Combining whole genome and transcriptome sequencing to analyze the pathogenic mechanism of Diplodia sapinea blight in Pinus sylvestris var. mongolica Litv

Ruiqi Wang; Yuting Wang; Sina Fu; Shixian Liao; Tingbo Jiang; Boru Zhou

doi:10.1080/21505594.2025.2490216

. 2025 Apr 13;16(1):2490216. doi: 10.1080/21505594.2025.2490216

Combining whole genome and transcriptome sequencing to analyze the pathogenic mechanism of Diplodia sapinea blight in Pinus sylvestris var. mongolica Litv.

Ruiqi Wang ¹, Yuting Wang ¹, Sina Fu ¹, Shixian Liao ¹, Tingbo Jiang ¹, Boru Zhou ^1,^✉

PMCID: PMC12005458 PMID: 40223234

ABSTRACT

Diplodia sapinea (= Sphaeropsis sapinea) is an opportunistic pathogen that usually lives in symbiosis (the coexistence of dissimilar organisms) with its host and can cause disease under extreme climatic or physiological stress. In this study, we generated a high-quality genome map of D. sapinea using PacBio Circular Consensus Sequencing (CCS) technology and analysed the key disease-causing genes of D. sapinea by RNA sequencing (RNA-seq). In the study, a number of cell wall degrading enzyme genes were identified to be up-regulated during pathogen infection, which may be involved in biotic stress response in P. sylvestris var. mongolica Litv. It was also found that the expression of antioxidant-related genes, such as those involved in carotenoid biosynthesis, ascorbate and glutathione metabolism, was up-regulated in the P. s. var. mongolica Litv. after fungus infection. Differently expressed genes (DEGs) -based protein-protein interaction (PPI) network was constructed that included 163 pairs of significantly positively correlated proteins, forming three highly interacting gene clusters, and the PPI network was predicted to be associated with the replication and propagation processes of the fungus. These results provide important information for understanding the pathogenic mechanisms of Diplodia tip blight and developing control strategies in P. s. var. mongolica Litv.

KEYWORDS: Diplodia sapinea, fungi, pathogenic, reactive oxygen species, genome sequencing

Introduction

Diplodia sapinea (Fr.) Fuckel (= Sphaeropsis sapinea Fr., Syst. mycol. (Lundae)), a member of the Botryosphaeriaceae, can cause Diplodia tip blight, also known as Sphaeropsis tip blight, in coniferous trees, particularly pine trees. Austrian pine (Pinus nigra J.F. Arnold), mountain pine (P. mugo Turra), and Scots pine (P. sylvestris L.) are among the susceptible species [1]. In recent years, it has been found that P. sylvestris var. mongolica Litv. (Pinales: Pinaceae), an excellent tree species for soil and water conservation, develops a large Diplodia tip blight infection in northeastern China [2]. D. sapinea is an opportunistic pathogen that normally lives in symbiosis with its host, often with outbreaks occurring during periods of physiological stress, extreme weather, or infestation [3]. Symbiosis, as originally defined by de Bary in 1879 [4], refers to the coexistence of dissimilar organisms. This term encompasses various types of close and long-term biological interactions between two different species, including mutualistic, commensalistic, and parasitic relationships. In the context of the P. s. var. mongolica Litv. - D. sapinea pathosystem, symbiosis could be understood as a dynamic interaction that can shift from an endophytic lifestyle to a parasitic phase or directly to a saprobic phase, depending on environmental conditions and host stress levels. The main site of infection of D. sapinea is the new shoots, which exhibit shoot dieback and browning, and Diplodia tip blight can cause severe disease and economic losses in conifers, therefore a deeper understanding of its causal mechanisms is needed to develop effective control strategies.

Broadly speaking, the life cycle of the fungus and the process of infection is very complex and not fully understood. According to the available information, the fungus first enters the plant in the form of spores and then forms adherent cells on the leaf surface that are able to penetrate into the plant cells and begin its life cycle of infection [5]. The fungus then penetrates deeper into the plant cell as hyphae and begins to multiply rapidly. This process requires the fungus to actively suppress the immune response in the plant's response and use nutrients from the host to meet its growth requirements. As the infection progresses, the fungus begins to extract nutrients from the plant tissues, causing the plant cells to die. Eventually, the dead plant cells cause the fungus to produce new spores, which are spread by physical means to begin a new cycle of infection [6]. Ghelardini et al. [7] concluded that the most important factor in the emergence of fungal diseases in forests is the cryptic and latent nature of the fungi. Diplodia sapinea possesses these characteristics, and it has been reported that it has been found as a saprotroph on cones [8] and as an asymptomatic endophyte in shoots of healthy Scots pine [9]. D. sapinea grows and reproduces more readily in warmer climates [10], and because of the endophytic stage of D. sapinea the accumulation of this pathogen can proceed unnoticed before disease outbreaks.

The invasion of plants by D. sapinea involves many biological processes, including cell differentiation, mycelium formation, immune suppression, immune escape, nutrient acquisition, and spore formation. Unlike invasion of animal cells, fungal invasion of plants also involves the process of breaking through the cell wall. The plant cell wall is an important barrier against biotic and abiotic stresses, and to gain access to plant cells, fungi develop highly specialized infection structures and a range of cell wall degrading enzymes such as pectinases, cellulolytic enzymes, protein hydrolases, and so on [11]. These cell wall degrading enzymes often cause significant damage to plants by degrading the polymers of the cell wall components. Degradation of plant cell walls by fungi is a necessary part of their invasion of the host and facilitates osmotic and nutrient uptake [12].

