that these genes are responsive to salinity stress.

Following the drought treatment, the PfGRF1/4/5/6/8/9/10 expression level were down-regulated, which were in contrast to the significantly up-regulated expression of PfGRF11/12/13/15, indicating that these genes respond differentially to drought. A transcriptome sequencing analysis of samples exposed to salt and drought conditions detected differences in PfGRF expression levels, suggesting that PfGRF TFs may have different functions during responses to drought and salinity.

Considering the diversity and similarity of the PfGRF genes revealed by the analyses of gene structures and evolutionary relationships, the transcriptome data were used to examine PfGRF expression levels in P. fortunei infected with phytoplasma. Differentially expressed PfGRF genes in response to PaWB were identified by analyzing RNA-seq data from WPF and WPFI plants. The results showed that the expression of all 16 PfGRF genes was affected by the phytoplasma presence (Fig. 5C). PfGRF1, PfGRF3, PfGRF4, PfGRF5, PfGRF6, PfGRF11, and PfGRF16 expression levels were significantly up-regulated, which was in contrast to the significantly down-regulated expression of PfGRF2, PfGRF7, PfGRF8, PfGRF9, PfGRF10, PfGRF12, PfGRF13, PfGRF14, and PfGRF15. Notably, PfGRF14 expression levels were down-regulated by 4.93 times, respectively, in response to the phytoplasma infection.

According to the expression profile analysis, PfGRF1/3/4/5/6/11/16 act as positive regulators of PaWB, whereas PfGRF2/7/8/9/10/12/13/14/15 function as negative regulators.

Previous studies have shown that GRFs usually interact with GIFs to participate in regulating the size of the blades [28,29]. PfGIFa (Pfo04g014660) was confirmed to interact with PfGRF14 through both Yeast two hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays (Fig. 6). It is speculated that PfGRF14 may contribute to the development of the small-leaf symptom during PaWB infection.