Exploring the genetic basis of heterosis in eucalypt growth based on transcriptome analysis

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In order to reveal the genetic mechanism of the formation of heterosis in eucalypt growth, based on previous research on the relationship between eucalypt heterosis and parental combining ability, we selected two artificial hybrids 18H167 (T15 × U3423) and 19H74 (U3423 × U6) with significant differences in birth length and similar parental relationships as the research objects. Results Transcriptome analysis using RNA-seq technology showed that the correlation between gene expression levels indicated that the male parent had a greater impact on the heterosis of eucalypt growth. Based on GO and KEGG annotations, GSEA enrichment and WGCNA analysis identified 8 pathways and 7 Hub genes that may be related to growth differences in eucalypt. These candidate pathways are related to genes and ribosomal subunits, extracellular regulatory mechanisms, and three amino acid synthesis pathways. From their biological functions, the growth differences of eucalypt may be strongly correlated with their ability to adapt to environmental stress. AS analysis showed that the AS events of the two hybrids were significantly higher than those of their parents, with SE events possibly related to growth disadvantage and RI events more likely to be related to growth advantage. Conclusions This study provides a more in-depth exploration of the formation mechanism of heterosis in eucalypt growth, which is expected to guide the selection of parents in eucalypt hybrid breeding. The discovery of candidate genes/pathways provides genetic information for eucalypt genome or molecular marker assisted selection breeding. Eucalypt hybrid Heterosis Enrichment analysis WGCNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Eucalypt is native to Australia and surrounding areas, and is one of the most important fast-growing timber species worldwide. It has been widely introduced in tropical, subtropical, and some temperate regions. According to statistics, the global area of eucalypt plantations has exceeded 20 million hectares, with China, Brazil, India and other countries being the main planting countries [ 1 ]. Eucalyptus wood has long fibers and uniform texture, making it a high-quality raw material for fiberboard and papermaking industries. At the same time, its high biomass characteristics make it an important source of biomass energy. In addition, eucalypt has strong drought resistance and soil adaptability, which can be used for ecological restoration of degraded land [ 2 ]. Currently, large-scale monoculture afforestation in Chinese eucalypt plantations is a persistent issue, along with associated problems such as variety degradation, reduced productivity, and diminished stress resistance. The development and promotion of superior eucalypt varieties with diverse genotypes is the primary strategy for addressing these challenges. Given that the genetic transformation system for eucalypt in China remains in its nascent stages, conventional artificial hybridization continues to be the most effective approach for breeding novel eucalypt varieties. The effective exploitation of heterosis is crucial for the success of hybrid breeding programs. Heterosis has been widely applied in crops such as corn and rice. In the genetic improvement of new forest tree varieties, heterosis also has important value, for example, hybrids of Populus, Pinus, and Eucalypt often exhibit faster growth rates, higher wood yields, and stronger environmental adaptability [ 3 ]. China has achieved significant results in the research of eucalypt hybrid breeding and the development and application of new varieties, such as ( E. urophylla × E. grandis ) hybrid exhibits significant growth advantages in the South China region [ 4 ]. However, compared with crops, the molecular genetic mechanism of forest hybrid vigor is still relatively lagging behind, mainly due to the long generation cycle, complex genetic background, and lack of efficient molecular breeding technology system in forest [ 5 ]. Concurrently, eucalypt represents an introduced species within the Chinese context, characterized by a comparatively limited scope of genetic research. Furthermore, the challenges associated with establishing extensive hybrid populations have resulted in a paucity of reports elucidating the genetic underpinnings of heterosis in eucalypt at the omics level. Consequently, a comprehensive investigation into the genetic basis of heterosis in eucalypt is of paramount importance for expediting the selection of superior varieties and enhancing the productivity of artificial forests. Regarding the genetic mechanism of heterosis, in addition to its classical theories of dominance hypothesis, overdominance hypothesis, and epistasis hypothesis, many studies have shown that it may be related to factors such as allele complementarity, gene expression regulation changes [ 6 ], epigenetic modifications [ 7 ], and metabolic pathway optimization [ 3 ]. In recent years, with the development of high-throughput sequencing technology, transcriptome sequencing (RNA-seq) has become an important tool for studying heterosis, which can systematically analyze gene expression differences, alternative splicing, allele specific expression (ASE), and co expression networks between hybrid and parents. In forest trees, transcriptome analysis has been used to reveal key genes and pathways related to poplar heterosis, such as differential expression of auxin signaling, cell division, and secondary wall synthesis related genes [ 8 ]. However, there is still limited research on the molecular mechanisms of eucalypt heterosis, especially how the gene expression patterns of different hybrid combinations affect growth traits. Our research team initiated an investigation into the formation mechanism of eucalypt growth heterosis, focusing on the interplay between parental combining ability, heterosis, and the additive and non-additive genetic architectures of growth traits across diverse parental combinations. Despite this, the genetic underpinnings of heterosis in eucalypt growth disparities remained unexplored at the genetic level. Consequently, building upon the established correlation between parental combining ability and hybrid growth heterosis, this study selected two hybrids exhibiting significant growth heterosis differences and identified their respective parents. Employing transcriptome sequencing, we analyzed the relationship between gene differential expression and heterosis across parental and hybrids, as well as in dominant and non-dominant combinations, to identify functional genes and pathways associated with growth difference in eucalypt. This approach aims to elucidate the genetic basis of eucalypt heterosis, thereby enriching the theoretical framework of eucalypt hybrid breeding and refining breeding strategies. Furthermore, the identification of candidate genes related to growth differences will provide valuable genetic information for molecular marker-assisted selection breeding in eucalypt. Materials and Methods 2.1 Experimental Design and Sampling According to our previous research findings [9], the selection of genetic materials (Table 1) in this study was based on two principles: (1) The difference in heterosis is significant, which can be determined based on our previous research on the relationship between the combining ability of eucalyptus hybrid parents and heterosis; (2) The genetic relationship between the selected hybrid parents was similar, which could be achieved by having the same parents or having the same/similar tree species. Based on this, we selected the superior hybrid 18H167 (T15 × U3423) and the growth inferior hybrid 19H74 (U3423 × U6). For the selected genetic material, this study conducted transcriptome analysis between 5 comparison groups (a: H74 vs U6; b: H74 vs U3423; c: H167 vs H74; d: H167 vs T15; e: H167 vs U3423). Table 1 Hybrids studied with significant difference in growth and their parents of eucalypts Hybrids Growth Female Species Male Species 18H167 excellent T15 E.tereticornis U3423 E.urophylla 19H74 inferior U3423 E.urophylla U6 E.urophylla×E.tereticornis 2.2 RNA-seq 2.2.1 Quality control and sequence alignment The fragmented mRNA was used as a template to synthesize strand of cDNA. PCR library amplification was then performed, and finally, detection was carried out using the Illumina Novaseq X Plus. Reads were further filtered by fastp [10] (version 0.18.0). The parameters were as follows: 1) removing reads containing adapters; 2) removing reads containing more than 10% of unknown nucleotides(N); 3) removing low quality reads containing more than 50% of low quality (Q-value≤20) bases. Short reads alignment tool Bowtie2 [11] (version 2.2.8) was used for mapping reads to ribosome RNA (rRNA) database. Paired-end clean reads were mapped to the reference genome using HISAT2 2.1.0 [12] (Figure S1). The mapped reads of each sample were assembled by using StringTie v1.3.1 [13, 14] (Figure S2). A FPKM (fragment per kilobase of transcript per million mapped reads) value was calculated by using RSEM [15] software. 2.2.2 Sample relationship analysis and enrichment analysis Principal component analysis (PCA) was performed with R package gmodels (http://www.rproject.org/). RNAs differential expression analysis was performed by DESeq2 [16] software between two different groups (and by edgeR [17] between two samples) (Table S1). The genes/transcripts with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change≥2 were considered differentially expressed genes/transcripts. Significantly enriched Gene Ontology [18] database (http://www.geneontology.org/)\KEGG [19] terms in DEGs were defined by hypergeometric test, comparing with the whole genome background. We performed gene set enrichment analysis using software GSEA and MSigDB [20] to identify whether a set of genes in specific GO terms\KEGG pathways showed significant differences in two groups. 2.2.3 Splicing Variants and Mutation Calling The bcftools [21] was used for calling variants of transcripts, and ANNOVAR was used for SNP/InDel annotation. RNA editing refered to variants on the mRNA level : 1) Removing the low quality SNPs while calling SNP by bcftools. 2) Correcting the SNPs around InDel region. 3) Choosing non-overlapping SNPs in UTR and EXON region. 4) Choosing SNPs with reference reads>=2 and variate reads>=3. 5) Choosing SNPs with the mutation frequency between 0.1 and 0.9 [22, 23]. The software rMATS [24] (version 4.0.1) (http://rnaseq-mats.sourceforge.net/index.html) was used to identify alternative splicing events and analyze differential alternative splicing events between samples (Figure S3). Protein-Protein interaction network was identified using String v10 [25]. The network file was visualized using Cytoscape (v3.7.1) software. The Hisat2 was used in reconstruction of transcripts [26]. 2.2.4 WGCNA (weighted gene co-expression network analysis) Co-expression networks were constructed using WGCNA (v1.47) package in R [27]. Differential gene expression values were imported into WGCNA to construct coexpression modules using the automatic network construction function blockwiseModules. Genes with high Intramodular connectivity (K.in) and module correlation degree (MM) tended to be hub genes which might have important functions. Pearson correlation between each gene and growth trait data under the module was calculated, and gene significance value (GS) was obtained. Finally, GO and KEGG pathway enrichment analysis were conducted to analyze the biological functions of modules. 2.3 Determination of gene set related to growth differences in eucalypt It is generally believed that the results with | NES |>1, NOM p-val<0.05, and FDR q-val (Qvalue)<0.25 have the highest credibility. Therefore, the top 20 GO terms (GO enrichment) and pathways (KEGG enrichment) with the smallest Qvalue, as well as the top 20 pathways with the largest |NES| (GSEA enrichment), were selected. In order to improve the accuracy and efficiency of identifying the common differentially expressed gene pathways among the five comparison groups, we used Cross column duplicate filtering (Code 1) to determine the Common ID, and then used Q value search on demand (Code 2) and Merge with 3 decimal places retained (Code 3) to retain the 3 decimal places Qvalue, and merged the Common ID and Qvalue. In H167-H74, the enriched GO pathway gene set and the KEGG gene set corresponding to the same differential gene products (up-regulated proteins (enzymes) or metabolites) in the three KEGG pathways of the comparison groups were determined using Cross column duplicate filtering (Code 1), and the Hub gene set of WGCNA was defined as the preliminary target gene set for this study (Table 6). Subsequently, PPI (Protein Protein Interaction) network analysis was performed to determine the target gene set related to the heterosis of eucalyptus growth differences. Finally, use Delete duplicate values (Code 4) to remove duplicate genes and preserve unique values. Results 3.1 Preparation of preliminary data The proportion of clean reads was higher than 99.5% (Table S2), the proportion of Q20 and Q30 based quality values was higher than 95.34% (Table S3), and the proportion of unmapped reads was higher than 97.89% (Table S4). The sequencing quality of this study was reasonable. According to the alignment results of the Total_Mapped reads that could be located on the genome (Table S5), the proportion of sequencing reads aligned to exon regions for all samples was above 87.20% (Table S6), indicating a relatively complete gene annotation. Novel genes were genes that had not been included in the reference genome (Table S7). Generally, the proportion of known genes was higher than 64.19%, and the proportion of new genes was higher than 68.02% (Table S8), indicating a relatively complete reference genome. The proportion of genes with a coverage range of 80-100% was the highest, and the gene coverage of each sample was higher than 65.16% in this range (Figure S4). When the sequencing volume reached 30 reads (×1000000), the growth rate of the gene numbers in the 5 samples tended to flatten (Figure S5), and the gene coverage and sample sequencing saturation met the standards. 3.2 Sample Relationship Analysis Based on the tpm values of each gene (Table S9), the gene abundance was highest when log10 (tpm) was 1-2, and there was only one peak in gene abundance for each sample (Figure 1Aa). The gene expression levels of the five samples in this study were basically consistent (Figure 1Ab), which can basically excluded the influence of enrichment analysis on subsequent differentially expressed genes. The expression levels of the three parents remained relatively stable in the repetition (Figure 1B). The contribution of the first principal component to sample differences was higher, with H167 and H74 showing significant differences on PC2 (Figure 1Ba). This might indicate that variety differences dominate this major element. For the common parent U3423 of the two hybrids, H167 showed a higher correlation with it, and the correlation between H167 and the male parent U3423 was much higher than that with the female parent T15. Similarly, the correlation between H74 and the male parent U6 was much higher than its correlation with the female parent U3423. Therefore, we preliminarily speculated that the parent U3423 was more likely to inherit excellent growth traits to hybrid when producing hybrid, and the influence of the male parent on the heterosis of hybrid growth was greater. There were a maximum of 5157 differentially expressed genes between H167 and T15. There were 4460 differentially down-regulated genes between H74 and U3423 (Figure S6a), significantly higher than other comparison groups. This indicated that there were more differentially expressed genes between the female parent and the hybrid, so we might need to more accurately locate genes related to eucalypt growth differences through the differentially expressed genes between the male parent and the hybrid. There were many genes with significant differences between H74 and U6 (Figure S6b-6f), which was consistent with our preliminary speculation and might indicate that the influence of male parents on the growth and heterosis of hybrid was indeed more pronounced. The gene expression patterns in each comparison group in this study were similar, and the differences in expression levels were also significant (Figure S7). This indicated that it was feasible for us to select these 5 comparison groups to search for genes related to differences in eucalypt growth. 