In contrast, during the long evolutionary process, plants have developed complex defence systems to defend themselves against invading pathogens, such as the rapid accumulation of reactive oxygen species (ROS) in plants after fungal invasion [13,14]. ROS are the first line of defence of plants against invading pathogens and play a key role in the plant immune system [15,16], which can both cause direct damage to pathogens and act as signalling molecules to induce a host defence response. Highly adapted pathogens have evolved counteracting mechanisms for ROS detoxification, such as Fusarium oxysporum Schltdl. neutralization of ROS in plants by Srpk1 deacetylation [17]. The adaptive response to host-derived ROS in Magnaporthe oryzae B.C. Couch is regulated by a self-balancing circuit centred on the MoOsm1 kinase [18]. In summary, evolution has established a long-term offensive-defensive relationship between pathogens and their hosts. An in-depth understanding of this information will not only shed light on the biology of pathogen, but also provide new ways to control the diseases it causes.

Genome and transcriptome sequencing technologies have been rapidly developed and popularized in recent years. In the field of microbiological research, a large number of studies have used these two technologies to analyse the mechanisms of fungal infection and stress response at the molecular level. For example, in citrus research, two pathogenic fungi, Phyllosticta citricarpa (McAlpine) Aa and Phyllosticta capitalensis Henn., have been identified and genome sequencing and comparative genomic analyses have revealed the different characteristics and protein expansions of their genomes [19]. Genomic and metabolomic sequencing analyses of an important oleaginous yeast, Rhodosporidiobolus odoratus (J.P. Samp., Á. Fonseca & E. Valério) Q.M. Wang, F.Y. Bai, M. Groenew. & Boekhout, helped elucidat its genomic features and the types of core metabolites [20]. Similarly, the genome of Phlebia radiata Fr. was generated using PACBio sequencing and revealed the lignocellulose degradation machinery of phlebioid fungi by comparative genomic analysis [21]. In the study of rice blast, a family of effectors of were revealed by transcriptome sequencing of Magnaporthe oryzae-infected plants [6]. The putative causative agent of Ascochyta rabiei (Pass.) Labr. in plants was identified by RNA-seq as well [22]. The interactions and potential links between oxidative stress and secondary metabolism have been elucidated by transcriptome and metabolome sequencing in studies of filamentous fungi [23]. The above studies highlight the role that genome and transcriptome sequencing technologies are playing in the field of microbiological research.

Genome sequence is a prerequisite for understanding the genetic background, and in this study, we first used PacBio Circular Consensus Sequencing (CCS) sequencing to obtain a high-quality genome of D. sapinea. Comparative genomic analyses were then performed to investigate species-specific and homologous genes between D. sapinea with its close relative Diplodia corticola A.J.L. Phillips, A. Alves & J. Luque and Diplodia seriata De Not. In addition, fungal samples of D. sapinea were collected before and after infection of P. s. var. mongolica Litv. seedlings, and key pathogenic genes of D. sapinea were analysed by RNA-seq in combination with genomic background information. This study aims to elucidate the pathogenic mechanism of D. sapinea infecting P. s. var. mongolica Litv. through genome assembly of D. sapinea and transcriptome sequencing before and after host infection. The high-quality genome sequence provided in this study is an important reference for tree disease research, microbiomics, and evolutionary dynamics.

Materials and methods

Isolation and culture conditions of Diplodia sapinea

Diseased leaves of P. s. var. mongolica Litv. were collected from the forest farm in Zhangwu County, Fuxin City, Liaoning Province, China (longitude: 122.530875, latitude: 42.385250). The samples were cut into 1 cm segments, soaked in 70% ethanol for 5 min, disinfected with 0.2% sodium hypochlorite solution for 10 min, rinsed with sterile water, and then placed in PDA medium [24] for dark incubation at 25°C. Firstly, step-out identification based on mycelial morphology type was carried out with reference to Bußkamp et al. [25]. After fungus growth had stabilized (Figure S1a), they were inoculated into liquid PDA medium for propagation. After one week of incubation, the fungal solution was centrifuged at low speed and 1–2 mg of tissue was collected and resuspended in 100 µl TE buffer. A microwave oven (600W) was then used twice for 1 minute each with a 30-second pause in between to disrupt the cells. Centrifuge tubes were allowed to cool at −20°C for 20 min, centrifuged at 10,000 rpm for 5 min, and the supernatant was collected and diluted 100-fold for use in polymerase chain reaction (PCR). Primer pairs for amplification of the ITS1, 5.8S and ITS2 regions were ITS1F/ITS4 or ITS1/ITS4 [26,27]. PCR amplification was performed on 5 μl of samples using KOD FX DNA Polymerase (TOYOBO, Japan). PCR products were separated by 1% agarose gel electrophoresis and purified using the Gel Extraction Kit (OMEGA, USA). Sequencing of purified products was entrusted to BGI Genomics (Shenzhen, China). The DNA sequences were aligned using MEGA6 [28] and submitted to GenBank (accession number, MG098333). After confirming that the sequence was highly homologous to Sphaeropsis sapinea isolate w×m23 internal transcribed spacer 1 (NCBI accession number, HM061312) by using MEGA6 to compare the sequencing results, the bacterial solution was mixed with 25% glycerol and stored at −80°C. Next, the fungus was again inoculated into PDA liquid medium for propagation and incubated at 25°C 130 r/min for one week, then fresh samples of the fungus were collected and transported at low temperature to Biomarker Biotechnology Ltd (Beijing, China) for genome sequencing. On the other hand, the above fungal solution was centrifuged, the supernatant removed and resuspended in sterile water for spraying plants.

