Root development is a key factor for determining the growth and stress resistance of plants, especially fruit trees. To demonstrate the effect of KAR1 on apple root development, Gala apple tissue culture−generated plants were treated with different KAR1 concentrations (0, 1 nM, 5 nM, 50 nM and 500 nM) and rooted in ½ MS (Murashige and Skoog) media supplemented with 0.1 mg·L^-1 IAA. KAR1 significantly promoted the development of taproots and lateral roots compared with those in the control group ( Figures 1A–C ).

Plant hormones play crucial roles in regulating root development. Furthermore, the contents of abscisic acid (ABA), gibberellic acid 3 (GA3), and trans−zeatin−riboside (TZR) plant hormones in the roots of apple seedlings subcultured for 4 days in MS media with KAR1 concentrations of 0, 1 nM, 5 nM, 50 nM, and 500 nM were determined. These results indicated that KAR1 significantly influences the endogenous hormone levels of apple roots ( Figures 1D–F ). The ABA content increased in the treatments with 5 nM, 50 nM, and 500 nM KAR1. The GA3 content decreased in the treatment range of 1 to 500 nM KAR1. The TZR content significantly increased in the 50 nM KAR1 treatment group but decreased in the 500 nM KAR1 treatment group. Treatment with 50 nM KAR1 had the most marked effect on roots, significantly increasing the ABA and TZR contents and reducing the GA3 content in apple roots, which indicated that KAR1−induced apple root development may be mediated by increasing ABA and TZR contents and decreasing the GA3 content.

Biological function is determined by molecular structure. Like that in A. thaliana, the natural receptor of KAR1 in apple, MdKAI2, is also conserved. Bioinformatics prediction revealed that MdKAI2 was a less stable weakly hydrophobic peripheral membrane protein ( Supplementary Figures S1A, C, D ) and had a greater content of alpha helices and random coils ( Supplementary Figure S1B ), which was related to its biological localization and function. In addition, a total of 9 sequence−conserved motifs (E value<0.05) were predicted among the 6 orthologous KAI2s from different Rosaceae plants ( Supplementary Figures S1E, F ). Several cis−acting elements related to plant hormone signals and stress resistance, including ABREs, EREs, and AREs, were also predicted in the promoter regions of the MdKAI2 genes. In particular, multiple transcription factors (TFs) related to stress resistance, including MYB and MYC, are predicted to specifically bind to the promoter cis−elements of MdKAI2 genes. Many basic cis−elements (TATA box and CAAT box) have also been predicted, which suggests that MdKAI2 may be involved in the regulation of resistance and maintenance of high transcription levels when needed ( Supplementary Figure S3 ).

The amino acid sequences of A. thaliana AtKAI2 and 129 KAI2s that are homologous to Malus domestica MdKAI2 were retrieved from the GenBank database and used to construct the phylogenetic tree. According to the phylogenetic relationships shown ( Figure 2 ), the 131 KAI2 proteins can be divided into 6 groups and belong to 66 families. The genetic distance among members of the same genus is relatively short, and the genetic relationship is relatively close. Eight KAI2s belonging to the Vitis, Rhamnos, Moraceae, Cannabis, Fabaceae and Rosaceae taxa were clustered into one group. This extensive phylogenetic conservation among species implies the importance of the biological function of KAI2.

To investigate the biological function of resistance mediated by MdKAI2, Gala apple tissue culture−generated seedlings and Orin apple calli were used for genetic transformation. Two binary vectors, pBI121−CaMV 35S::MdKAI2−GFP and pBI121−CaMV 35S::GFP, were used for Agrobacterium tumefaciens−mediated apple transfection, and the positive lines were the OE and CT lines, respectively. Western blot ( Figure 3A ) and quantitative real−time PCR (qRT−PCR) ( Supplementary Figure S2 ) assays were conducted to identify the positive transgenic callus lines.

To simulate osmotic stress, 20% polyethylene glycol (PEG) was used. In response to PEG stress, the endogenous expression level of MdKAI2 significantly increased in wild−type apple calli ( Figure 3B ), which indicated that MdKAI2 was markedly induced by external osmotic stress. The water content of the calli did not significantly differ with or without MdKAI2 transfection but significantly decreased after treatment with 20% PEG ( Figure 3C ). Although the total flavonoid contents of calli were reduced in the presence of PEG stress, the total flavonoid contents in the MdKAI2−overexpressing lines were significantly greater than those in the CT lines under PEG stress or nonstress conditions ( Figure 3D ). Compared with that of the CT lines, the biomass of the MdKAI2−positive calli significantly increased without PEG treatment but significantly decreased under PEG stress ( Figure 3E ). These results were also reflected in the phenotypic traits ( Figure 3A ). Notably, some white cell death was observed on the surface of the CT lines under PEG stress, whereas white cell death was not observed in the OE lines. Our results indicated that MdKAI2 promoted the growth of apple calli under normal environmental conditions but inhibited their growth under stress conditions and increased the total flavonoid content to increase survival under relatively extreme stress.