3.3 Enrichment analysis Comparing the common differentially expressed gene pathways with more pairs of comparison groups, the stronger the correlation between it and the growth differences of eucalypt. The priority of differential gene pathway selection was 5>4>3>2>1 (unit: pair of comparison group). When at the same priority, we first selected the comparison group with the highest likelihood of genes related to growth differences, namely H167-H74. Secondly, the two comparison groups we identified were two hybrids of the same variety, H167-U3423 and H74-U3423, which had significant differences in their growth traits. The likelihood of their common differential gene pathways being associated with growth differences was second highest, and they were highly correlated with growth heterosis. Finally, referring to the previous text, it was believed that the differential genes between the male parent and the hybrid had a stronger correlation with growth heterosis, namely H74-U6. Through GO enrichment analysis, we selected four pairs of differentially expressed gene pathways shared by the comparison group, namely H167-H74, H167-T15, H167-U3423, and H74-U3423, namely extracellular region (GO: 0005576), external encapsulating structure (GO: 0030312), and cell periphery (GO: 0071944). The Qvalue of the three GO pathways was 0.0<0.05. Through KEGG enrichment analysis, we found that there were three differentially expressed gene pathways shared by the four comparison groups, but the Qvalues of KO00909 and KO00061 were 0.10411 and 0.356548, respectively, which did not satisfy Qvalue ≤ 0.05. Therefore, one KEGG pathway Plant-parent interaction (KO04626) was selected, with Qvalue=0.091886<0.05 (Table 2, Figure 2; Figure S8-10). Figure 2 was a partial display of the enrichment circle diagram, and the complete enrichment circle diagram could be found in Figure S8. In addition, there was a common pathway Plant hormone signal transduction (KO04075) in the four comparison groups H167-T15, H167-U3423, H74-U3423, and H74-U6 between the hybrid and parents, with Qvalue=0.017821<0.05 (Table 2, Figure 2; Figure S8-10). These findings might suggest a discernible relationship between combining ability and heterosis. Similarly, for the GSEA-GO analysis results, we selected the large ribosomal subunit (GO: 0015934) (Figure 3a) and ribosomal subunit (GO: 0044391) (Figure 3b) of the common differentially expressed gene pathways in the four comparison groups, H167-H74, H167-T15, H167-U3423, and H74-U3423, both of which satisfied FDR q-val<0.25. However, for the GSEA-KEGG analysis results, the maximum number of comparison groups (5) only had one pathway Splicosome (KO03040) (Figure 3d) that satisfied FDR q-val<0.25. Therefore, we also chose H167-H74, H167-U3423, H74-U3423, H74-U6, these four pairs of comparison groups shared a differential gene pathway called Phenyalalanine, tyrosine and tryptophan biosynthesis (KO00400) (Figure 3c), Satisfied FDR q-val<0.25 (Table 3). Figure 3 was a partial display of the GSEA diagram, and the complete GSEA diagram could be found in Figure S12. In addition, we found that there were 8 common differentially expressed gene pathways Plant hormone signal transduction (KO04075) (Figure 2; Figure S8-10), Cutin, suberine and wax biosynthesis (KO00073), N-Glycan biosynthesis (KO00510), Various types of N-glycan biosynthesis (KO00513), Phenylpropanoid biosynthesis (KO00940), Flavonoid biosynthesis (KO00941), Ribosome biogenesis in eukaryotes (KO03008), Nucleocytoplasmic transport (KO03013) (Figure 3; Figure S12), FDR q-val<0.25 (Table 2-3) in the four comparison groups H167-T15, H167-U3423, H74-U3423, and H74-U6 between the hybrid and parents. These findings suggested a potential positive correlation between combining ability and heterosis, which aligned with our prior conclusions [9]. Table 2 GO and KEGG Enrichment Analysis Enrichment method Comparison group Common ID (Qvalue) GO H167-T15,H74-U6 GO:0016301(0.000),GO:0016310(0.000) H167-H74,H167-T15,H74-U6 GO:0004672(0.000),GO:0016773(0.000) H167-U3423,H74-U6 GO:0001101(0.000),GO:0010243(0.000) H167-H74,H74-U6 GO:0005524(0.007),GO:0008270(0.006),GO:0032555(0.006), GO:0035639(0.006) H167-H74,H167-T15,H167-U3423,H74-U3423 GO:0005576(0.000),GO:0030312(0.000),GO:0071944(0.000) H167-U3423,H74-U3423 GO:0009698(0.000),GO:0009832(0.000),GO:0016020(0.000), GO:0042546(0.000),GO:0071554(0.000) H167-H74,H167-T15 GO:0016772(0.000) KEGG H167-U3423,H74-U3423,H74-U6 KO00940(0.000),KO00941(0.042),KO01100(0.000),KO01110(0.000) H167-H74,H167-T15,H167-U3423,H74-U6 KO04626(0.092) H167-H74,H167-U3423,H74-U3423,H74-U6 KO00909(0.104) H167-T15,H167-U3423,H74-U3423,H74-U6 KO04075(0.018) H167-U3423,H74-U6 KO04016(0.222) H167-H74,H167-T15,H74-U3423,H74-U6 KO00780(0.438) H167-H74,H74-U6 KO01040(0.845) H74-U3423,H74-U6 KO00400(0.001),KO00910(0.327) H167-H74,H74-U3423,H74-U6 KO00944(0.196) H167-T15,H167-U3423,H74-U6 KO00062(0.222) H167-U3423,H74-U3423 KO00360(0.222),KO00520(0.261),KO00900(0.050),KO00902(0.042) H167-H74,H167-T15,H167-U3423,H74-U3423 KO00061(0.357) H167-H74,H167-U3423,H74-U3423 KO00500(0.313),KO01212(0.357) H167-H74,H167-T15,H74-U3423 KO00052(0.056) H167-H74,H167-T15 KO00511(1.000),KO00531(0.730) H167-T15,H167-U3423 KO00010(0.216),KO00480(0.010),KO02010(0.313) Based on the enrichment analysis results of the three types mentioned above, we have defined 8 gene sets related to the heterosis of eucalypt growth differences (Table 6). For the three KEGG gene sets, in the Plant-parent interaction (KO04626) pathway diagram, we found that the four comparison groups shared the differential gene product CDPK, CaMCML,EDS1,MPK3/6,RPS2 (Table 4). The differences in RPS2 were consistent among the four comparison groups. CDPK and CaMCML showed significant up-regulation between hybrids, while both up-regulation and down-regulation were observed between hybrid and parents, might indicating that both were genetically related. EDS1 exhibited down-regulation between the male parent and the hybrid, while up-regulation was observed between the two hybrids. MPK3/6 demonstrated up-regulation between the dominant hybrid and the male parent, as well as between the two hybrids (Figure S11A), which further supported the potential for a greater male influence on the growth traits of eucalypt, as previously suggested. In Phenylalanine, tyrosine and tryptophan biosynthesis (KO00400) pathway diagram, we found that the only common differential gene product among the four comparison groups was 2.6.1.5 (enzyme EC number), namely TAT and ARO8 (Table 4). Except for up-regulation between the hybrid with growth disadvantage and the female parent, down-regulation was observed between the hybrid with growth advantage and the parents, as well as between the two hybrids (Figure S11B). In the Splicosome (KO03040) pathway diagram, we found that the only common differential gene product among the five comparison groups was the protein complex Lsm (Table 4), which was located in the U4/U6 complex. It was crucial that Lsm was both up-regulation and down-regulation between the growth advantage hybrid and the parents, only down-regulation between the growth disadvantaged hybrid and the parents, and up-regulation between the two hybrids. This might indicate a strong correlation between it and growth differences. Secondly, according to our priority principle mentioned earlier, the SF3a and eIFA3 proteins shared by the four comparison groups might also be particularly critical (Table 4), which were located in the U2 and EJC/TREX complexes, respectively (Figure S11C). Table 3 Gene set enrichment analysis Gene set Comparison group Common ID (Qvalue) GO H167-U3423,H74-U6 GO:0000785(0.130) H167-H74,H74-U6 GO:0004521(0.457),GO:0004540(0.864),GO:0015149(0.427), GO:0015665(0.740),GO:0016893(0.478),GO:0043473(0.467), GO:0043476(0.461),GO:0043478(0.468),GO:0043479(0.468), GO:0043480(0.466),GO:1901618(0.732) H167-H74,H74-U3423,H74-U6 GO:0004523(0.443),GO:0016891(0.435) H74-U3423,H74-U6 GO:0034728(0.181) H167-T15,H74-U3423 GO:0005198(0.475),GO:0006220(0.161),GO:0006221(0.144), GO:0015926(0.269),GO:0051168(0.444),GO:0072527(0.314) H167-H74,H167-U3423,H74-U3423 GO:0005618(0.016),GO:0031225(0.002) H167-H74,H167-T15,H74-U3423 GO:0005840(0.179),GO:0072528(0.387) H167-U3423,H74-U3423 GO:0006364(0.072),GO:0008033(0.149),GO:0009451(0.060), GO:0009698(0.022),GO:0009699(0.016),GO:0009808(0.001), GO:0016298(0.013),GO:0016679(0.001),GO:0034470(0.068), GO:0042254(0.057),GO:0046992(0.014),GO:0046993(0.028) H167-H74,H74-U3423 GO:0009637(0.294) H167-T15,H167-U3423,H74-U3423 GO:0009832(0.000),GO:0016682(0.000),GO:0022613(0.157),GO:0042546(0.001) H167-H74,H167-T15,H167-U3423,H74-U3423 GO:0015934(0.166),GO:0044391(0.087) H167-T15,H167-U3423 GO:0008375(0.135),GO:0015935(0.093) KEGG H167-U3423,H74-U3423,H74-U6 KO00062(0.210),KO00330(0.433),KO00520(0.079),KO00945(0.023) H167-T15,H167-U3423,H74-U3423,H74-U6 KO00073(0.110),KO00510(0.111),KO00513(0.134),KO00940(0.028), KO00941(0.000),KO03008(0.016),KO03013(0.215) H167-H74,H167-T15,H167-U3423,H74-U6 KO00195(0.111) H167-H74,H167-T15,H74-U6 KO00196(0.991),KO03410(1.000) H167-H74,H74-U6 KO00230(1.000),KO00350(0.923),KO00903(0.979),KO03050(0.969) H74-U3423,H74-U6 KO00250(0.980) H167-H74,H167-U3423,H74-U3423,H74-U6 KO00400(0.045) H167-T15,H74-U6 KO00500(0.983) H167-H74,H167-T15,H74-U3423,H74-U6 KO00904(0.940) H167-U3423,H74-U6 KO00909(0.411),KO03450(0.424) H167-T15,H167-U3423,H74-U6 KO03015(0.681) H167-T15,H74-U3423,H74-U6 KO03022(0.845) H167-H74,H167-T15,H167-U3423,H74-U3423,H74-U6 KO03030(0.253),KO03040(0.181) H167-H74,H167-U3423,H74-U3423 KO00040(0.054),KO00900(0.270),KO03060(0.927),KO04141(0.437) H167-T15,H74-U3423 KO00052(0.560),KO00561(0.723),KO00860(0.989) H167-H74,H74-U3423 KO00053(0.960) H167-H74,H167-T15,H74-U3423 KO00300(1.000),KO00565(0.410) H167-T15,H167-U3423,H74-U3423 KO00600(0.222),KO00630(0.410),KO00910(0.562),KO03020(0.058) H167-U3423,H74-U3423 KO00770(0.132),KO00908(0.410),KO04145(0.099) H167-H74,H167-T15,H167-U3423,H74-U3423 KO03010(0.005) H167-H74,H167-U3423 KO00360(0.036),KO00514(0.340),KO00950(0.421),KO00960(0.226) H167-H74,H167-T15 KO00531(0.990),KO00563(0.807),KO00920(1.000) H167-H74,H167-T15,H167-U3423 KO00902(0.429) Table 4 The same differentially expressed gene products in the KEGG pathway KEGG Pathway Comparison group Common gene product Plant-pathogen interaction (KO04626) H167-T15,H167-U3423,H74-U6 BAK1BKK1,CNGCs,FLS2,KCS1/10,PR1,Pti5,RIN4,RPM1,WRKY2533 H167-H74,H167-T15,H167-U3423,H74-U6 CDPK,CaMCML,EDS1,MPK3/6,RPS2 H167-H74,H167-U3423 SGT1 Phenylalanine, tyrosine and tryptophan biosynthesis (KO00400) H167-U3423,H74-U3423,H74-U6 1.1.1.25,2.5.1.54,2.6.1.9,4.2.1.10,4.2.1.51,4.2.1.91 H74-U3423,H74-U6 2.6.1.1,4.2.1.20 H167-H74,H167-U3423,H74-U3423,H74-U6 2.6.1.5 H167-U3423,H74-U3423 2.7.1.71,4.1.3.27,5.4.99.5 Spliceosome (KO03040) H167-H74,H167-T15,H167-U3423,H74-U3423,H74-U6 Lsm H167-H74,H167-U3423,H74-U3423,H74-U6 SF3a,eIFA3 H167-H74,H167-T15,H167-U3423,H74-U6 Snu66 H167-T15,H167-U3423,H74-U3423,H74-U6 SR H167-T15,H167-U3423,H74-U6 HSP73 H167-U3423,H74-U3423 THOC H167-T15,H167-U3423 PRL1 3.4 Mutation analysis The trends of SNP and InDel statistics and RNA editing classification analysis for all samples were almost the same, and mutations would not significantly affect the screening of differentially expressed genes in the previous section (Figure S13). The RNA editing results of two biological replicates of H167 and H74 showed significant differences. H167 had a higher frequency at an editing ratio of 0.5, while H74 had a higher frequency at an editing ratio of 0.25. However, the editing ratio and frequency of H74-1 are similar to H167. And during the increasing editing ratio, there were significant differences in the RNA editing frequencies of the three biological replicates of H74. However, the RNA editing frequency of the other four samples remained relatively stable with the change of editing ratio in the three biological replicates, showing a normal distribution with a mode of 0.5 (Figure 4). This might indicate uncertain variations in the modification and processing of post transcriptional mature RNA molecules in growth disadvantaged hybrid. It might also indicate that dominant growth genes have genetic stability. 3.5 Alternative splicing analysis Two hybrids had more alternative splicing numbers than the parents, and the heterosis might come from AS (Figure S14). In JC only difference AS, the number of AS between H74 and U6 was generally the highest, and the number of AS of each type was also the highest. The number of AS between H167 and U3423 was generally the lowest (Figure S15A). This was consistent with our previous conclusion that growth traits might be more closely related to the male parent. However, in the JC+readsOnTarget differential AS, H167 vs H74 had the lowest overall number of AS (Figure 5b). Among the 5 comparison groups, the two most common event types were SE and RI. The comparison groups of H74 vs U6, H74 vs U3423, and H167 vs H74 had the highest number of SEs, followed by RI. The two comparison groups, H167 vs T15 and H167 vs U3423, had the highest number of RI, followed by SE (Figure 5). These might indicate that SE was likely related to growth disadvantage, while RI was more likely to be related to growth advantage. Only H167 vs H74 (Figure 5) are shown in the main text, and the rest can be found in the supplementary chart (Figure S15). 