P. sylvestris var. mongolica Litv. culture and fungal inoculation

Seeds of P. s. var. mongolica Litv. were harvested from Zhangwu County Forestry and planted in a constant temperature culture room at Northeast Forestry University, temperature: 26°C, 16/8 h light/dark cycle, and 60-day-old seedlings were used for fungal inoculation. Five small wounds were made on each pine needle (Figure S1b), followed by spraying with fungus solution as the infected (I) group and spraying with sterile water as the control. In addition, D. sapinea was inoculated and grown in PDA medium at 26°C under the same conditions as the I group, named CK group. After 10 days of treatment, the growth conditions of water treatment group and I groups are shown in Figure S1c and d, respectively. The pine needles and stem tips that had been sprayed with fungi were withered, while the P. s. var. mongolica Litv. that had been sprayed with water were in good growth condition. Infected pine needles and fungal were collected as I group samples, each sample made from a mixture of eight seedling with similar growth trends. Simultaneously, fungal mycelia were collected from PDA after 10 days as CK group samples. All the collected samples were immediately frozen in liquid nitrogen and then stored at − 80°C for RNA isolation.

DNA extraction, genome sequencing and assembly

Fungal genomic DNA was extracted using the TIANamp DNA kit (TIANGEN, Beijing, China) and whole-genome CCS sequencing was performed using the Pacbio Sequel II from Biomarker Biotechnology Ltd (Beijing, China). The procedure was as follows: disruption of DNA samples using g-TUBE; damage repair for disrupted DNA samples; end repair of DNA; connect dumbbell joints; digestion with exonuclease; screening of target fragments and generation of sequencing libraries. The CCS reads were assembled using Hifiasm software [29]. The Pilon software [30] was used to correct errors in the assembled genome. The completeness of genome assembly was evaluated using the BUSCO software (v3.0.1) [31] with the reference gene set of fungi_odb9 database.

Gene prediction and functional annotation

We use RepeatMasker v4.0.6 software [32] to predict repeat sequences. Gene structure prediction mainly used ab initio prediction, homologous protein prediction and transcriptome data prediction, and then integrates the three prediction results. Genscan [33], Augustus v2.4 [34], GlimmerHMM v3.0.4 [35], GeneID v1.4 [36], SNAP [37] were used for de novo prediction; GeMoMa v1.3.1 [38] was used for homologous protein prediction. rRNAs, tRNAs and other non-coding RNAs were identified in the genome using tRNAscan-SE [39] and Infernal [40] software. Functional annotation of the genes was then performed, and the predicted proteins were blast (e-value: 1e-5) against Nr [41], Swiss-Prot [42], TrEMBL [42], KEGG [43], KOG [44]. Blast2go [45] was used for GO annotation. HMMER [46] was used for Pfam annotation. Furthermore, pathogenicity was determined using BLAST against CAZy [47], TCDB [48], PHI [49], CYPED [50], DFVF [51] databases.

Comparative genomic analysis

Genomic data of D. corticola (PRJNA325745) and D. seriata (PRJNA350273), was downloaded from the NCBI Genome database. The software MCScanX [52] was used to identify the syntenic blocks between D. sapinea with D. corticola or D. seriata based on core-orthologous gene sets identified by BLASTP (e-value ≤1e-5). The KaKs Calculator (Ka: Nonsynonymous Substitution Rate; Ks: Synonymous Substitution Rate) of TBtools [53] software was used to calculate the substitution rates (Ka/Ks) for all of the pairwise comparisons of each single-copy orthologous gene using the free ratio model.

Transcriptome sequencing

The two sets of samples I group and CK group obtained in the previous period were sent to Biomarker Biotechnology Ltd (Beijing, China) for transcriptome sequencing. The libraries were constructed using the Illumina Truseq^TM RNA Sample Prep Kit method and most of the bases scored Q30 or above after high throughput output of a large amount of high-quality raw data. To ensure high quality reads were obtained, we remove low quality reads such as containing connectors. Clean reads were mapped to our obtained D. sapinea genome, and then the mapped reads were assembled and quantified using StringTie [54]. The FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) values of the genes were then calculated using RSEM (RNA-Seq by Expectation-Maximization) [55].

Identification and functional enrichment of differentially expressed genes

Differentially expressed genes (DEGs) were filtered with a threshold of |logFC| ≥ 1 and FDR ≤ 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and KEGG map enrichment analyses of DEGs were performed using OmicStudio online software [56], and the required gene annotation information was obtained from our obtained D. sapinea genome.

Construction of protein-protein interaction (PPI) networks

The amino acid sequences of all resulting proteins were uploaded to the STRING website [57] to construct the initial PPI network. Pearson correlation coefficients of all protein pairs were then calculated using SPSS and filtered with a threshold of R ≥ 0.9 and p ≤0.01 to filter out strongly significantly positively correlated protein pairs, which were visualized using Cytoscape software [58].

RT-qPCR analysis

Total RNA (isolated from RNA-Seq samples) was used for synthesizing first-strand cDNA (Hifair ® miRNA 1 st Strand cDNA Synthesis Kit, Yeasen, China). SGExcel FastSYBR Mixture (Sangon Biotech, China) was applied to identify genes expression patterns, and β-tubulin was used as endogenous reference gene. 2^−ΔΔCT relative quantification method was used to analyse the relative changes of gene expression. Standard errors and standard deviations were calculated from three replicates. All primers in this study have been listed in Table S12.

Results

De novo genome assembly

To obtain a de novo genome assembly of D. sapinea, we performed whole genome sequencing using PacBio CCS technology, which generated a total of 2.51 Gb of CCS reads. As shown in Figure 1, the final assembled genome size of D. sapinea was 37.8 Mb, including 88 contigs, with an N50 length of 2,421.1 kb, an N90 length of 653.0 kb, a longest contig length of 3,904,812 bp, a shortest contig length of 9,762 bp, and a GC content of 55.6%. Comparative analysis revealed that the genome size of D. sapinea is essentially the same as that of D. corticola and D. seriata, and the number of protein-coding genes is more similar to that of D. corticola (Table 1). The completeness assessment of the assembled genome by the BUSCO software (v3.0.1) resulted in the identification of 99.31% complete BUSCOs (duplicated: 0.69%; single copy: 98.62%) and 0.69% missing BUSCOs, indicating the relatively complete and accurate assembly of the D. sapinea genome.