To further investigate the physicochemical mechanisms by which MdKAI2 is involved in the regulation of plant resistance, several main indicators were investigated ( Figure 4 ). The activity of reactive oxygen species (ROS)−scavenging enzymes, including SOD, POD, and CAT, increased after treatment with 20% PEG and further significantly increased in the MdAKI2 transgenic callus lines, which indicated that the ROS balance mechanism was significantly promoted by MdKAI2 in apple cells. The level of malondialdehyde (MDA) and electrical conductivity are crucial indicators for evaluating the degree of oxidative damage in cells. The MDA content did not significantly differ among the CT lines and OE lines without PEG stress, whereas it significantly decreased in the OE lines under PEG stress. Although the electrical conductivity significantly decreased under PEG stress in the CT lines, the electrical conductivity markedly decreased in the OE lines.

Osmoregulatory substances are also markedly regulated by MdKAI2. Under PEG stress, the total soluble protein content significantly increased. Moreover, the contents of these genes were further elevated in the OE lines, and the contents in the OE lines reached the highest level. Under normal conditions, no significant differences in total soluble sugar content were detected between the CT and OE lines. The total soluble sugar contents of the CT lines significantly decreased in response to PEG, whereas there were no significant differences in the total soluble sugar contents of the OE lines. Moreover, under PEG stress, the total soluble sugar content in the OE lines was significantly greater than that in the CT lines. The proline content significantly decreased under PEG stress in the CT lines. In contrast, it significantly increased in the OE lines under PEG stress and reached an approximately equivalent level to that of the CT lines without PEG treatment. Although the content of glycine betaine significantly increased under PEG stress, no significant difference was detected between the CT and OE lines with or without PEG stress. These results demonstrated that MdKAI2 could promote the osmotic stress resistance of apple plants by increasing their ability to scavenge reactive oxygen species and facilitating the accumulation of osmoregulatory substances.

To analyze the transcriptional regulatory mechanism, Orin apple calli transformed with MdKAI2−GFP were subjected to RNA sequencing (RNA−Seq), and the CT group was used as the control. Three independent biological replicates were conducted. The average percentages of clean reads in the CT and OE groups were 93.85% and 93.74%, respectively, which met the analysis requirements.

PCA (principal component analysis) revealed that the intergroup difference was significant ( Figure 5A ), and the intragroup correlations were greater than the intergroup correlations ( Figure 5B ), which implied that the significant difference in the transcriptional level was due mainly to MdKAI2 transformation. A total of 34132 transcripts were detected, and 344 transcripts and 264 transcripts were up− and downregulated, respectively (log₂ (fold change)≥1 or ≤-1, P value<0.05; Figure 5C ). A total of 608 differentially expressed genes (DEGs) were divided into three clusters on the basis of their expression abundance ( Figure 5D ). Cluster 1 represented the downregulated DEGs, whereas cluster 2 and cluster 3 represented the upregulated DEGs. The DEGs in Cluster 2 were expressed slightly more highly in the CT lines than those in Cluster 3 were.

In addition, a total of 51 TFs belonging to 20 types of TFs were screened according to a log₂ (fold change) ≥ 2 or ≤ -2 and a P value < 0.05. These TFs were significantly up− or downregulated by MdKAI2 overexpression ( Figure 5E ). The numbers of genes belonging to the MYB, bHLH, AP2−EREBP, and NAC families were relatively large ( Supplementary Table S3 ). Moreover, the expression levels of representative TFs, including MdWRKY, MdbHLH, MdAP2−EREBP, MdNAC and MdMYB, which are closely associated with plant resistance, were verified via qRT−PCR ( Supplementary Figure S4 ). These results were essentially in accordance with the RNA−Seq results and demonstrated that MdKAI2 significantly regulates the expression of multiple crucial TFs. Interaction network prediction of differentially expressed TFs was conducted ( Figure 5F ), and bHLH, MADS, MYB, WRKY, C2C2−GATA, C2H2 and LOB TFs exhibited strong interaction relationships.