3.6 WGCNA analysis Based on the five traits related to eucalypt growth, including HT, DBH, SUR, VOL, and SS, the tan module had the strongest correlation with growth. Except for the SS trait which showed a significant positive correlation with the module, all other traits showed a significant negative correlation (p<0.1). In addition, the grey60 module had a strong correlation with growth, and the positive and negative correlations between the five growth traits and grey60 were consistent with the tan module (p<=0.1). The skyblue module had the strongest and most significant negative correlation with HT among all modules (|Pearson correlation coefficient |=0.7 maximum; P=0.004 minimum) (Figure 6). It was worth mentioning that this was consistent with our previous research results [9], which suggested that the growth quality and straightness influenced by genes in the module might be exactly opposite. In the analysis of trait correlation, we defined |GS|>0.8, |K.within|>100 and |MM|>0.9 under the tan module were highly correlated with growth traits (Table 5). Three hub genes, MSTRG.35350，MSTRG.4104 and ncbi_104443483, were screened out (Table 6). Analysis revealed that all five growth traits were positively correlated to varying degrees with K. within and MM values (Figure S16). Especially for the two most critical growth traits of HT and DBH, the positive correlation between GS and K. within and MM values was the strongest, and the vast majority of genes were clustered near the fitting lines with the highest GS, K. within, and MM values (Figure S16a-b). This further demonstrated the important biological role of the tan module in the differential growth of eucalypt. Table 5 Growth trait related genes in the tan module Gene GS GS.pvalue K.within MM HT ncbi_104435039 -0.807 0.00027961 63.975 0.834 ncbi_104433568 -0.809 0.00026183 83.985 0.869 MSTRG.35350 -0.821 0.000176975 135.789 0.956 MSTRG.4104 -0.821 0.000176975 135.789 0.956 ncbi_104417067 -0.824 0.000160364 54.319 0.805 ncbi_104443483 -0.830 0.00012707 103.313 0.908 ncbi_104432758 -0.861 3.83E-05 70.148 0.845 ncbi_104448101 -0.874 2.06E-05 68.861 0.833 ncbi_104438296 -0.914 1.95E-06 74.302 0.854 MSTRG.16990 -0.922 1.05E-06 59.302 0.815 ncbi_104419919 -0.940 2.00E-07 54.963 0.802 DBH ncbi_104435039 -0.814 0.000219944 63.975 0.834 ncbi_104449238 -0.846 7.03E-05 10.338 0.554 VOL ncbi_104449238 -0.814 0.000218769 10.338 0.554 SS ncbi_108959151 0.851 5.77E-05 51.770 0.792 Analysis found that all significantly enriched GO pathways in the tan module were biological processes (Figure S17a), which was different from our previous GO enrichment of all genes. The significant pathways enriched in KEGG were mostly related to Metabolism (Figure S17b). More importantly, the pathways significantly enriched in the tan module were highly consistent with our previous KEGG enrichment results, where Plant-pathogen interaction and Spliceosome were present in both enrichments. In addition, the most significant GO enrichment pathway captured in the tan module was response to endogenouous stimulus (Pvalue=2.2811E-10) (Figure S18a), the most significant GO enrichment pathway was Galactose metabolism (Qvalue=0.006965) (Figure S18b). We found significant up-regulation of proteins corresponding to 2.4.1.123 and 2.4.1.82 in the Galactose metabolism pathway (Figure S19). Finally, we screened three Hub genes nbci_104448375,nbci_104440165 and nbci_104434572, as well as one transcription factor (TF) nbci_104452186 (Figure S20) (Table 6). In summary, we had implemented a screening process for genes related to growth differences in eucalypt from surface to line and then to point (Table 6). Finally, the target gene PPI network was used to further streamline the genes (H167-H74) associated with heterosis in eucalypt growth differences. We found no connectivity between Hub genes (Figure S21). Therefore, the gene with the highest average abundance and connectivity (abundance/connectivity=10) was identified from 5 GO pathways (Figure 22a-e) and 3 KEGG pathways (Figure 22f-h) as the final target gene for this study. Only the pathway with the most target genes was displayed in the main text (Figure 7). See supplementary charts for the rest (Figure 22). Table 6 Genes related to growth differences in eucalypt extracellular region (GO:0005576) external encapsulating structure (GO:0030312) cell periphery (GO:0071944) large ribosomal subunit (GO:0015934) ribosomal subunit (GO:0044391) Plant-pathogen interaction (KO04626) Phenylalanine, tyrosine and tryptophan biosynthesis (KO00400) Spliceosome (KO03040) Hub MSTRG.14620, MSTRG.7374, ncbi_104414198, ncbi_104414737, ncbi_104418686, ncbi_104419239, ncbi_104419266, ncbi_104419267, ncbi_104421220, ncbi_104421331, ncbi_104422176, ncbi_104422221, ncbi_104423986, ncbi_104424336, ncbi_104424824, ncbi_104425290, ncbi_104429693, ncbi_104430354, ncbi_104431376, ncbi_104431697, ncbi_104433012, ncbi_104433102, ncbi_104433114, ncbi_104433143, ncbi_104433278, ncbi_104433548, ncbi_104434119, ncbi_104434971, ncbi_104436031, ncbi_104439389, ncbi_104440647, ncbi_104444321, ncbi_104445772, ncbi_104447974, ncbi_104448866, ncbi_104449401, ncbi_104453907, ncbi_104454445, ncbi_104456301, ncbi_104456417, ncbi_104456797, ncbi_104456805, ncbi_108960152 MSTRG.22406, MSTRG.2742, ncbi_104414130, ncbi_104414885, ncbi_104414996, ncbi_104415288, ncbi_104416283, ncbi_104418615, ncbi_104419209, ncbi_104419698, ncbi_104419919, ncbi_104422202, ncbi_104422289, ncbi_104422587, ncbi_104431265, ncbi_104432337, ncbi_104433339, ncbi_104433444, ncbi_104436406, ncbi_104437065, ncbi_104437692, ncbi_104437950, ncbi_104438301, ncbi_104440602, ncbi_104441483, ncbi_104441601, ncbi_104444833, ncbi_104447095, ncbi_104448843, ncbi_104450091, ncbi_104454132, ncbi_104454644, ncbi_104456905, ncbi_104457259, ncbi_104457277 MSTRG.1672, MSTRG.23128, MSTRG.34138, MSTRG.8582, ncbi_104421866, ncbi_104440695, ncbi_104440743, ncbi_104452223 ncbi_104422945, ncbi_104444802 MSTRG.10089, MSTRG.35802 ncbi_104448874, ncbi_104429374, ncbi_104430019, ncbi_108956299, MSTRG.6764, MSTRG.7876, MSTRG.26157, ncbi_104448378, ncbi_104451496, ncbi_104432152, ncbi_104450866, ncbi_104451452, ncbi_104452309, ncbi_104452311, ncbi_104452312, ncbi_104451438 ncbi_104440906, ncbi_104440877, ncbi_104440927 ncbi_104453291, MSTRG.13227, MSTRG.8529, MSTRG.13017, ncbi_108954086 MSTRG.35350, MSTRG.4104, ncbi_104443483, nbci_104448375, nbci_104440165, nbci_104434572, nbci_104452186 Discussion The up-regulation of extracellular region genes has been implicated in grape development [ 28 ] and the enhancement of egg quality and reproduction in aquaculture [ 29 ]. The external encapsulating structure primarily refers to the physical barrier surrounding cells or organisms, exemplified by cell walls. Currently, there is a paucity of specific reports linking this pathway to growth. However, we hypothesize that this enrichment pathway may modulate cell wall relaxation and facilitate cellular and organ growth by influencing cellulose, hemicellulose, and pectinase within plant cell walls. Furthermore, the cell periphery was associated with the cold tolerance of eucalyptus trees [ 30 ]. Based on the proteins localized within the KEGG pathway in this study, extracellular region may regulate growth mechanisms by transmitting growth factor signals to cell periphery membrane receptors, activating the MAPK/CDPK pathway, facilitating protein or metabolite transport, and mitigating environmental stress during the reproductive phase. Ribosomal subunits are the core machinery of protein synthesis, and their assembly, activity, and regulation directly affect cell proliferation, metabolic adaptation, and tissue development. The Cryphonectriaceae isolate identified from the large ribosomal subunit of eucalypt was associated with pathogenicity in two eucalypt hybrids [ 31 ]. Large subunit ribosomal DNA was commonly used for physiological activity research [ 32 ] and identification of fungi [ 33 ]. Although there is currently no research indicating a direct relationship between ribosomal subunit and crop or forest growth, based on the proteins located in the KEGG pathway in this study, large ribosomal subunit and ribosomal subunit may be quite related to the translation and assembly of RPS2, Lsm, SF3a, and elF3, and thus participate in the synthesis and genetic processes of eucalypt growth substances. The five GO pathways obtained through enrichment in this study were all cellular component that described genes. Cellular component was influenced by light conditions, which in turn affect the metabolism and developmental processed of rapeseed growth [ 34 ], and were related to the molecular mechanisms of chicken growth [ 35 ]. One pollen-specific protein (Cla001608) that was in cellular component etc, providing insight into the molecular basis of the developmental stages of male flowers in watermelon and may aid in dominant cross breeding [ 36 ]. The dynamic coordination of cellular components and the efficiency of growth signaling pathways may be key to regulating growth. Compared with the KEGG enrichment analysis results of this study, previous research on plant-pathogen interaction, biosynthesis of three amino acids, and spliceosome related pathways has mostly been related to crop disease or stress resistance, with few studies related to growth traits. Studies have shown that plant plant interaction promotes the growth of rice [ 37 ] and tobacco [ 38 ], providing new insights and theoretical foundations for their breeding. Plant-pathogen interaction was associated with the shedding of mature sugarcane leaves [ 39 ]. As the name suggests, plant-pathogen interaction was more related to disease resistance and participates in abiotic stress tolerance [ 40 ], such as gummy stem blight (GSB) [ 41 ], and P. brassicae infection under dry climate conditions [ 42 ]. In addition, it affected salt resistance [ 43 ], drought resistance [ 44 ], detoxification pathways [ 45 ], and might also affect the mechanisms of flowering time control and adaptive evolution in plants growing at high altitudes [ 46 ]. Phenylalanine, tyrosine and tryptophan biosynthesis might be related to the mechanisms of NC against hypoxic stress [ 47 ]. CircRNAs (non-coding RNAs) played roles on spliceosome, etc., showing the potential role involved in the abiotic stress response in tomato [ 48 ]. Study found some key genes like spliceosome pathway (13 miRNAs) were about the mechanism underlying grapevine responses to heat and drought stress [ 49 ]. However, based on the pathway enrichment results of our transcriptome data, it is believed that these three pathways may be related to the heterosis of eucalypt growth, certain gene expression products in the pathway played an indispensable role. Therefore, we focus on proteins and metabolites regulated by genes with significant upregulation and downregulation changes within the pathway. Calcium-dependent protein kinases (CDPKs), which were important sensors of Ca2 + flux in plants, were known to play essential roles in plant development and adaptation to abiotic stresses [ 50 ]. Studies have shown that PnCDPK1 was related to Japanese Morning Glory's ( Pharbitis nil )’s germination, seedling growth [ 51 ] and changed in fruit yield and dry matter production of tomato leaves [ 52 ]. It seemed that PnCDPK52 is associated with the germination process and PnCDPK56 with seedling growth [ 53 ]. Perhaps, PgCDPK1a was involved in ginseng growth, as a positive regulator [ 54 ]. CaM-like protein CML is involved in plant growth, development, and stress adaptation [ 55 ]. Both this study and the aforementioned research indicate that CDPK and CaMCML have important relationships with plant growth. EDS1 is Enhanced Disease Susceptibility 1, which mainly plays a role in the innate immune response of plants and regulates seed yield [ 56 ]. Poplar EDS1 affected tree morphology, photosynthetic efficiency, ROS and SA metabolism, as well as leaf senescence [ 57 ]. MPK usually refers to Mitogen Activated Protein Kinase, which plays an important role in cell signaling, especially in cell growth, differentiation, stress response, and other aspects. Studies have shown that, light signal transcription factors FHY3 and FAR1 could integrate light signals with immune signals by directly interacting with EDS1, widely regulating plant growth [ 58 ]. EDS1 might provide a novel resistance mechanism for the sustainable management of rust diseases [ 59 ]. According to Brown et al.'s study, MPK was associated with cold adaptation and frost resistance [ 60 ]. The MAPK signaling pathway in plants maintained the adaptability of Populus simonii × Populus nigra to rapid growth by regulating the signal transduction of salt tolerant hormones [ 61 ]. Both this study and the aforementioned research indicate that EDS1 has an important relationship with plant growth. RPS2 is an abbreviation for ribosomal protein S2, belonging to the S5P family of ribosomal proteins. It is a component of the 40S subunit and might be found in tropical L Plays an important role in biology [ 62 ]. It was also related to the growth of maltose and glucose [ 63 ]. A review put special focus on LSM rings' function in abiotic stress responses [ 64 ]. The mediator and Lsm complex synergistically controlled the growth regulation expression of ribosomal protein genes at the transcriptional and splicing levels [ 65 ]. SF3a mRNA splicing complex was required for a robust innate immune response [ 66 ]. There was research showing that eIF3 binding mRNA had a higher ribosome density in growing cells [ 67 ]. These conclusions all indicated that they were all related to RNA metabolism, possibly related to the sustained expression of growth response factors and the improvement of translation efficiency. Utilizing WGCNA, this study identified 19 gene co-expression modules significantly associated with growth traits. The tan module exhibited the most robust correlation with the target phenotype (Fig. 6 ). Compared to single-gene analysis, WGCNA elucidates synergistic regulatory patterns within gene modules, offering a systems-level perspective on the molecular mechanisms underlying complex traits. However, the connectivity of hub genes identified within the tan module via PPI network analysis was not strong. This may be attributed to the limited sample size, which could compromise module stability. Future studies should incorporate larger sample sizes to validate the generalizability of these hub genes. Furthermore, the extended breeding cycle of forest trees presents challenges in validating the regulatory effects on growth through functional experiments, such as hub gene knockout or overexpression. Nevertheless, we hypothesize that the upregulation of GOLS and K06617 proteins within the Galactose metabolism pathway, mediated by seven hub genes, influences growth quality (Figure S19). Consequently, future investigations may integrate single-cell metabolomics, multidimensional omics, and gene editing technologies to elucidate the correlation between key proteins and energy metabolism, thereby unraveling their profound impact on the heterosis of Eucalyptus growth. This approach aims to elucidate the regulatory mechanisms governing the spatiotemporally specific interaction networks of complex proteins, ultimately providing a programmable molecular blueprint for the precise design of "high-yield, stable" intelligent hybrids. Advantageous growth genes demonstrate considerable genetic stability under selective pressure, coupled with distinct alternative splicing profiles. Splicing events (SE) are observed in the inheritance of growth disadvantage, whereas intron retention (RI) is the converse. Dominant genes may mediate genetic stability and phenotypic plasticity by suppressing single nucleotide polymorphism (SNP) mutations (preserving core function) while permitting regulated RI events (generating functional isoforms) (Figure S13; Figure S15). These findings suggest that shear regulation may function as a buffering mechanism for adaptive evolution. During the genetic process, functional loss mutations resulting from exon skipping (SE) can induce growth disadvantages, whereas intron retention (RI) events may facilitate the accumulation of advantageous mutations. This 'splicing-mediated mutation buffering' could be attributed to the elevated RI/SE ratio during the genetic expression of growth-dominant genes (Figure S15). Mutations arising from SE events may initiate nonsense-mediated mRNA decay, leading to energy expenditure and growth