Figure 1. — Genomic landscape of *Diplodia sapinea*. Only contigs with distribution of protein-coding genes are shown. C stands for contig. (a-b) Darker colors represent higher density or abundance. (c-d) The height of the line represents the density.

Table 1.

Genomic features of three kinds of fungus.

Statistic terms	D. sapinea	D. corticola	D. seriata
Genome assembly size (Mb)	37.8	35	37.3
Number of contigs	88	181	115
Contig N50 length (Mb)	2.4	0.45	2.9
Complete BUSCOs	99.31%	–	–
GC contents	55.60%	57%	57%
Protein-coding genes	10,755	10,831	8,050

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genome available information: D. sapinea (PRJNA1098348); D. corticola (PRJNA325745); D. seriata (PRJNA350273).

Annotation of the Diplodia sapinea genome

We predicted 10,755 genes in the D. sapinea genome using de novo prediction and homology search. And 67 rRNAs, 434 tRNAs and 186 other non-coding RNAs were identified in the genome. The 10,755 gene sequences obtained were then compared with public databases to generate gene function annotation results using BLASTP (e-value <1e⁻⁵). The results showed that 6,956, 3,213, 7,628, and 6,356 genes were annotated to GO, KEGG, Pfam and Swissprot databases, respectively (Table S1-S2). In addition, annotation of genes involved in carbohydrate degradation and utilization showed that 265 glycoside hydrolases (GHs), 87 glycosyl transferases (GTs), 21 polysaccharide lyases (PLs), 117 carbohydrate esterases (CEs) and 94 auxiliary activities (AAs) were present in the genome (Table S3) [47]. A total of 3,087 possible pathogenic, virulence, and effector protein genes have also been annotated using the Pathogen-host-interactions (PHI) database [49], suggesting that D. sapinea may interact with the host via these genes (Table S4). Compared to D. seriata (1, 120 pathogenic genes), D. sapinea has more pathogenic genes, suggesting that it may be more pathogenic to the host [59]. Finally, we predicted the number of all secreted proteins, transmembrane proteins and effectors, 717, 2249 and 67, respectively (Table S5).

Comparative genomics analysis

We infer an evolutionary relationship between D. sapinea and the other nine Botryosphaeriaceaes (Figure 2a). The results show that D. sapinea is closer to D. corticola and D. seriata, all three from the Diplodia genus. Subsequent homology comparisons of genes in the D. sapinea with D. corticola and D. seriata genomes were performed (Figure 2b). A total of 8,846 pairs of collinear genes were found between D. sapinea and D. corticola, and 7,292 pairs of collinear genes were found between D. sapinea and D. seriata, with a similar number of collinear genes obtained. All three fungi are pathogenic to plants, suggesting that these collinear genes may play a role in the infection process. We also calculated Ks and Ka values for each pair of collinear genes to investigate the cross-species evolutionary dynamics of these genes. Among the collinear genes of D. sapinea and D. corticola, there were 2,974 pairs of Ka/Ks values less than 1, accounting for 33.6% of all collinear genes in both. Among the collinear genes of D. sapinea and D. seriata, there were 3,030 pairs of Ka/Ks values less than 1, accounting for 41.6% of all collinear genes in both. These gene pairs with Ka/Ks values less than 1 have undergone strong purifying selection during evolution, and the three species are very closely related in terms of gene pair ratios.

Transcriptome sequencing and identification of differentially expressed genes (DEGs)

Transcriptome sequencing yielded an average of 43,255,520 and 41,280,931 clean reads for the CK and I samples, respectively. The reads mapped to an average of 9,989 and 9,728 genes for the CK and I samples, with a comparison rate of 92.87% and 90.45%, respectively, indicating that the quality of the sequencing was high (Table S6). A total of 4,778 genes were significantly differentially expressed after infecting, of which 2,975 were significantly up-regulated and 1,803 were significantly down-regulated (Figure 3a and Table S7). To validate the RNA-seq data, we selected 10 DEGs for RT-qPCR validation. The results showed that the RNA-seq results were highly consistent with the RT-qPCR results (Figure S2), indicating that the sequencing results were credible. In our results, a total of 83 differentially expressed transcription factors (TFs) were identified from 22 TF families, with the highest number of TFs from the Zinc finger-C2H2 type family (24 zf-C2H2s), followed by the Zinc finger and BTB domain-containing protein family (10 ZBTBs) and the v-myb avian myeloblastosis viral oncogene homolog (8 MYBs) and basic helix-loop-helix (8 bHLHs) families (Figure 3b). The expression pattern of the top 4 most abundant TF families showed that only 1 TF in the zf-C2H2 family was significantly up-regulated after infection, while the other 23 were significantly down-regulated (Figure 3c). In the ZBTB family, the expression levels of six TFs were significantly up-regulated and four were significantly down-regulated (Figure 3c). Three members of the MYB family were significantly up-regulated and five were significantly down-regulated (Figure 3c). Two TFs in the bHLH family were significantly up-regulated and six were significantly down-regulated (Figure 3c).

Figure 3. — Analysis of differentially expressed genes (DEGs) and transcription factors (TFs). (a) Number of DEGs. (b) Number of differentially expressed TFs. (c) Expression patterns of zf-C2H2, ZBTB, MYB and bHLH family members of fungi before and after infection. Heatmap colours represent the average of gene expression across multiple samples.