According to the cluster analysis based on the expression levels of DEGs ( Figure 5C ), the two clusters of upregulated DEGs and one cluster of downregulated DEGs induced by MdKAI2 overexpression were analyzed via Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and classification analysis. The DEGs were involved mainly in the pathways of the global and overview maps; environmental adaptation; signal transduction; and carbohydrate, lipid, amino acid terpenoid, polyketide and other secondary metabolism ( Figure 6A ).

The downregulated DEGs (cluster 1) were associated mainly with brassinosteroid (BR) biosynthesis, ABC transporter, RNA polymerase, porphyrin metabolism, and the MAPK signaling pathway. These genes are primarily responsible for regulating plant growth and development, promoting reproductive growth, modulating stomatal activity to achieve equilibrium water potential, enhancing light energy performance, inducing the synthesis and accumulation of stress−resistant substances, and strengthening the capacity to defend against both biotic and abiotic stresses.

The upregulated DEGs of cluster 2 were associated mainly with pathways such as the MAPK signaling pathway; plant−pathogen interaction; cutin, suberin and wax biosynthesis; DNA replication; fatty acid degradation; and plant hormone signal transduction ( Figure 6B ). These genes are involved mainly in regulating the plant signal response and transduction, reducing transpirational water loss, and enhancing pathogen defense ability and lipid metabolism. The upregulated DEGs of cluster 3 are related mainly to pathways including alpha−linolenic acid metabolism, phenylpropanoid biosynthesis, DNA replication, cyanoamino acid metabolism, and pentose and glucuronate interconversions ( Figure 6C ).

The processes related to these DEGs involve mainly lipid and amino acid metabolism and predominantly enhance the fluidity and stability of membrane lipids, govern the construction of cell walls and stress defense mechanisms, facilitate the synthesis and transduction of stress signals, and strengthen the defense against natural enemies and other multiple resistance mechanisms; these pathways may be involved in multiple signal transduction processes related to MAPK.

The plant metabolites are closely related to the plant defensive response. Orin apple calli transformed with MdKAI2−GFP were subjected to metabolome analysis, and the CT group was used as the control group. As shown by the PLS−DA (partial least squares discriminant analysis) results, metabolites with different abundances were more significantly different between the CT and OE groups, and the analysis model did not overfit the data ( Figures 7A, B ). A total of 2709 metabolites with different abundances were detected, and 135 metabolites and 189 metabolites were up− and downregulated, respectively (log₂ (fold change) ≥1 or ≤ -1, P value < 0.05, Figure 7C ). The expression levels of 2709 metabolites markedly differed in response to MdKAI2 overexpression, and the metabolites could be divided into three groups on the basis of their differences in abundance ( Figure 7D ). Among the 324 differentially expressed metabolites (DEMs), 37 ( Supplementary Table S1 ) were annotated in the KEGG database and were used for KEGG enrichment analysis ( Figures 7E, F ). The 37 annotated DEMs were mainly related to amino acids or peptides or their analogs, benzene and derivatives, organic acids, carbohydrates, terpenoids, alkaloids and lipids and are involved in lipid, nucleotide, secondary metabolite, carbohydrate, and amino acid metabolism processes, which were basically consistent with those of the KEGG analyses of RNA−Seq. The above results indicated that metabolites and metabolic processes, which are related primarily to amino acids, peptides and their derivatives, nucleic acids, ester compounds, and carbohydrates, were markedly affected by MdKAI2 overexpression.

According to the results of the KEGG analyses of the DEGs ( Figure 6 ), the MAPK signaling pathway may involve multiple resistance pathways induced by MdKAI2 overexpression. The cross−talk between calcium−dependent protein kinases (CDPKs) and MAPK is important for regulating plant−specific responses to biotic and abiotic stresses (Ludwig et al., 2005). To further investigate whether some associations exist between CDPK−MAPK signaling pathways and these DEMs, MdKAI2 and 41 differentially expressed MdMAPK signaling−related DEGs combined with 37 annotated DEMs were utilized for correlation analysis. In response to MdKAI2 overexpression, the expression levels of two MdCDPKs were upregulated 4.8− and 8.8−fold, that of ten MAPK signaling−related DEGs was significantly downregulated, that of eight MAPK signaling−related DEGs was significantly upregulated, and the expression levels of representative DEGs related to MAPK signaling were verified via qRT−PCR ( Supplementary Figure S4 ).