impairment; conversely, RI may generate regulatory non-coding RNAs or functionally acquired isoforms by incorporating intronic regulatory elements, thereby promoting environmental adaptation. This energy function trade-off, or key driving force, in shaping shear patterns within the environment, elucidates the macroscopic correlation between shear patterns and mutation adaptation. Consequently, scRNA sequencing and CRISPR technologies can be employed to investigate the spatiotemporal heterogeneity of shear variation at single-cell resolution, the regulatory roles of RI events in protein interaction networks, and the co-evolutionary relationship between shear regulation and epigenetic modifications (e.g., DNA methylation). We have depicted the classification and quantity of JC only differential AS using a bar chart (Figure S15A) and represented the classification and quantity of JC + readsOnTarget differential AS using a pie chart (Figure S15B). Surprisingly, the classification trend of JC + readsOnTarget differential AS aligns perfectly with that of JC only differential AS. However, the number of differential AS detected using JCEC values exceeded that detected using JC values across all five comparison groups. Notably, in H74 vs U6, H74 vs U3423, and H167 vs H74, the differences in SE and RI counts were less pronounced when compared to those detected using JC values (Figure S15). Although in most studies, utilizing JC + readsOnTarget for differential AS statistics appears to yield fewer events with higher confidence and clearer biological significance, in our study of heterosis in eucalypt growth differences, we may lean towards using the JC only method for differential AS classification and quantitative analysis. When solely exploring genes associated with growth traits, we might opt for JC + readsOnTarget to analyze the differential AS classification and quantity between two hybrids exhibiting significant growth differences. This approach would help narrow down the scope of alternative splicing events, facilitate verification or refined analysis, and ensure the reliability of the identified differential events. Conclusions This research examined the mechanism underlying heterosis in eucalypt growth, identifying eight pathways and seven hub genes potentially associated with growth variations in eucalypt through enrichment and WGCNA analyses. Declarations Corresponding author Correspondence to Wanhong Lu. Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding: the National Key Research and Development Program of China, grant number 2022YFD2200203–3 Author Contribution LJZ is responsible for experimental design and afforestation; LY and LWH are responsible for controlling pollination and afforestation; LG and HAY are responsible for afforestation; SZY and LWH are responsible for data collection and investigation; SZY is responsible for data organization and analysis; SZY and LWH are responsible for writing manuscript. Acknowledgement We are grateful to the fund the National Key Research and Development Program of China, grant number 2022YFD2200203--3. Data Availability Data is provided within the manuscript or supplementary information files.The first author ( [email protected] ) has to be contacted in case of any queries or requirement of data. References Zhou XD, Wingfield MJ. Eucalypt diseases and their management in China. Australas Plant Path. 2011; 40(4): 339-345. https://doi.org/10.1007/s13313-011-0053-y Ouyang LN, Chen SX, Yang WT, Zheng JQ, Ye LS, Liu Q, Yang, JQ. Organic fertilizer improved the lead and cadmium metal tolerance of Eucalyptus camaldulensis by enhancing the uptake of potassium, phosphorus, and calcium. Front Plant Sci. 2024; 15: 1444227. https://doi.org/10.3389/fpls.2024.1444227 Su ZY, Lu WH, Lin Y, Luo JZ, Liu G, Huang AY. Exploring the Genetic Basis of Calonectria spp. Resistance in Eucalypts. CIMB. 2024; 46: 10854-10879. https://doi.org/10.3390/cimb46100645 Liu G, Wu ZH, Luo JZ, Wang CB, Shang XH, Zhang GW. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6499800","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452501025,"identity":"3feb1592-8f81-4a83-9f87-051cb420fc54","order_by":0,"name":"Zhiyi Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2SsUoDQRCG/+UkNidnOYLknkBYCagg5FmyXHGllhaCFw42jWArxIewEuxOFkyTB0iwuSWtRcorInHuoqlyi6XFftUwzLe7M7OAx/MfIZFh0ESi3GT2h0O7/JsSyE0mNHmPnMo2+lUo1Yehw4jHuaZyZRCNDFDdIj4/spoP6ndPst2KeHrTpLQBTROI+3ecvo6VLq+R9M6K3UrA9aQyA4mrAgfcl/xQI0ko1EuL0qmVAT9MRguIr1qZ872hQwkbpcMKJQiaW2bCrRCp/ELpFDRbIDjmXp6niocs23uJH1M7r1aXiB64/U+emJxMrF3e9LttCrNXr+Yu47VXwPonKVvLawLn7/B4PB4PvgF6+Vf02tjgSQAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":true,"prefix":"","firstName":"Zhiyi","middleName":"","lastName":"Su","suffix":""},{"id":452501027,"identity":"f26690b0-d307-42ff-ade9-7f03fe8f2ded","order_by":1,"name":"Wanhong Lu","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Wanhong","middleName":"","lastName":"Lu","suffix":""},{"id":452501028,"identity":"b8ccb0d2-b66e-477a-a03a-2e24f07d4da7","order_by":2,"name":"Yan Lin","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Lin","suffix":""},{"id":452501030,"identity":"ee6026ce-45a9-4f5f-9e70-d4025b7de808","order_by":3,"name":"Guo Liu","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Liu","suffix":""},{"id":452501035,"identity":"fb96a75e-7ba1-4e67-a7e9-74c70060352f","order_by":4,"name":"Anying Huang","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Anying","middleName":"","lastName":"Huang","suffix":""},{"id":452501038,"identity":"be55033a-22f3-4129-86a7-7fb0ce19945b","order_by":5,"name":"Jianzhong Luo","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Jianzhong","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-04-22 03:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6499800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6499800/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12077-9","type":"published","date":"2025-10-03T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82252248,"identity":"e4e7b038-0b02-4c70-88c5-f32e580803a5","added_by":"auto","created_at":"2025-05-08 10:20:38","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":815403,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression and sample relationship analysis (Note: A: Gene expression distribution. a: Gene expression abundance distribution plot (The X-axis in the figure is log10 (tpm), and the higher the value, the higher the gene expression level; The Y-axis represents the abundance of genes, which is the number of genes corresponding to the expression level on the horizontal axis divided by the total number of detected expressed genes; Each color in the figure represents a sample; The peak of the distribution curve represents the region with the highest concentration of gene expression in the entire sample.); b: Violin plot of gene expression levels (The violin plot is generally used for visualizing gene abundance expression and can display data density at any location. The white dots represent the median Q2 (i.e. half of the data is greater than the median, above it, and the other half is less than the median, below it); The black rectangle is the range from the lower quartile to the upper quartile, with the upper edge of the rectangle being the upper quartile Q3, representing that one-quarter of the data is greater than the upper quartile, and the lower edge being the lower quartile Q1, representing that one-quarter of the data is less than the lower quartile. The interquartile spacing IQR (the upper and lower quartiles are the number spacing); The length represents the degree of dispersion and symmetry of non anomalous data, with longer representing dispersion and shorter representing concentration; The black line running through the violin chart represents the range from the minimum non outlier min to the maximum non outlier max, with the upper and lower limits representing the upper and lower limits respectively. Data outside this range is considered abnormal; The outer shape of the black rectangle represents kernel density estimation, with the length of the Y-axis representing the degree of data dispersion and the length of the X-axis representing the distribution of data at a certain vertical coordinate position.) B: Sample relationship analysis. a: Principal Component Analysis of Samples (The PC1 coordinate represents the first principal component, and the percentage in parentheses represents the contribution of the first principal component to the sample differences; The PC2 coordinate represents the second principal component, and the percentage in parentheses represents the contribution of the second principal component to the sample differences. The colored dots in the figure represent each sample.); b: Sample correlation heatmap (The horizontal and vertical axes in the figure represent each sample, and the color intensity indicates the magnitude of the correlation coefficient between the two samples. The closer it is to blue, the greater the correlation; the closer it is to white, the smaller the correlation.).)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/46622cbace44df1450b3683e.jpeg"},{"id":82252249,"identity":"998097f6-9d41-4091-ac13-55577d90f119","added_by":"auto","created_at":"2025-05-08 10:20:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":521646,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment circle diagram (Note: A: GO; B: KEGG. The manuscript image only shows one comparison group H167-H74. a: GO; b: KEGG. First circle: Enrich the top 20 GOterms (A)/pathway (B), and outside the circle is the coordinate ruler of the number of differentially expressed genes. Different colors represent different Ontology (A)/A class (B); Second circle: The number and Q value of the GOterm (A)/pathway (B) in the differential gene background. The more differential gene backgrounds there are, the longer the bars, and the smaller the Q value, the redder the color; Third circle: Bar chart of the proportion of upregulated and downregulated differentially expressed genes, with dark purple representing the proportion of upregulated differentially expressed genes and light purple representing the proportion of downregulated differentially expressed genes; The specific numerical values are displayed below; Fourth circle: RichFactor values for each GOterm (A)/pathway (B) (the number of differentially expressed genes in the GOterm (A)/pathway (B) divided by all numbers in the GOterm (A)/pathway (B)), background grid lines, with each grid representing 0.1.)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/beeb7669eea171cb3d06a912.jpeg"},{"id":82251963,"identity":"7f1463c5-2531-4fc7-b671-c0b798a953b7","added_by":"auto","created_at":"2025-05-08 10:12:38","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":853976,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA ES diagram (Note: The manuscript image only shows one comparison group H167-H74. a: GO:0015934; b: GO:0044391; c: KO00400; d: KO03040.)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/7b480e21a32f412f6a782d5f.jpeg"},{"id":82251962,"identity":"afbe638f-f2f5-4792-b3be-54d3097664cc","added_by":"auto","created_at":"2025-05-08 10:12:38","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":526843,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical chart of RNA editing frequency\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/ab663f02edc9836d1eaa99a3.jpeg"},{"id":82251961,"identity":"0265c587-efcd-4c85-9c9a-a784c43d86f1","added_by":"auto","created_at":"2025-05-08 10:12:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":211841,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential AS classification and quantity statistical diagram (Note: a: JC only difference AS classification and quantity statistical chart; b: JC+reads OnTarget difference AS classification and quantity statistics chart.)\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/3b082784475d1be6ab90c471.jpeg"},{"id":82253018,"identity":"dfa730d3-c882-4ac2-9654-7a66651d3341","added_by":"auto","created_at":"2025-05-08 10:28:39","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":528235,"visible":true,"origin":"","legend":"\u003cp\u003eModule trait relation diagram (Note: The horizontal axis represents the trait, and the vertical axis represents the module. Plot the Pearson correlation coefficient. Red represents positive correlation, green represents negative correlation, darker colors indicate stronger correlation, and the number in parentheses below represents significance P value. The smaller the value, the stronger the significance. This chart can intuitively reflect the correlation between each module and each trait.)\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/e380c708a49ae350862120e6.jpeg"},{"id":82252251,"identity":"9cf4f70e-2532-4a3b-aaf0-8811ac9055e4","added_by":"auto","created_at":"2025-05-08 10:20:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":941697,"visible":true,"origin":"","legend":"\u003cp\u003eCell periphery (GO:0071944) Target gene PPI network (Note: The PPI network of other target genes can be found in Figure S22)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/e6855b96de346ba5eb892f13.png"},{"id":92884027,"identity":"afccda08-cbc6-4480-bb47-bb8a81c6783a","added_by":"auto","created_at":"2025-10-06 16:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5052666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/b566a880-4aa0-4432-a29d-7e86d1a5cbb2.pdf"},{"id":82251972,"identity":"d77cab26-df8a-437a-855e-6170007eb051","added_by":"auto","created_at":"2025-05-08 10:12:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4653640,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499800/v1/17f2268a0d43afe51da389dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the genetic basis of heterosis in eucalypt growth based on transcriptome analysis","fulltext":[{"header":"Background","content":" \u003cp\u003eEucalypt is native to Australia and surrounding areas, and is one of the most important fast-growing timber species worldwide. It has been widely introduced in tropical, subtropical, and some temperate regions. According to statistics, the global area of eucalypt plantations has exceeded 20\u0026nbsp;million hectares, with China, Brazil, India and other countries being the main planting countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Eucalyptus wood has long fibers and uniform texture, making it a high-quality raw material for fiberboard and papermaking industries. At the same time, its high biomass characteristics make it an important source of biomass energy. In addition, eucalypt has strong drought resistance and soil adaptability, which can be used for ecological restoration of degraded land [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrently, large-scale monoculture afforestation in Chinese eucalypt plantations is a persistent issue, along with associated problems such as variety degradation, reduced productivity, and diminished stress resistance. The development and promotion of superior eucalypt varieties with diverse genotypes is the primary strategy for addressing these challenges. Given that the genetic transformation system for eucalypt in China remains in its nascent stages, conventional artificial hybridization continues to be the most effective approach for breeding novel eucalypt varieties. The effective exploitation of heterosis is crucial for the success of hybrid breeding programs.