Functional enrichment of differentially expressed genes

GO and KEGG enrichment analysis and Nr annotation (Non-redundant protein sequence database) was performed to explore the function of these DEGs (Tables S7–S9). The results show that GO terms are enriched in three categories: biological process, cellular component, and molecular function. In the biological process category, the top three GO terms with the highest abundance were obsolete oxidation-reduction process, organonitrogen compound biosynthetic process, and small molecule metabolic process (Table S8). In the cellular component category, the top three GO terms with the highest abundance were ribonucleoprotein complex, ribosome, and ribosomal subunit (Table S8). In the molecular function category, catalytic activity, hydrolase activity, and oxidoreductase activity were the top three GO terms with the highest gene abundance (Table S8). In addition, the key GO terms associated with fungal attack on the host were extracted, such as cellulose binding and cellulase activity, which may be involved in the digestion of host cellulose by D. sapinea (Figure 4a). GO terms hydrolase activity and monooxygenase activity possibly involved in the degradation of plant cell walls by the fungus were also enriched (Figure 4a). It was also found to be enriched in a number of biological processes related to the catabolism of monosaccharides or polysaccharides, such as polysaccharide catabolic process, response to monosaccharide and glucan catabolic process, which may be related to further digestion of the host by the fungus (Figure 4a). Notably, these DEGs were also significantly enriched in response to oxygen-containing compounds, and may be involved in the escape effect of the fungus on the host immune system (Figure 4a).

Figure 4. — Differentially expressed genes GO and KEGG enrichment analysis. (a) Key GO terms associated with fungal pathogenicity processes. (b) Critical pathways associated with fungal pathogenic processes.

On the other hand, KEGG enrichment analyses indicate that multiple pathways are involved in the pathogenic process (Table S9). For example, several pathways involved in ROS scavenging processes, such as carotenoid biosynthesis, ascorbate and aldarate metabolism, and glutathione metabolism, which may be related to fungal resistance/escape from ROS, were enriched (Figure 4b). Pathways associated with fungal growth and reproduction were also enriched, such as meiosis – yeast, cell cycle – yeast, mitophagy – yeast, and cell growth and death, suggesting that D. sapinea undergoes plausible growth and reproduction in hosts (Figure 4b). As scape from oxidative stress is critical for fungal survival, the ROS scavenging/escape-related pathways were analysed in detail.

The expression levels of most genes in the carotenoid biosynthesis, ascorbate and aldarate metabolism, and glutathione metabolism pathways were significantly up-regulated (Figure 5a,b,d). Among them, thirteen genes were significantly differentially expressed during glutathione metabolism. GST was able to promote the conversion of glutathione (GSH) to R-S-glutathione, with three significantly up-regulated and two significantly down-regulated expressions. The significantly up-regulated expression of DUG was able to promote the conversion of L-cysteinyl-glycine and R-S-cysteinyl-glycine to L-cysteine and R-S-cysteine, respectively. Significant up-regulated expression of two OPLAH genes, Sphaeropsis0G098300 and Sphaeropsis0G044290, was able to promote the conversion of 5-oxoproline to L-glutamate. The differential expression of GCLC may affect the conversion process of L-cysteine and L-glutamate to L-γ-glutamylcysteine. Significant up-regulation of two ggt genes, Sphaeropsis0G000250 and Sphaeropsis0G105030 May 2001 promote the conversion of L-amino acid and glutathione (GSH) to L-γ-glutamyl-L-amino acid and L-cysteinyl-glycine, respectively, as well as the conversion of GSH to L-glutamate and R-S-glutathione to R-S-cysteinyl-glycine.

There were 23 peroxisome-related genes differentially expressed after D. sapinea colonization of the host (Figure 5c), of which PEX3,5,6,7 and 19, which are associated with peroxisome biogenesis, were significantly up-regulated. HPCL2, AMACR (Sphaeropsis0G064900), ACOX, SCPX, ACAA1, PDCR, ECH, PEC1 and CRAT were significantly up-regulated and AMACR (Sphaeropsis0G064910) was significantly down-regulated, suggesting that the process of fatty acid oxidation may be enhanced after host infection. In the antioxidant system, CAT (Sphaeropsis0G050820) and SOD (Sphaeropsis0G100690) were significantly differentially expressed; XDH was significantly up-regulated during purine metabolism; MVK was significantly up-regulated during sterol precursor biosynthesis; AGT and HAO were significantly up-regulated during amino acid metabolism. In total, 21 out of 23 DEGs were significantly up-regulated (Figure 5c).

Analysis of DEGs encoding plant cell wall degrading enzymes (CWDEs)

Combined with the results of Nr, Swissprot, and Carbohydrate-Active enzyme (CAZyme) annotation of all genes of D. sapinea, the CWDEs of D. sapinea after infection of host were identified and classified into four categories according to substrate type (Table 2). There were 9 enzymes (3 cellobiose dehydrogenases, 3 probable 1,4-beta-D-glucan cellobiohydrolases, 1 putative fungal cellulose binding domain protein, 1 cellulose-binding domain fungal, 1 cellulose growth-specific protein) identified using cellulose as a substrate, of which 8 were significantly up-regulated and 1 was significantly down-regulated after infection. The highest number of enzymes capable of degrading hemicellulose was 36, with 20 showing significant up-regulation and 16 showing significant down-regulation. Fifteen enzymes were identified as potential lignin degraders, with eight showing significant up-regulation and seven showing significant down-regulation. Additionally, thirteen enzymes were found to have a possible catabolic role for pectin, all of which were up-regulated.

Table 2.

Enzymes associated with plant cell wall degradation.