The results of the correlation analysis ( Figure 8 ) revealed that 37 DEMs were significantly correlated with the MAPK signaling−related DEGs. These genes were involved in the MAPK signaling pathway, plant hormone signal transduction, and plant−pathogen interactions ( Figure 9A ), and the DEMs were involved mainly in multiple secondary metabolic processes, such as amino acid metabolism ( Figure 9B ).

Specifically, induced by MdKAI2 overexpression, DEMs labeled as group M1 (mainly fatty acids, esters, and terpenoids) were significantly upregulated ( Figure 8 ) and involved metabolic pathways such as glycine, serine, and threonine metabolism ( Figure 9B ), and other DEMs labeled as group M2 (mainly acid derivatives, amides and alcohols) were downregulated ( Figure 8 ) and mainly participated in metabolic pathways such as lysine degradation; tropane, piperidine, and pyridine alkaloid biosynthesis; and the biosynthesis of secondary metabolites ( Figure 9B ). The DEGs associated with the MAPK signaling pathways can be classified into three categories (labeled as groups G1, G2, and G3).

The downregulated DEGs of group G1, which included multiple protein kinases, phosphatases, and TFs, including MYC, WRKY, and bHLH, were significantly positively correlated with those of group M2 DEMs and significantly negatively correlated with those of group M1 DEMs. These genes may positively regulate metabolites of amino acid derivatives, amides and alcohols by being involved in lysine degradation and the biosynthesis of tropane, piperidine, pyridine alkaloids and other secondary metabolites, while these processes are negatively regulated by MdKAI2 overexpression ( Figures 8 , 9B ; Supplementary Table S2 ). The upregulated DEGs of group G3, which included multiple protein kinases, including CDPK, and TFs, including MYC, bHLH and ERF, were significantly positively correlated with those of group M1 DEMs and significantly negatively correlated with those of group M2 DEMs. These genes may positively regulate metabolites of fatty acids, esters, and terpenoids via glycine, serine and threonine metabolism, and these processes are positively regulated by MdKAI2 overexpression ( Figures 8 , 9B ; Supplementary Table S2 ). In addition, the upregulated DEGs of group G2, including genes encoding calcium−binding proteins and protein kinases, may be involved in the positive regulation of some fatty acids, esters, and terpenoids and were not significantly correlated with DEMs, including amino acid derivatives, amides and alcohols ( Figure 8 ; Supplementary Table S2 ).

Gala tissue culture seedlings (Hu et al., 2020) were subcultured in MS media (0.5 mg·L^-1 6−BA, 0.2 mg·L^-1 IAA) at 24°C with a photoperiod of 16 hours of light and 8 hours of darkness. A rooting−inducing assay was conducted in ½ MS media supplemented with 0.1 mg·L^-1 IAA or KAR1 (CAS: 857054−02−5) at different concentrations. The culture conditions were the same as those for the subcultures described above, and photographs were taken after treatment for 58 days. The contents of ABA, GA3 and TZR plant hormones in apple seedling roots subcultured for 4 days in MS media with KAR1 concentrations of 0, 1 nM, 5 nM, 50 nM, and 500 nM were determined via the LC−MS method (Hashiguchi et al., 2021).

Physical and chemical property analyses were conducted via the ProtParam online program (https://web.ExPASy.org/protparam/). The protein secondary structure of MdKAI2 was determined via the SOPMA online program (https://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html). Protein transmembrane structure analysis was conducted via the TMHMM online program (https://services.healthtech.dtu.dk/services/TMHMM-2.0/). Protein hydrophilicity analysis was conducted via the ProtScale online program (https://web.ExPASy.org/protscale/). Protein motif analysis was conducted via the MEME online program (https://meme-suite.org/meme/tools/meme). The promoter region of the 2000 nucleotide sequence was retrieved upstream of the ATG translation start codon of the MdKAI2 gene.

The AtKAI2 coding sequence in A. thaliana was acquired from the TAIR (The Arabidopsis Information Resource) database with the gene ID AT4G37470.1, and the MdKAI2 coding sequence in M. domestica was acquired from the GDR (Genome Database for Rosaceae) database with the gene ID MD02G1181300. A total of 129 amino acid−coding sequences of orthologous genes highly consistent with those of MdKAI2 were aligned via GenBank, and one representative coding sequence per species was retrieved, together with the amino acid−coding sequences of AtKAI2 and MdKAI2, for analysis of the evolutionary relationship. A phylogenetic tree was constructed via MEGA 7 software, the neighbor−joining method was used to evaluate the evolutionary relationships, and the evolutionary distances were calculated via Poisson correction.