\u003c/p\u003e \u003cp\u003eHeterosis has been widely applied in crops such as corn and rice. In the genetic improvement of new forest tree varieties, heterosis also has important value, for example, hybrids of Populus, Pinus, and Eucalypt often exhibit faster growth rates, higher wood yields, and stronger environmental adaptability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. China has achieved significant results in the research of eucalypt hybrid breeding and the development and application of new varieties, such as (\u003cem\u003eE. urophylla\u003c/em\u003e\u0026times;\u003cem\u003eE. grandis\u003c/em\u003e) hybrid exhibits significant growth advantages in the South China region [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, compared with crops, the molecular genetic mechanism of forest hybrid vigor is still relatively lagging behind, mainly due to the long generation cycle, complex genetic background, and lack of efficient molecular breeding technology system in forest [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Concurrently, eucalypt represents an introduced species within the Chinese context, characterized by a comparatively limited scope of genetic research. Furthermore, the challenges associated with establishing extensive hybrid populations have resulted in a paucity of reports elucidating the genetic underpinnings of heterosis in eucalypt at the omics level. Consequently, a comprehensive investigation into the genetic basis of heterosis in eucalypt is of paramount importance for expediting the selection of superior varieties and enhancing the productivity of artificial forests.\u003c/p\u003e \u003cp\u003eRegarding the genetic mechanism of heterosis, in addition to its classical theories of dominance hypothesis, overdominance hypothesis, and epistasis hypothesis, many studies have shown that it may be related to factors such as allele complementarity, gene expression regulation changes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], epigenetic modifications [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and metabolic pathway optimization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, with the development of high-throughput sequencing technology, transcriptome sequencing (RNA-seq) has become an important tool for studying heterosis, which can systematically analyze gene expression differences, alternative splicing, allele specific expression (ASE), and co expression networks between hybrid and parents.\u003c/p\u003e \u003cp\u003eIn forest trees, transcriptome analysis has been used to reveal key genes and pathways related to poplar heterosis, such as differential expression of auxin signaling, cell division, and secondary wall synthesis related genes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is still limited research on the molecular mechanisms of eucalypt heterosis, especially how the gene expression patterns of different hybrid combinations affect growth traits.\u003c/p\u003e \u003cp\u003eOur research team initiated an investigation into the formation mechanism of eucalypt growth heterosis, focusing on the interplay between parental combining ability, heterosis, and the additive and non-additive genetic architectures of growth traits across diverse parental combinations. Despite this, the genetic underpinnings of heterosis in eucalypt growth disparities remained unexplored at the genetic level. Consequently, building upon the established correlation between parental combining ability and hybrid growth heterosis, this study selected two hybrids exhibiting significant growth heterosis differences and identified their respective parents. Employing transcriptome sequencing, we analyzed the relationship between gene differential expression and heterosis across parental and hybrids, as well as in dominant and non-dominant combinations, to identify functional genes and pathways associated with growth difference in eucalypt. This approach aims to elucidate the genetic basis of eucalypt heterosis, thereby enriching the theoretical framework of eucalypt hybrid breeding and refining breeding strategies. Furthermore, the identification of candidate genes related to growth differences will provide valuable genetic information for molecular marker-assisted selection breeding in eucalypt.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e2.1 Experimental Design and Sampling\u003c/p\u003e\n\u003cp\u003eAccording to our previous research findings [9], the selection of genetic materials (Table 1) in this study was based on two principles: (1) The difference in heterosis is significant, which can be determined based on our previous research on the relationship between the combining ability of eucalyptus hybrid parents and heterosis; (2) The genetic relationship between the selected hybrid parents was similar, which could be achieved by having the same parents or having the same/similar tree species. Based on this, we selected the superior hybrid 18H167 (T15 \u0026times; U3423) and the growth inferior hybrid 19H74 (U3423 \u0026times; U6). For the selected genetic material, this study conducted transcriptome analysis between 5 comparison groups (a: H74 vs U6; b: H74 vs U3423; c: H167 vs H74; d: H167 vs T15; e: H167 vs U3423).\u003c/p\u003e\n\u003cp\u003eTable 1 Hybrids studied with significant difference in growth and their parents of eucalypts\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHybrids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e18H167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.75%;\"\u003e\n \u003cp\u003eexcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003eT15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u003cem\u003eE.tereticornis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003eU3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u003cem\u003eE.urophylla\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e19H74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.75%;\"\u003e\n \u003cp\u003einferior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.625%;\"\u003e\n \u003cp\u003eU3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9167%;\"\u003e\n \u003cp\u003e\u003cem\u003eE.urophylla\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003eU6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u003cem\u003eE.urophylla\u0026times;E.tereticornis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.2 RNA-seq\u003c/p\u003e\n\u003cp\u003e2.2.1 Quality control and sequence alignment\u003c/p\u003e\n\u003cp\u003eThe fragmented mRNA was used as a template to synthesize strand of cDNA. PCR library amplification was then performed, and finally, detection was carried out using the Illumina Novaseq X Plus. Reads were further filtered by fastp [10] (version 0.18.0). The parameters were as follows: 1) removing reads containing adapters; 2) removing reads containing more than 10% of unknown nucleotides(N); 3) removing low quality reads containing more than 50% of low quality (Q-value\u0026le;20) bases.\u003c/p\u003e\n\u003cp\u003eShort reads alignment tool Bowtie2 [11] (version 2.2.8) was used for mapping reads to ribosome RNA (rRNA) database. Paired-end clean reads were mapped to the reference genome using HISAT2 2.1.0 [12] (Figure S1). The mapped reads of each sample were assembled by using StringTie v1.3.1 [13, 14] (Figure S2). A FPKM (fragment per kilobase of transcript per million mapped reads) value was calculated by using RSEM [15] software.\u003c/p\u003e\n\u003cp\u003e2.2.2 Sample relationship analysis and enrichment analysis\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was performed with R package gmodels (http://www.rproject.org/). RNAs differential expression analysis was performed by DESeq2 [16] software between two different groups (and by edgeR [17] between two samples) (Table S1). The genes/transcripts with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change\u0026ge;2 were considered differentially expressed genes/transcripts. Significantly enriched Gene Ontology [18] database (http://www.geneontology.org/)\\KEGG [19] terms in DEGs were defined by hypergeometric test, comparing with the whole genome background. We performed gene set enrichment analysis using software GSEA and MSigDB [20] to identify whether a set of genes in specific GO terms\\KEGG pathways showed significant differences in two groups.\u003c/p\u003e\n\u003cp\u003e2.2.3 Splicing Variants and Mutation Calling\u003c/p\u003e\n\u003cp\u003eThe bcftools [21] was used for calling variants of transcripts, and ANNOVAR was used for SNP/InDel annotation. RNA editing refered to variants on the mRNA level : 1) Removing the low quality SNPs while calling SNP by bcftools. 2) Correcting the SNPs around InDel region. 3) Choosing non-overlapping SNPs in UTR and EXON region. 4) Choosing SNPs with reference reads\u0026gt;=2 and variate reads\u0026gt;=3. 5) Choosing SNPs with the mutation frequency between 0.1 and 0.9 [22, 23]. The software rMATS [24] (version 4.0.1) (http://rnaseq-mats.sourceforge.net/index.html) was used to identify alternative splicing events and analyze differential alternative splicing events between samples (Figure S3). Protein-Protein interaction network was identified using String v10 [25]. The network file was visualized using Cytoscape (v3.7.1) software. The Hisat2 was used in reconstruction of transcripts [26].\u003c/p\u003e\n\u003cp\u003e2.2.4 WGCNA (weighted gene co-expression network analysis)\u003c/p\u003e\n\u003cp\u003eCo-expression networks were constructed using WGCNA (v1.47) package in R [27]. Differential gene expression values were imported into WGCNA to construct coexpression modules using the automatic network construction function blockwiseModules. Genes with high Intramodular connectivity (K.in) and module correlation degree (MM) tended to be hub genes which might have important functions. Pearson correlation between each gene and growth trait data under the module was calculated, and gene significance value (GS) was obtained. Finally, GO and KEGG pathway enrichment analysis were conducted to analyze the biological functions of modules.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3 Determination of gene set related to growth differences in eucalypt\u003c/p\u003e\n\u003cp\u003eIt is generally believed that the results with | NES |\u0026gt;1, NOM p-val\u0026lt;0.05, and FDR q-val (Qvalue)\u0026lt;0.25 have the highest credibility. Therefore, the top 20 GO terms (GO enrichment) and pathways (KEGG enrichment) with the smallest Qvalue, as well as the top 20 pathways with the largest |NES| (GSEA enrichment), were selected. In order to improve the accuracy and efficiency of identifying the common differentially expressed gene pathways among the five comparison groups, we used Cross column duplicate filtering (Code 1) to determine the Common ID, and then used Q value search on demand (Code 2) and Merge with 3 decimal places retained (Code 3) to retain the 3 decimal places Qvalue, and merged the Common ID and Qvalue. In H167-H74, the enriched GO pathway gene set and the KEGG gene set corresponding to the same differential gene products (up-regulated proteins (enzymes) or metabolites) in the three KEGG pathways of the comparison groups were determined using Cross column duplicate filtering (Code 1), and the Hub gene set of WGCNA was defined as the preliminary target gene set for this study (Table 6). Subsequently, PPI (Protein Protein Interaction) network analysis was performed to determine the target gene set related to the heterosis of eucalyptus growth differences. Finally, use Delete duplicate values (Code 4) to remove duplicate genes and preserve unique values.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Preparation of preliminary data\u003c/p\u003e\n\u003cp\u003eThe proportion of clean reads was higher than 99.5% (Table S2), the proportion of Q20 and Q30 based quality values was higher than 95.34% (Table S3), and the proportion of unmapped reads was higher than 97.89% (Table S4). The sequencing quality of this study was reasonable. According to the alignment results of the Total_Mapped reads that could be located on the genome (Table S5), the proportion of sequencing reads aligned to exon regions for all samples was above 87.20% (Table S6), indicating a relatively complete gene annotation. Novel genes were genes that had not been included in the reference genome (Table S7). Generally, the proportion of known genes was higher than 64.19%, and the proportion of new genes was higher than 68.02% (Table S8), indicating a relatively complete reference genome. The proportion of genes with a coverage range of 80-100% was the highest, and the gene coverage of each sample was higher than 65.16% in this range (Figure S4). When the sequencing volume reached 30 reads (\u0026times;1000000), the growth rate of the gene numbers in the 5 samples tended to flatten (Figure S5), and the gene coverage and sample sequencing saturation met the standards.\u003c/p\u003e\n\u003cp\u003e3.2 Sample Relationship Analysis\u003c/p\u003e\n\u003cp\u003eBased on the tpm values of each gene (Table S9), the gene abundance was highest when log10 (tpm) was 1-2, and there was only one peak in gene abundance for each sample (Figure 1Aa). The gene expression levels of the five samples in this study were basically consistent (Figure 1Ab), which can basically excluded the influence of enrichment analysis on subsequent differentially expressed genes. The expression levels of the three parents remained relatively stable in the repetition (Figure 1B). The contribution of the first principal component to sample differences was higher, with H167 and H74 showing significant differences on PC2 (Figure 1Ba). This might indicate that variety differences dominate this major element. For the common parent U3423 of the two hybrids, H167 showed a higher correlation with it, and the correlation between H167 and the male parent U3423 was much higher than that with the female parent T15. Similarly, the correlation between H74 and the male parent U6 was much higher than its correlation with the female parent U3423. Therefore, we preliminarily speculated that the parent U3423 was more likely to inherit excellent growth traits to hybrid when producing hybrid, and the influence of the male parent on the heterosis of hybrid growth was greater.\u003c/p\u003e\n\u003cp\u003eThere were a maximum of 5157 differentially expressed genes between H167 and T15. There were 4460 differentially down-regulated genes between H74 and U3423 (Figure S6a), significantly higher than other comparison groups. This indicated that there were more differentially expressed genes between the female parent and the hybrid, so we might need to more accurately locate genes related to eucalypt growth differences through the differentially expressed genes between the male parent and the hybrid. There were many genes with significant differences between H74 and U6 (Figure S6b-6f), which was consistent with our preliminary speculation and might indicate that the influence of male parents on the growth and heterosis of hybrid was indeed more pronounced. The gene expression patterns in each comparison group in this study were similar, and the differences in expression levels were also significant (Figure S7). This indicated that it was feasible for us to select these 5 comparison groups to search for genes related to differences in eucalypt growth.