Substrate	Gene ID	Description	log₂(FC)
Cellulose	Sphaeropsis0G098860	Cellobiose dehydrogenase	8.60
	Sphaeropsis0G061270	Probable 1,4-beta-D-glucan cellobiohydrolase	8.32
	Sphaeropsis0G007440	Putative fungal cellulose binding domain protein	8.21
	Sphaeropsis0G023710	Cellulose-binding domain fungal	7.90
	Sphaeropsis0G033520	Probable 1,4-beta-D-glucan cellobiohydrolase	7.18
	Sphaeropsis0G090130	Probable 1,4-beta-D-glucan cellobiohydrolase	6.21
	Sphaeropsis0G106850	Cellulose growth specific protein	4.42
	Sphaeropsis0G062580	Cellobiose dehydrogenase	4.16
	Sphaeropsis0G099990	Cellobiose dehydrogenase	−2.07
Hemicellulose	Sphaeropsis0G045860	Putative extracellular cell wall glucanase	9.74
	Sphaeropsis0G033560	Endoglucanase	9.12
	Sphaeropsis0G068740	Endoglucanase	8.92
	Sphaeropsis0G065310	Probable endo-beta-1,4-glucanase	8.50
	Sphaeropsis0G067170	Endoglucanase	7.75
	Sphaeropsis0G013980	Endoglucanase	7.30
	Sphaeropsis0G105750	Probable endo-beta-1,4-glucanase	7.28
	Sphaeropsis0G079770	Glucan endo-1,3-alpha-glucosidase	6.19
	Sphaeropsis0G070390	Probable endo-beta-1,4-glucanase	6.14
	Sphaeropsis0G015690	Endoglucanase	5.93
	Sphaeropsis0G019840	Glucan endo-1,3-alpha-glucosidase	4.76
	Sphaeropsis0G001290	Probable endo-beta-1,4-glucanase	4.69
	Sphaeropsis0G081250	1,3-beta-glucanosyltransferase	4.55
	Sphaeropsis0G041050	1,3-beta-glucanosyltransferase	4.13
	Sphaeropsis0G046800	Putative endoglucanase type K	3.67
	Sphaeropsis0G036860	Probable endo-1,3(4)-beta-glucanase	3.66
	Sphaeropsis0G036640	Probable endo-beta-1,4-glucanase	3.05
	Sphaeropsis0G038980	Probable endo-beta-1,4-glucanase	2.95
	Sphaeropsis0G062570	Probable endo-beta-1,4-glucanase	2.79
	Sphaeropsis0G073570	Probable endo-beta-1,4-glucanase	2.58
	Sphaeropsis0G036480	1,3-beta-glucanosyltransferase	−1.16
	Sphaeropsis0G082910	Endoglucanase	−1.49
	Sphaeropsis0G036490	1,3-beta-glucan synthase component	−1.58
	Sphaeropsis0G005840	Dextranase	−1.64
	Sphaeropsis0G044040	Glucan endo-1,3-alpha-glucosidase	−1.82
	Sphaeropsis0G006870	Glucan endo-1,3-alpha-glucosidase	−1.99
	Sphaeropsis0G025550	Probable endo-1,3(4)-beta-glucanase	−2.06
	Sphaeropsis0G032330	Probable glucan 1,3-beta-glucosidase	−2.12
	Sphaeropsis0G090830	Glucoamylase	−2.14
	Sphaeropsis0G083210	Probable endo-1,3(4)-beta-glucanase	−3.10
	Sphaeropsis0G078570	Probable glucan endo-1,3-beta-glucosidase	−3.33
	Sphaeropsis0G093360	Putative cell wall glucanase protein	−3.38
	Sphaeropsis0G098020	Cell wall alpha-1,3-glucan synthase	−3.50
	Sphaeropsis0G022010	Glucan endo-1,3-alpha-glucosidase	−4.25
	Sphaeropsis0G106090	Probable glucan endo-1,3-beta-glucosidase	−4.26
	Sphaeropsis0G054530	1,3-beta-glucanosyltransferase	−5.91
Lignin	Sphaeropsis0G022110	Endo-1,4-beta-xylanase	8.97
	Sphaeropsis0G103580	Endo-1,4-beta-xylanase	7.44
	Sphaeropsis0G033930	Endo-1,4-beta-xylanase	6.18
	Sphaeropsis0G100360	Endo-1,4-beta-xylanase	4.99
	Sphaeropsis0G092640	Xyloglucan-specific endo-beta-1,4-glucanase	3.97
	Sphaeropsis0G034460	Probable xyloglucan-specific endo-beta-1,4-glucanase	3.94
	Sphaeropsis0G052540	Probable xyloglucan-specific endo-beta-1,4-glucanase	3.24
	Sphaeropsis0G033670	Laccase	2.39
	Sphaeropsis0G003150	Xyloglucan-specific endo-beta-1,4-glucanase	−1.13
	Sphaeropsis0G042840	Iron transport multicopper oxidase	−1.16
	Sphaeropsis0G080120	Fungal lignin peroxidase	−1.40
	Sphaeropsis0G013000	Putative glucooligosaccharide oxidase protein	−1.83
	Sphaeropsis0G010300	Laccase	−1.98
	Sphaeropsis0G031070	Laccase	−2.14
	Sphaeropsis0G055430	Fungal lignin peroxidase	−3.00
Pectin	Sphaeropsis0G081280	Probable pectate lyase	10.94
	Sphaeropsis0G049390	Probable pectate lyase	8.15
	Sphaeropsis0G084260	Pectinesterase	7.98
	Sphaeropsis0G029970	Probable pectate lyase	7.91
	Sphaeropsis0G079400	Probable pectinesterase	5.10
	Sphaeropsis0G107440	Probable pectate lyase	4.98
	Sphaeropsis0G081500	Probable pectate lyase	4.50
	Sphaeropsis0G043790	Pectinesterase	4.22
	Sphaeropsis0G015340	Probable pectate lyase	3.35
	Sphaeropsis0G031780	Probable pectate lyase	2.78
	Sphaeropsis0G106790	Probable pectate lyase	2.59
	Sphaeropsis0G004310	Pectin lyase fold/virulence factor	2.47
	Sphaeropsis0G038180	Probable pectate lyase	2.40

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Construction of a protein-protein interaction (PPI) network

A PPI network was constructed to investigate which proteins of D. sapinea function by interacting with each other during the infection process (Figure 6). A total of 163 significantly positively correlated protein pairs were obtained (Figure 6a), which included 21 transcription factors and 122 structural genes, forming three highly interacting gene clusters (Figure 6b and Table S10). Among them, transcription factors with very high connectivity play the core of the network and gene clusters, such as TF6 (zf-C2H2), TF8 (zf-C2H2), TF12 (Serum Response Factor, SRF), TF17 (High Mobility Group A, HMGA) and TF20 (Cysteine-Serine-Rich Nuclear Protein, CSRNPN). We then performed GO enrichment analysis on all genes in the PPI network, and the results showed that a total of 201 biological processes were significantly enriched, among which those related to the fungal invasion mechanism were extracted for gene-GO term network mapping. These biological processes were classified into five categories, namely, cell cycle, DNA replication, spore formation, reproduction, and response to stimuli/stress (Figure 7, Table S11). The network contained 6 transcription factors and 43 structural genes that are likely to be involved in the functions of fungal reproduction and immune escape during infection.