The primers used for amplification of the open reading frame (ORF) of MdKAI2 are shown in Supplementary Table S4 . The nucleotide fragment of the coding sequence was inserted into the pBI121 vector for the construction of a binary recombinant pBI121−CaMV 35S::MdKAI2−GFP overexpression vector, which was subsequently transfected into Orin apple calli (Wang et al., 2021) generated via Agrobacterium. An empty vector without the coding sequence (pBI121−CaMV 35S::GFP) was also constructed and used as a control. The positive transgenic calli were screened on MS media supplemented with 15 g·L^-1 agar, 1.5 mg·L^-1 2,4−D, 0.5 mg·L^-1 6−BA and 50 mg·L^-1 kanamycin A and were identified via both qRT−PCR and western blotting. Transgenic Orin calli were cultured in the dark at 24°C and subcultured every 15 days.

Orin transgenic calli were inoculated on PEG−infused plates after subculturing for 10 days. The PEG−infused plant system was constructed according to the method of Verslues et al. (2006). Because PEG prevents polymerization of the agar, the PEG−containing overlay solution was added on top of the solidified MS medium. PEG6000 was dissolved in liquid ½ MS media supplemented with 2 mM 2−morpholinoethanesulfonic acid (MES) without agar at a 20% final mass ratio of PEG. Except for MES, which was sterilized by filtration, the PEG overlay solution was autoclaved before use. The solidified MS media were prepared with 15 g·L^-1 agar, 1.5 mg·L^-1 2,4−D and 0.5 mg·L^-1 6−BA, and the pH was adjusted to 6.0. The remaining PEG overlay solution was removed after infusion and infiltration into solidified MS media for 12 hours.

The water content of the callus was calculated as the ratio of the water mass to the fresh weight. The biomass of the callus was measured by weighing the fresh weight after culture for 10 days. SOD activity was measured via the nitrogen blue tetrazole (NBT) photochemical reduction method according to the method described by Chang et al. (2022). POD activity was measured via the guaiacol oxidation method (Wang et al., 2019). CAT activity was measured via the ammonium molybdate method (Chang et al., 2022). The MDA, total soluble sugar and proline levels in the calli were measured according to the methods described by Chang et al. (2022). The total soluble protein content was analyzed via a Coomassie brilliant blue protein analysis kit (Jining, China). The relative electrical conductivity was measured according to the method described by Li et al. (2019). The glycine betaine content was measured according to the method described by Gupta et al. (2014).

Orin transgenic calli of CaMV 35S::MdKAI2−GFP (OE) were selected for RNA−Seq analysis, and transgenic calli of CaMV 35S::GFP (CT) were used for the control group. Three independent callus lines from each group were used for the repeats. mRNA extraction, cDNA library construction, next−generation sequencing and annotation of the transcripts were carried out by the BGI Company (China). The reference annotation information of the transcripts was derived from the genome data of M. domestica (Daccord et al., 2017) published in the GDR (https://www.rosaceae.org/) database. Metabolomic analysis of the abovementioned transgenic Orin calli was carried out via high−performance liquid chromatography−mass spectrometry (HPLC−MS) by the BGI Company (China).

The root projection area was calculated via ImageJ software (version 1.52 V), and the total root length was calculated via WinRHIZO software (version 2013). The evolutionary relationships among orthologous proteins of MdKAI2 were analyzed and plotted via Mega 7 software (version 7.0.26). The 2^-ΔΔCT method was used to analyze the qRT−PCR data, and the primers used are shown in Supplementary Table S4 . PCA and PLS−DA were performed via Simca software (version 14.1). Correlation analysis, volcano plots and cluster heatmaps were generated via Origin Pro software (version 2021). Transcription factors were annotated via PlantTFDB (https://planttfdb.gao-lab.org/). Diagrams of the KEGG and correlation analyses of the DAMs or DEGs were generated via the OmicShare online program (https://www.omicshare.com/), and bubble and column charts were drawn via Microsoft Excel software (version 2019).

One−way analysis of variance (ANOVA) and multiple comparisons via Fisher’s least significant difference (LSD) test were performed via IBM SPSS Statistics software (version 23). The homoscedasticity and normal distribution of the data were tested via Levene’s test and the Shapiro−Wilk test, respectively. Student’s t test with paired and two−tailed distributions for pairwise comparisons was conducted via Microsoft Excel software (version 2019). The column charts were drawn via GraphPad Prism software (version 9.5.0).