\u003c/p\u003e\n\u003cp\u003e3.3 Enrichment analysis\u003c/p\u003e\n\u003cp\u003eComparing the common differentially expressed gene pathways with more pairs of comparison groups, the stronger the correlation between it and the growth differences of eucalypt. The priority of differential gene pathway selection was 5\u0026gt;4\u0026gt;3\u0026gt;2\u0026gt;1 (unit: pair of comparison group). When at the same priority, we first selected the comparison group with the highest likelihood of genes related to growth differences, namely H167-H74. Secondly, the two comparison groups we identified were two hybrids of the same variety, H167-U3423 and H74-U3423, which had significant differences in their growth traits. The likelihood of their common differential gene pathways being associated with growth differences was second highest, and they were highly correlated with growth heterosis. Finally, referring to the previous text, it was believed that the differential genes between the male parent and the hybrid had a stronger correlation with growth heterosis, namely H74-U6.\u003c/p\u003e\n\u003cp\u003eThrough GO enrichment analysis, we selected four pairs of differentially expressed gene pathways shared by the comparison group, namely H167-H74, H167-T15, H167-U3423, and H74-U3423, namely extracellular region (GO: 0005576), external encapsulating structure (GO: 0030312), and cell periphery (GO: 0071944). The Qvalue of the three GO pathways was 0.0\u0026lt;0.05. Through KEGG enrichment analysis, we found that there were three differentially expressed gene pathways shared by the four comparison groups, but the Qvalues of KO00909 and KO00061 were 0.10411 and 0.356548, respectively, which did not satisfy Qvalue \u0026le; 0.05. Therefore, one KEGG pathway Plant-parent interaction (KO04626) was selected, with Qvalue=0.091886\u0026lt;0.05 (Table 2, Figure 2; Figure S8-10). Figure 2 was a partial display of the enrichment circle diagram, and the complete enrichment circle diagram could be found in Figure S8.\u003c/p\u003e\n\u003cp\u003eIn addition, there was a common pathway Plant hormone signal transduction (KO04075) in the four comparison groups H167-T15, H167-U3423, H74-U3423, and H74-U6 between the hybrid and parents, with Qvalue=0.017821\u0026lt;0.05 (Table 2, Figure 2; Figure S8-10). These findings might suggest a discernible relationship between combining ability and heterosis. Similarly, for the GSEA-GO analysis results, we selected the large ribosomal subunit (GO: 0015934) (Figure 3a) and ribosomal subunit (GO: 0044391) (Figure 3b) of the common differentially expressed gene pathways in the four comparison groups, H167-H74, H167-T15, H167-U3423, and H74-U3423, both of which satisfied FDR q-val\u0026lt;0.25. However, for the GSEA-KEGG analysis results, the maximum number of comparison groups (5) only had one pathway Splicosome (KO03040) (Figure 3d) that satisfied FDR q-val\u0026lt;0.25. Therefore, we also chose H167-H74, H167-U3423, H74-U3423, H74-U6, these four pairs of comparison groups shared a differential gene pathway called Phenyalalanine, tyrosine and tryptophan biosynthesis (KO00400) (Figure 3c), Satisfied FDR q-val\u0026lt;0.25 (Table 3). Figure 3 was a partial display of the GSEA diagram, and the complete GSEA diagram could be found in Figure S12.\u003c/p\u003e\n\u003cp\u003eIn addition, we found that there were 8 common differentially expressed gene pathways Plant hormone signal transduction (KO04075) (Figure 2; Figure S8-10), Cutin, suberine and wax biosynthesis (KO00073), N-Glycan biosynthesis (KO00510), Various types of N-glycan biosynthesis (KO00513), Phenylpropanoid biosynthesis (KO00940), Flavonoid biosynthesis (KO00941), Ribosome biogenesis in eukaryotes (KO03008), Nucleocytoplasmic transport (KO03013) (Figure 3; Figure S12), FDR q-val\u0026lt;0.25 (Table 2-3) in the four comparison groups H167-T15, H167-U3423, H74-U3423, and H74-U6 between the hybrid and parents. These findings suggested a potential positive correlation between combining ability and heterosis, which aligned with our prior conclusions [9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 GO and KEGG Enrichment Analysis\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eEnrichment method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eComparison group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCommon ID (Qvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 22px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-T15,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0016301(0.000),GO:0016310(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0004672(0.000),GO:0016773(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0001101(0.000),GO:0010243(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0005524(0.007),GO:0008270(0.006),GO:0032555(0.006),\u003c/p\u003e\n \u003cp\u003eGO:0035639(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0005576(0.000),GO:0030312(0.000),GO:0071944(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0009698(0.000),GO:0009832(0.000),GO:0016020(0.000),\u003c/p\u003e\n \u003cp\u003eGO:0042546(0.000),GO:0071554(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eGO:0016772(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" style=\"width: 22px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00940(0.000),KO00941(0.042),KO01100(0.000),KO01110(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO04626(0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00909(0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO04075(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO04016(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00780(0.438)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO01040(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00400(0.001),KO00910(0.327)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00944(0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00062(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00360(0.222),KO00520(0.261),KO00900(0.050),KO00902(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00061(0.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00500(0.313),KO01212(0.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00052(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00511(1.000),KO00531(0.730)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 594px;\"\u003e\n \u003cp\u003eKO00010(0.216),KO00480(0.010),KO02010(0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBased on the enrichment analysis results of the three types mentioned above, we have defined 8 gene sets related to the heterosis of eucalypt growth differences (Table 6). For the three KEGG gene sets, in the Plant-parent interaction (KO04626) pathway diagram, we found that the four comparison groups shared the differential gene product CDPK, CaMCML,EDS1,MPK3/6,RPS2 (Table 4). The differences in RPS2 were consistent among the four comparison groups. CDPK and CaMCML showed significant up-regulation between hybrids, while both up-regulation and down-regulation were observed between hybrid and parents, might indicating that both were genetically related. EDS1 exhibited down-regulation between the male parent and the hybrid, while up-regulation was observed between the two hybrids. MPK3/6 demonstrated up-regulation between the dominant hybrid and the male parent, as well as between the two hybrids (Figure S11A), which further supported the potential for a greater male influence on the growth traits of eucalypt, as previously suggested. In Phenylalanine, tyrosine and tryptophan biosynthesis (KO00400) pathway diagram, we found that the only common differential gene product among the four comparison groups was 2.6.1.5 (enzyme EC number), namely TAT and ARO8 (Table 4). Except for up-regulation between the hybrid with growth disadvantage and the female parent, down-regulation was observed between the hybrid with growth advantage and the parents, as well as between the two hybrids (Figure S11B). In the Splicosome (KO03040) pathway diagram, we found that the only common differential gene product among the five comparison groups was the protein complex Lsm (Table 4), which was located in the U4/U6 complex. It was crucial that Lsm was both up-regulation and down-regulation between the growth advantage hybrid and the parents, only down-regulation between the growth disadvantaged hybrid and the parents, and up-regulation between the two hybrids. This might indicate a strong correlation between it and growth differences. Secondly, according to our priority principle mentioned earlier, the SF3a and eIFA3 proteins shared by the four comparison groups might also be particularly critical (Table 4), which were located in the U2 and EJC/TREX complexes, respectively (Figure S11C).\u003c/p\u003e\n\u003cp\u003eTable 3 Gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eGene set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eComparison group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eCommon ID (Qvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 5px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0000785(0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0004521(0.457),GO:0004540(0.864),GO:0015149(0.427),\u003c/p\u003e\n \u003cp\u003eGO:0015665(0.740),GO:0016893(0.478),GO:0043473(0.467),\u003c/p\u003e\n \u003cp\u003eGO:0043476(0.461),GO:0043478(0.468),GO:0043479(0.468),\u003c/p\u003e\n \u003cp\u003eGO:0043480(0.466),GO:1901618(0.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0004523(0.443),GO:0016891(0.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0034728(0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0005198(0.475),GO:0006220(0.161),GO:0006221(0.144),\u003c/p\u003e\n \u003cp\u003eGO:0015926(0.269),GO:0051168(0.444),GO:0072527(0.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0005618(0.016),GO:0031225(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0005840(0.179),GO:0072528(0.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0006364(0.072),GO:0008033(0.149),GO:0009451(0.060),\u003c/p\u003e\n \u003cp\u003eGO:0009698(0.022),GO:0009699(0.016),GO:0009808(0.001),\u003c/p\u003e\n \u003cp\u003eGO:0016298(0.013),GO:0016679(0.001),GO:0034470(0.068),\u003c/p\u003e\n \u003cp\u003eGO:0042254(0.057),GO:0046992(0.014),GO:0046993(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0009637(0.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0009832(0.000),GO:0016682(0.000),GO:0022613(0.157),GO:0042546(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0015934(0.166),GO:0044391(0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGO:0008375(0.135),GO:0015935(0.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"23\" style=\"width: 5px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00062(0.210),KO00330(0.433),KO00520(0.079),KO00945(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00073(0.110),KO00510(0.111),KO00513(0.134),KO00940(0.028),\u003c/p\u003e\n \u003cp\u003eKO00941(0.000),KO03008(0.016),KO03013(0.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00195(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00196(0.991),KO03410(1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00230(1.000),KO00350(0.923),KO00903(0.979),KO03050(0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00250(0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00400(0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00500(0.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00904(0.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00909(0.411),KO03450(0.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO03015(0.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO03022(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO03030(0.253),KO03040(0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00040(0.054),KO00900(0.270),KO03060(0.927),KO04141(0.437)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00052(0.560),KO00561(0.723),KO00860(0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00053(0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00300(1.000),KO00565(0.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00600(0.222),KO00630(0.410),KO00910(0.562),KO03020(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00770(0.132),KO00908(0.410),KO04145(0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO03010(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00360(0.036),KO00514(0.340),KO00950(0.421),KO00960(0.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00531(0.990),KO00563(0.807),KO00920(1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKO00902(0.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 The same differentially expressed gene products in the KEGG pathway\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eKEGG Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eComparison group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eCommon gene product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 30px;\"\u003e\n \u003cp\u003ePlant-pathogen interaction (KO04626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBAK1BKK1,CNGCs,FLS2,KCS1/10,PR1,Pti5,RIN4,RPM1,WRKY2533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eCDPK,CaMCML,EDS1,MPK3/6,RPS2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSGT1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 30px;\"\u003e\n \u003cp\u003ePhenylalanine, tyrosine and tryptophan biosynthesis (KO00400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e1.1.1.25,2.5.1.54,2.6.1.9,4.2.1.10,4.2.1.51,4.2.1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e2.6.1.1,4.2.1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e2.6.1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e2.7.1.71,4.1.3.27,5.4.99.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 30px;\"\u003e\n \u003cp\u003eSpliceosome (KO03040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eLsm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSF3a,eIFA3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-H74,H167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSnu66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423,H74-U6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eHSP73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-U3423,H74-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eTHOC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003eH167-T15,H167-U3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePRL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.4 Mutation analysis\u003c/p\u003e\n\u003cp\u003eThe trends of SNP and InDel statistics and RNA editing classification analysis for all samples were almost the same, and mutations would not significantly affect the screening of differentially expressed genes in the previous section (Figure S13). The RNA editing results of two biological replicates of H167 and H74 showed significant differences. H167 had a higher frequency at an editing ratio of 0.5, while H74 had a higher frequency at an editing ratio of 0.25. However, the editing ratio and frequency of H74-1 are similar to H167. And during the increasing editing ratio, there were significant differences in the RNA editing frequencies of the three biological replicates of H74. However, the RNA editing frequency of the other four samples remained relatively stable with the change of editing ratio in the three biological replicates, showing a normal distribution with a mode of 0.5 (Figure 4). This might indicate uncertain variations in the modification and processing of post transcriptional mature RNA molecules in growth disadvantaged hybrid. It might also indicate that dominant growth genes have genetic stability.