Figure 6. — Construction of a protein-protein interaction (PPI) network in the infection process of *Diplodia sapinea*. (a) Correlation coefficients were calculated between genes. Only gene pairs with R ≥ 0.9 were shown, * indicates p ≤ 0.01. (b) PPI network constructed by combining the correlation between genes and the STRING prediction results. Node size represents connectivity. Cyan nodes indicate transcription factors (TFs) and grey nodes indicate structural genes. View gene IDs in Table S9.

Figure 7. — GO term network of key genes in the PPI network. Blue nodes represent transcription factors, green nodes represent structural genes, and pink nodes represent GO terms. (I) Represent cell cycle-related GO terms, (II) represent DNA replication-related GO terms, (III) represent sporulation-related GO terms, (IV) represent reproduction-related GO terms, and (V) represent stimuli/stress response-related GO terms. Significant enrichment occurred for all displayed GO terms (p ≤0.05). View gene IDs in Table S9.

Discussion

D. sapinea is a serious pathogen of pines, and the mechanisms by which the fungus colonizes plant tissue and causes disease are still poorly understood [1,2]. To elucidate the pathogenic mechanism of the fungus, we first performed whole-genome sequencing and obtained a high-quality genome of 37.8 Mb, which was annotated. Comparative genomic analyses revealed a large number of collinear genes in D. sapinea with the genomes of D. corticola and D. seriata (8846 and 7292, respectively), of which D. seriata has been reported to be able to cause Botryosphaeria dieback [60]. The collinear genes we obtained are likely to be key genes in the induction of tip blight by these two fungi, and the exact function remains to be verified.

Transcription factors that may play the pathogenic factors

Transcriptome sequencing analysis revealed that a total of 83 TFs were differentially expressed in D. sapinea after infection, of which 24 belonged to the zf-C2H2 family, one member of which, ZFH1, has already been reported to be involved in the development of the nucleus and ascospores, and also to influence mycelial growth in Sclerotinia sclerotiorum (Lib.) de Bary [61]. Ten TFs belonged to the ZBTB family members, among which ZBTB25 was reported to be able to associate with the HDAC1-Sin3a complex in Mycobacterium tuberculosis (Zopf) – infected macrophages, possibly inducing cellular autophagy [62]. We also found that eight MYBs and eight bHLHs were differentially expressed, of which the MYB family, a family of proteins widely distributed in vertebrates, plants, and fungi [63], and its member MYT3 have been reported to be able to affect fungal development and pathogenicity in Fusarium graminearum Schwabe [64]. Fusarium oxysporum f. sp. cubense Tropical Race 4 showed that MaEFM-like (MYB member) was able to induce cell death in tobacco leaves [65]. The bHLH family member Ecdysone Receptor (EcdR) has been reported to be involved in the control of conidia production, pigmentation, and virulence in Aspergillus fumigatus Fresenius [66]. SREBP (Sterol Regulatory Element-Binding Protein, bHLH member) has been reported to regulate triterpenoid and lipid metabolism in the medicinal mushroom Ganoderma lingzhi S.H. Wu, Y. Cao & Y.C. Dai [67]. The differentially expressed TFs we identified in D. sapinea are likely to perform similar functions during infection and can be proposed as key g regulators for pathogenesis.

Genes that may help fungi cross host ROS defensive line

GO and KEGG enrichment analyses of the DEGs identified several terms or pathways associated with ROS scavenging or oxidative stress response, such as response to oxygen-containing compounds, carotenoid biosynthesis, glutathione metabolism, etc. We then visualized these ROS scavenging-related DEGs in metabolic pathways and found that significant differential expression of AL1 and carD occurred in the carotenoid (antioxidant) biosynthesis pathway [68–70], affecting the expression of phytoene and β-apo-4’-carotenal towards 3,4-didehydro lycopene and neurosporaxanthin, respectively (Figure 5a). Ascorbate is an important antioxidant in organisms, and we found that the expression of MIOX and ALDH was significantly up-regulated in the ascorbate and aldarate pathway after infection, promoting the conversion of myo-inositol and D-glucarate to D-glucuronate and D-glucurono-lactone, respectively (Figure 5b). It was also found that the expression level of RGN was also significantly up-regulated (Figure 5b), which facilitated the interconversion process of L-gulonate and D-glucurono-lactone [71–73].

Peroxisomes constitute a crucial component of the fungal cell’s repertoire, participating in cellular lipid homoeostasis, reactive oxygen metabolism, and the synthesis of secondary metabolites [74–76]. A total of 23 peroxisome-related genes were differentially expressed, 21 of which were significantly up-regulated (Figure 5c). This suggests that ROS defence is formed after the host was attacked by the fungus and that the peroxisome in D. sapinea is activated to defend against ROS. In the glutathione metabolic pathway, 10 genes were significantly up-regulated, implying that that glutathione metabolism is activated in D. sapinea after infection, possibly related to ROS stimulation. Overall, the fungus, after infecting hosts, may cause the host to produce large amounts of ROS at the site of inflammation, triggering an oxidative response of the fungus. And these antioxidant pathways associated DEGs may be involved in the fungal resistance/escape process in oxidative stress.