\u003c/p\u003e\n\u003cp\u003e3.5 Alternative splicing analysis\u003c/p\u003e\n\u003cp\u003eTwo hybrids had more alternative splicing numbers than the parents, and the heterosis might come from AS (Figure S14). In JC only difference AS, the number of AS between H74 and U6 was generally the highest, and the number of AS of each type was also the highest. The number of AS between H167 and U3423 was generally the lowest (Figure S15A). This was consistent with our previous conclusion that growth traits might be more closely related to the male parent. However, in the JC+readsOnTarget differential AS, H167 vs H74 had the lowest overall number of AS (Figure 5b). Among the 5 comparison groups, the two most common event types were SE and RI. The comparison groups of H74 vs U6, H74 vs U3423, and H167 vs H74 had the highest number of SEs, followed by RI. The two comparison groups, H167 vs T15 and H167 vs U3423, had the highest number of RI, followed by SE (Figure 5). These might indicate that SE was likely related to growth disadvantage, while RI was more likely to be related to growth advantage. Only H167 vs H74 (Figure 5) are shown in the main text, and the rest can be found in the supplementary chart (Figure S15).\u003c/p\u003e\n\u003cp\u003e3.6 WGCNA analysis\u003c/p\u003e\n\u003cp\u003eBased on the five traits related to eucalypt growth, including HT, DBH, SUR, VOL, and SS, the tan module had the strongest correlation with growth. Except for the SS trait which showed a significant positive correlation with the module, all other traits showed a significant negative correlation (p\u0026lt;0.1). In addition, the grey60 module had a strong correlation with growth, and the positive and negative correlations between the five growth traits and grey60 were consistent with the tan module (p\u0026lt;=0.1). The skyblue module had the strongest and most significant negative correlation with HT among all modules (|Pearson correlation coefficient |=0.7 maximum; P=0.004 minimum) (Figure 6). It was worth mentioning that this was consistent with our previous research results [9], which suggested that the growth quality and straightness influenced by genes in the module might be exactly opposite.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the analysis of trait correlation, we defined |GS|\u0026gt;0.8, |K.within|\u0026gt;100 and |MM|\u0026gt;0.9 under the tan module were highly correlated with growth traits (Table 5). Three hub genes, MSTRG.35350，MSTRG.4104 and ncbi_104443483, were screened out (Table 6).\u003c/p\u003e\n\u003cp\u003eAnalysis revealed that all five growth traits were positively correlated to varying degrees with K. within and MM values (Figure S16). Especially for the two most critical growth traits of HT and DBH, the positive correlation between GS and K. within and MM values was the strongest, and the vast majority of genes were clustered near the fitting lines with the highest GS, K. within, and MM values (Figure S16a-b). This further demonstrated the important biological role of the tan module in the differential growth of eucalypt.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Growth trait related genes in the tan module\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eGS.pvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eK.within\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 12px;\"\u003e\n \u003cp\u003eHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104435039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.00027961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e63.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104433568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.00026183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e83.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eMSTRG.35350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000176975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e135.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eMSTRG.4104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000176975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e135.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104417067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000160364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e54.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104443483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.00012707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e103.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104432758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.83E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e70.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104448101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.06E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e68.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104438296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e74.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eMSTRG.16990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.05E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e59.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104419919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e54.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104435039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000219944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e63.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104449238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7.03E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eVOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_104449238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.000218769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003encbi_108959151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.77E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e51.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAnalysis found that all significantly enriched GO pathways in the tan module were biological processes (Figure S17a), which was different from our previous GO enrichment of all genes. The significant pathways enriched in KEGG were mostly related to Metabolism (Figure S17b). More importantly, the pathways significantly enriched in the tan module were highly consistent with our previous KEGG enrichment results, where Plant-pathogen interaction and Spliceosome were present in both enrichments. In addition, the most significant GO enrichment pathway captured in the tan module was response to endogenouous stimulus (Pvalue=2.2811E-10) (Figure S18a), the most significant GO enrichment pathway was Galactose metabolism (Qvalue=0.006965) (Figure S18b). We found significant up-regulation of proteins corresponding to 2.4.1.123 and 2.4.1.82 in the Galactose metabolism pathway (Figure S19). Finally, we screened three Hub genes nbci_104448375,nbci_104440165 and nbci_104434572, as well as one transcription factor (TF) nbci_104452186 (Figure S20) (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, we had implemented a screening process for genes related to growth differences in eucalypt from surface to line and then to point (Table 6). Finally, the target gene PPI network was used to further streamline the genes (H167-H74) associated with heterosis in eucalypt growth differences. We found no connectivity between Hub genes (Figure S21). Therefore, the gene with the highest average abundance and connectivity (abundance/connectivity=10) was identified from 5 GO pathways (Figure 22a-e) and 3 KEGG pathways (Figure 22f-h) as the final target gene for this study. Only the pathway with the most target genes was displayed in the main text (Figure 7). See supplementary charts for the rest (Figure 22).\u003c/p\u003e\n\u003cp\u003eTable 6 Genes related to growth differences in eucalypt\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eextracellular region (GO:0005576)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eexternal encapsulating structure (GO:0030312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003ecell periphery (GO:0071944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003elarge ribosomal subunit (GO:0015934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eribosomal subunit (GO:0044391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePlant-pathogen interaction (KO04626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003ePhenylalanine, tyrosine and tryptophan biosynthesis (KO00400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eSpliceosome (KO03040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eHub\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eMSTRG.14620, MSTRG.7374, ncbi_104414198, ncbi_104414737, ncbi_104418686, ncbi_104419239, ncbi_104419266, ncbi_104419267, ncbi_104421220, ncbi_104421331, ncbi_104422176, ncbi_104422221, ncbi_104423986, ncbi_104424336, ncbi_104424824, ncbi_104425290, ncbi_104429693, ncbi_104430354, ncbi_104431376, ncbi_104431697, ncbi_104433012, ncbi_104433102, ncbi_104433114, ncbi_104433143, ncbi_104433278, ncbi_104433548, ncbi_104434119, ncbi_104434971, ncbi_104436031, ncbi_104439389, ncbi_104440647, ncbi_104444321, ncbi_104445772, ncbi_104447974, ncbi_104448866, ncbi_104449401, ncbi_104453907, ncbi_104454445, ncbi_104456301, ncbi_104456417, ncbi_104456797, ncbi_104456805, ncbi_108960152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMSTRG.22406, MSTRG.2742, ncbi_104414130, ncbi_104414885, ncbi_104414996, ncbi_104415288, ncbi_104416283, ncbi_104418615, ncbi_104419209, ncbi_104419698, ncbi_104419919, ncbi_104422202, ncbi_104422289, ncbi_104422587, ncbi_104431265, ncbi_104432337, ncbi_104433339, ncbi_104433444, ncbi_104436406, ncbi_104437065, ncbi_104437692, ncbi_104437950, ncbi_104438301, ncbi_104440602, ncbi_104441483, ncbi_104441601, ncbi_104444833, ncbi_104447095, ncbi_104448843, ncbi_104450091, ncbi_104454132, ncbi_104454644, ncbi_104456905, ncbi_104457259, ncbi_104457277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMSTRG.1672, MSTRG.23128, MSTRG.34138, MSTRG.8582, ncbi_104421866, ncbi_104440695, ncbi_104440743, ncbi_104452223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003encbi_104422945, ncbi_104444802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eMSTRG.10089, MSTRG.35802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003encbi_104448874, ncbi_104429374, ncbi_104430019, ncbi_108956299, MSTRG.6764, MSTRG.7876, MSTRG.26157, ncbi_104448378, ncbi_104451496, ncbi_104432152, ncbi_104450866, ncbi_104451452, ncbi_104452309, ncbi_104452311, ncbi_104452312, ncbi_104451438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003encbi_104440906, ncbi_104440877, ncbi_104440927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003encbi_104453291, MSTRG.13227, MSTRG.8529, MSTRG.13017, ncbi_108954086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eMSTRG.35350, MSTRG.4104, ncbi_104443483, nbci_104448375, nbci_104440165, nbci_104434572, nbci_104452186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe up-regulation of extracellular region genes has been implicated in grape development [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the enhancement of egg quality and reproduction in aquaculture [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The external encapsulating structure primarily refers to the physical barrier surrounding cells or organisms, exemplified by cell walls. Currently, there is a paucity of specific reports linking this pathway to growth. However, we hypothesize that this enrichment pathway may modulate cell wall relaxation and facilitate cellular and organ growth by influencing cellulose, hemicellulose, and pectinase within plant cell walls. Furthermore, the cell periphery was associated with the cold tolerance of eucalyptus trees [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on the proteins localized within the KEGG pathway in this study, extracellular region may regulate growth mechanisms by transmitting growth factor signals to cell periphery membrane receptors, activating the MAPK/CDPK pathway, facilitating protein or metabolite transport, and mitigating environmental stress during the reproductive phase.\u003c/p\u003e \u003cp\u003eRibosomal subunits are the core machinery of protein synthesis, and their assembly, activity, and regulation directly affect cell proliferation, metabolic adaptation, and tissue development. The Cryphonectriaceae isolate identified from the large ribosomal subunit of eucalypt was associated with pathogenicity in two eucalypt hybrids [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Large subunit ribosomal DNA was commonly used for physiological activity research [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and identification of fungi [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although there is currently no research indicating a direct relationship between ribosomal subunit and crop or forest growth, based on the proteins located in the KEGG pathway in this study, large ribosomal subunit and ribosomal subunit may be quite related to the translation and assembly of RPS2, Lsm, SF3a, and elF3, and thus participate in the synthesis and genetic processes of eucalypt growth substances.\u003c/p\u003e \u003cp\u003eThe five GO pathways obtained through enrichment in this study were all cellular component that described genes. Cellular component was influenced by light conditions, which in turn affect the metabolism and developmental processed of rapeseed growth [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and were related to the molecular mechanisms of chicken growth [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. One pollen-specific protein (Cla001608) that was in cellular component etc, providing insight into the molecular basis of the developmental stages of male flowers in watermelon and may aid in dominant cross breeding [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The dynamic coordination of cellular components and the efficiency of growth signaling pathways may be key to regulating growth.\u003c/p\u003e \u003cp\u003eCompared with the KEGG enrichment analysis results of this study, previous research on plant-pathogen interaction, biosynthesis of three amino acids, and spliceosome related pathways has mostly been related to crop disease or stress resistance, with few studies related to growth traits. Studies have shown that plant plant interaction promotes the growth of rice [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and tobacco [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], providing new insights and theoretical foundations for their breeding. Plant-pathogen interaction was associated with the shedding of mature sugarcane leaves [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As the name suggests, plant-pathogen interaction was more related to disease resistance and participates in abiotic stress tolerance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], such as gummy stem blight (GSB) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and \u003cem\u003eP. brassicae\u003c/em\u003e infection under dry climate conditions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, it affected salt resistance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], drought resistance [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], detoxification pathways [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and might also affect the mechanisms of flowering time control and adaptive evolution in plants growing at high altitudes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Phenylalanine, tyrosine and tryptophan biosynthesis might be related to the mechanisms of NC against hypoxic stress [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. CircRNAs (non-coding RNAs) played roles on spliceosome, etc., showing the potential role involved in the abiotic stress response in tomato [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Study found some key genes like spliceosome pathway (13 miRNAs) were about the mechanism underlying grapevine responses to heat and drought stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, based on the pathway enrichment results of our transcriptome data, it is believed that these three pathways may be related to the heterosis of eucalypt growth, certain gene expression products in the pathway played an indispensable role.