Plant cell wall degrading enzymes that may be involved in pathogenesis

The plant cell wall (PCW) is a line of defence against fungal invasion and inhibits fungal growth [77]. From the fungal point of view, the PCW provides nutrients to the fungus although it prevents its invasion. Modification of the PCW is therefore an important part of fungal colonization of the host [78–80]. Among the cellulose-degrading enzymes we identified, cellobiose dehydrogenase (CDH) is an extracellular fungal oxidoreductase that has been reported to be involved in plant biomass degradation processes [81]. There were a large number of endo-beta-1,4-glucanases (EGs) we identified in hemicellulose-degrading enzymes, which degrade cellulose by attacking the amorphous regions to produce more accessible new free chain ends for the action of cellobiohydrolases [82]. On the other hand, eight Endo-1,4-beta-xylanases were identified as being able to potentially degrade lignin, and specificity of Endo-1,4-beta-xylanase for insoluble xylan degradation has been reported in studies of Caldicellulosiruptor lactoaceticus [83]. Overall, the significant differential expression that occurred in most of the CWDEs suggests that they were activated by D. sapinea to degrade the cell wall of host to obtain nutrients and to break through the cell wall to enter the cell once inside the host.

Key genes involved in reproduction after entering cell in Diplodia sapinea

In the biological field, the construction and analysis of protein-protein interaction networks can reveal potential biological information [84,85]. There are 21 transcription factors and 122 structural genes in our PPI network. A total of 6 TFs and 43 structural genes were captured by the fungal pathogenicity related GO terms (Figure 7). Among the transcription factors TF3 (zf-GATA), TF4 (zf-CCCH), TF8/TF9 (zf-C2H2), TF17 (HMGA) and TF21 (bHLH), the most highly connected was TF17, which was significantly up-regulated after infection and whose family has been widely reported to be involved in cell migration, cell differentiation and senescence [86,87]. TF8 also has high connectivity and both it and TF9 belong to the zf-C2H2 family, members of which may be associated with mycelial growth [61]. TF3 from the GATA family is likely to play an important role in directing developmental genetic programmers and cell differentiation and is conserved in animals, plants and fungi [88]. Members of the CCCH family have been reported to be associated with the cell cycle and DNA replication in plant and animal species [89,90], and TF4 from this family was enriched for the cell cycle, with a significant up-regulation of its expression, which may be involved in the pathogenic process of D. sapinea (Figure 7, Tables S7 and S10). In addition to transcription factors, structural genes in the network were enriched in GO terms associated with fungal pathogenesis and these genes, although not yet reported, may have potential pathogenic functions that remain to be verified.

Host-pathogen interactions during infection

From the point of view of D. sapinea, the pathogenic process mainly involves the crossing of the ROS and cell wall defence. From the point of view of the host, P. sylvestris L., studies have shown that the plant defence mechanism corresponds to the different stages of the disease. Hu et al. showed that seven days after P. sylvestris L. infection of D. sapinea, the H₂O₂ content of the plant increased significantly [91], indicating that the ROS defence was established after the plant was infected. While ROS also cause damage to plant cells, the plant own antioxidant system is also activated, leading to the activation of many transcriptional and metabolic pathways of phytohormones and antioxidant alkaloids, such as the glutathione synthesis pathway [92] and the abscisic acid (ABA) synthesis pathway [91]. Plants are likely to use ROS to successfully defend themselves against pathogens if they are in a favourable environment for survival. And when in an unfavourable environment, such as salinity, drought and low-temperature, D. sapinea is likely to invade the host cell via its own ROS scavenging systems.

Next, after D. sapinea invaded the host, there was a significant accumulation of lignin content in the needles of P. sylvestris L [91,92]. The lignin synthesis pathway was significantly activated [91]. Active lignification has been well documented as a defence reaction of plants upon pathogen infection [93]. Also in vitro studies indicate that lignification plays an essential role in the response of its host against D. sapinea [94,95]. Our study showed that eight lignin-degrading enzyme genes were significantly up-regulated for expression during D. sapinea invasion, suggesting that the pathogen was attempting to degrade the cell wall. In turn, the plant consequently activates a lignification enhancement programmer, and the two fight around the cell wall. This means that these lignin-degrading enzymes are key to pathogenicity, and inhibiting the expression of these enzymes in the pathogen or increasing the expression of genes related to lignin synthesis in the plant is an effective means of controlling D. sapinea.

Supplementary Material

Supplementary tables.xlsx

KVIR_A_2490216_SM8445.xlsx^{(2.1MB, xlsx)}

Supplementary figures.docx

KVIR_A_2490216_SM8444.docx^{(870KB, docx)}

Funding Statement

This study was supported by the National Key R& D Program of China [2022YFD1401005] and the Local Post-doctoral Funding [LBH-Z23055].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

R.W.: conducted the experiments and analysed the data, wrote the manuscript, and cultured the plant material. Y.W., S.L. and S.F.: analysed the data. T.J. and B.Z.: designed research, acquired funding, and developed methodology; All authors have read and agreed to the published version of the manuscript.

Data availability statement

The datasets used and analysed in this study are available in the Sequence Read Archive (SRA) at NCBI at the National Center for Biotechnology Information. The accession number is PRJNA1098348. Supplementary tables and figures have been uploaded to the Figshare database (https://doi.org/10.6084/m9.figshare.28087769.v1). The authors confirm that the data supporting the results of this study are available in the article and its supplementary materials.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21505594.2025.2490216

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Data Availability Statement

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PERMALINK

Combining whole genome and transcriptome sequencing to analyze the pathogenic mechanism of Diplodia sapinea blight in Pinus sylvestris var. mongolica Litv.

Ruiqi Wang

Yuting Wang

Sina Fu

Shixian Liao

Tingbo Jiang

Boru Zhou