\u003c/p\u003e \u003cp\u003eTherefore, we focus on proteins and metabolites regulated by genes with significant upregulation and downregulation changes within the pathway. Calcium-dependent protein kinases (CDPKs), which were important sensors of Ca2\u0026thinsp;+\u0026thinsp;flux in plants, were known to play essential roles in plant development and adaptation to abiotic stresses [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have shown that PnCDPK1 was related to Japanese Morning Glory's (\u003cem\u003ePharbitis nil\u003c/em\u003e)\u0026rsquo;s germination, seedling growth [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and changed in fruit yield and dry matter production of tomato leaves [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It seemed that PnCDPK52 is associated with the germination process and PnCDPK56 with seedling growth [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Perhaps, PgCDPK1a was involved in ginseng growth, as a positive regulator [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. CaM-like protein CML is involved in plant growth, development, and stress adaptation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Both this study and the aforementioned research indicate that CDPK and CaMCML have important relationships with plant growth.\u003c/p\u003e \u003cp\u003eEDS1 is Enhanced Disease Susceptibility 1, which mainly plays a role in the innate immune response of plants and regulates seed yield [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Poplar EDS1 affected tree morphology, photosynthetic efficiency, ROS and SA metabolism, as well as leaf senescence [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. MPK usually refers to Mitogen Activated Protein Kinase, which plays an important role in cell signaling, especially in cell growth, differentiation, stress response, and other aspects. Studies have shown that, light signal transcription factors FHY3 and FAR1 could integrate light signals with immune signals by directly interacting with EDS1, widely regulating plant growth [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. EDS1 might provide a novel resistance mechanism for the sustainable management of rust diseases [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. According to Brown et al.'s study, MPK was associated with cold adaptation and frost resistance [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The MAPK signaling pathway in plants maintained the adaptability of \u003cem\u003ePopulus simonii\u003c/em\u003e \u0026times; \u003cem\u003ePopulus nigra\u003c/em\u003e to rapid growth by regulating the signal transduction of salt tolerant hormones [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Both this study and the aforementioned research indicate that EDS1 has an important relationship with plant growth.\u003c/p\u003e \u003cp\u003eRPS2 is an abbreviation for ribosomal protein S2, belonging to the S5P family of ribosomal proteins. It is a component of the 40S subunit and might be found in tropical L Plays an important role in biology [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. It was also related to the growth of maltose and glucose [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. A review put special focus on LSM rings' function in abiotic stress responses [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The mediator and Lsm complex synergistically controlled the growth regulation expression of ribosomal protein genes at the transcriptional and splicing levels [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. SF3a mRNA splicing complex was required for a robust innate immune response [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. There was research showing that eIF3 binding mRNA had a higher ribosome density in growing cells [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. These conclusions all indicated that they were all related to RNA metabolism, possibly related to the sustained expression of growth response factors and the improvement of translation efficiency.\u003c/p\u003e \u003cp\u003eUtilizing WGCNA, this study identified 19 gene co-expression modules significantly associated with growth traits. The tan module exhibited the most robust correlation with the target phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared to single-gene analysis, WGCNA elucidates synergistic regulatory patterns within gene modules, offering a systems-level perspective on the molecular mechanisms underlying complex traits. However, the connectivity of hub genes identified within the tan module via PPI network analysis was not strong. This may be attributed to the limited sample size, which could compromise module stability. Future studies should incorporate larger sample sizes to validate the generalizability of these hub genes. Furthermore, the extended breeding cycle of forest trees presents challenges in validating the regulatory effects on growth through functional experiments, such as hub gene knockout or overexpression. Nevertheless, we hypothesize that the upregulation of GOLS and K06617 proteins within the Galactose metabolism pathway, mediated by seven hub genes, influences growth quality (Figure S19).\u003c/p\u003e \u003cp\u003eConsequently, future investigations may integrate single-cell metabolomics, multidimensional omics, and gene editing technologies to elucidate the correlation between key proteins and energy metabolism, thereby unraveling their profound impact on the heterosis of Eucalyptus growth. This approach aims to elucidate the regulatory mechanisms governing the spatiotemporally specific interaction networks of complex proteins, ultimately providing a programmable molecular blueprint for the precise design of \"high-yield, stable\" intelligent hybrids.\u003c/p\u003e \u003cp\u003eAdvantageous growth genes demonstrate considerable genetic stability under selective pressure, coupled with distinct alternative splicing profiles. Splicing events (SE) are observed in the inheritance of growth disadvantage, whereas intron retention (RI) is the converse. Dominant genes may mediate genetic stability and phenotypic plasticity by suppressing single nucleotide polymorphism (SNP) mutations (preserving core function) while permitting regulated RI events (generating functional isoforms) (Figure S13; Figure S15). These findings suggest that shear regulation may function as a buffering mechanism for adaptive evolution. During the genetic process, functional loss mutations resulting from exon skipping (SE) can induce growth disadvantages, whereas intron retention (RI) events may facilitate the accumulation of advantageous mutations. This 'splicing-mediated mutation buffering' could be attributed to the elevated RI/SE ratio during the genetic expression of growth-dominant genes (Figure S15). Mutations arising from SE events may initiate nonsense-mediated mRNA decay, leading to energy expenditure and growth impairment; conversely, RI may generate regulatory non-coding RNAs or functionally acquired isoforms by incorporating intronic regulatory elements, thereby promoting environmental adaptation. This energy function trade-off, or key driving force, in shaping shear patterns within the environment, elucidates the macroscopic correlation between shear patterns and mutation adaptation. Consequently, scRNA sequencing and CRISPR technologies can be employed to investigate the spatiotemporal heterogeneity of shear variation at single-cell resolution, the regulatory roles of RI events in protein interaction networks, and the co-evolutionary relationship between shear regulation and epigenetic modifications (e.g., DNA methylation).\u003c/p\u003e \u003cp\u003eWe have depicted the classification and quantity of JC only differential AS using a bar chart (Figure S15A) and represented the classification and quantity of JC\u0026thinsp;+\u0026thinsp;readsOnTarget differential AS using a pie chart (Figure S15B). Surprisingly, the classification trend of JC\u0026thinsp;+\u0026thinsp;readsOnTarget differential AS aligns perfectly with that of JC only differential AS. However, the number of differential AS detected using JCEC values exceeded that detected using JC values across all five comparison groups. Notably, in H74 vs U6, H74 vs U3423, and H167 vs H74, the differences in SE and RI counts were less pronounced when compared to those detected using JC values (Figure S15). Although in most studies, utilizing JC\u0026thinsp;+\u0026thinsp;readsOnTarget for differential AS statistics appears to yield fewer events with higher confidence and clearer biological significance, in our study of heterosis in eucalypt growth differences, we may lean towards using the JC only method for differential AS classification and quantitative analysis. When solely exploring genes associated with growth traits, we might opt for JC\u0026thinsp;+\u0026thinsp;readsOnTarget to analyze the differential AS classification and quantity between two hybrids exhibiting significant growth differences. This approach would help narrow down the scope of alternative splicing events, facilitate verification or refined analysis, and ensure the reliability of the identified differential events.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research examined the mechanism underlying heterosis in eucalypt growth, identifying eight pathways and seven hub genes potentially associated with growth variations in eucalypt through enrichment and WGCNA analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eCorresponding author\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCorrespondence to\u003c/strong\u003e \u003cp\u003eWanhong Lu.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eEthics declarations\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003ethe National Key Research and Development Program of China, grant number 2022YFD2200203\u0026ndash;3\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLJZ is responsible for experimental design and afforestation; LY and LWH are responsible for controlling pollination and afforestation; LG and HAY are responsible for afforestation; SZY and LWH are responsible for data collection and investigation; SZY is responsible for data organization and analysis; SZY and LWH are responsible for writing manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the fund the National Key Research and Development Program of China, grant number 2022YFD2200203--3.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.The first author ([email protected]) has to be contacted in case of any queries or requirement of data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou XD, Wingfield MJ. 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Cellular translational enhancer elements that recruit eukaryotic initiation factor 3. RNA 2025; 31(2): 193-207. https://doi.org/10.1261/rna.080310.124\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Eucalypt hybrid, Heterosis, Enrichment analysis, WGCNA","lastPublishedDoi":"10.21203/rs.3.rs-6499800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6499800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fundamental way to solve the problems of single species, poor stress resistance, and low yield in Chinese eucalypt plantations is to scientifically utilize the heterosis of eucalypt and select new varieties with rich genotypes. In order to reveal the genetic mechanism of the formation of heterosis in eucalypt growth, based on previous research on the relationship between eucalypt heterosis and parental combining ability, we selected two artificial hybrids 18H167 (T15 × U3423) and 19H74 (U3423 × U6) with significant differences in birth length and similar parental relationships as the research objects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome analysis using RNA-seq technology showed that the correlation between gene expression levels indicated that the male parent had a greater impact on the heterosis of eucalypt growth. Based on GO and KEGG annotations, GSEA enrichment and WGCNA analysis identified 8 pathways and 7 Hub genes that may be related to growth differences in eucalypt. These candidate pathways are related to genes and ribosomal subunits, extracellular regulatory mechanisms, and three amino acid synthesis pathways. From their biological functions, the growth differences of eucalypt may be strongly correlated with their ability to adapt to environmental stress. AS analysis showed that the AS events of the two hybrids were significantly higher than those of their parents, with SE events possibly related to growth disadvantage and RI events more likely to be related to growth advantage. \u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides a more in-depth exploration of the formation mechanism of heterosis in eucalypt growth, which is expected to guide the selection of parents in eucalypt hybrid breeding. The discovery of candidate genes/pathways provides genetic information for eucalypt genome or molecular marker assisted selection breeding.\u003c/p\u003e","manuscriptTitle":"Exploring the genetic basis of heterosis in eucalypt growth based on transcriptome analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 10:12:34","doi":"10.21203/rs.3.rs-6499800/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T17:04:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T07:56:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140394705582067990722483607523882349860","date":"2025-06-03T04:19:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T10:59:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196703346009975015404956772678320868561","date":"2025-05-10T18:20:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86273703272755465123774558006749394514","date":"2025-05-06T07:55:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T12:38:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-02T04:19:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T01:34:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T01:31:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-04-22T03:08:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00025da9-1bf6-4fdf-a387-86d45c975c30","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:07:19+00:00","versionOfRecord":{"articleIdentity":"rs-6499800","link":"https://doi.org/10.1186/s12864-025-12077-9","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-10-03 15:58:01","publishedOnDateReadable":"October 3rd, 2025"},"versionCreatedAt":"2025-05-08 10:12:34","video":"","vorDoi":"10.1186/s12864-025-12077-9","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12077-9","workflowStages":[]},"version":"v1","identity":"rs-6499800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6499800","identity":"rs-6499800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}