Abstract

   Phytophthora infestans causes potato late blight and significantly
   impacts potato production. The pathogen’s remarkable adaptability and
   ability to generate new physiological races through virulence variation
   undermine varietal resistance, posing challenges for disease prevention
   and control. To explore the genetic mechanisms behind these, different
   physiological races of P. infestans were inoculated on potato leaves
   and assayed through transcriptomics combined with metabolic profiling
   methods. It was found that the DL04 strain, which carries virulence
   factor 3, exhibited a high level of pathogenicity. Biologically, DL04
   showed more rapid growth and denser mycelial structures compared to
   most other strains, indicating enhanced pathogenicity. DL04 induced a
   greater enrichment of carbon metabolism, biosynthesis of amino acids,
   and glycolysis/gluconeogenesis pathways during the infection process.
   This led to the upregulation of genes related to cell hydrolysis,
   accelerating leaf infection and contributing to its higher level of
   pathogenicity. The reasons for the differences in pathogenicity among
   different physiological races of P. infestans were clarified at the
   transcriptional level. This finding provides valuable insights into the
   genetic basis of P. infestans pathogenicity and offer critical
   information for developing effective control strategies, breeding for
   disease resistance, and improving potato production practice.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12870-025-06736-y.

   Keywords: Physiological races, Genetic analysis, Late blight, Metabolic
   pathways, Potato resistance

Introduction

   Late blight caused by Phytophthora infestans is the most devastating
   disease of potato (Solanum tuberosum) [[50]1]. Potatoes have developed
   multiple late blight resistance genes. Currently, 11 resistance genes
   (R1-R11) have been identified in potatoes, each counteracting a
   corresponding P. infestans race and virulence factor. However, P.
   infestans continually evolves new physiological races that overcome the
   host resistance. Conventionally, P. infestans races are determined
   using a set of indicator potato varieties, each of which harboring one
   of the known resistance genes [[51]2]. However, the rapid emergence of
   novel P. infestans races is outpacing the development of resistant
   varieties [[52]3–[53]5], leading to the breakdown of resistance
   mechanisms.

   Virulence factor 3 has been identified as a key determinant of
   pathogenicity in P. infestans, multiple studies worldwide have
   emphasised the necessity of investigating the impact of epidemic races
   on the severity of potato late blight [[54]6–[55]8]. A total number of
   126 P. infestans isolates from Poland were analysed and found vir3
   present in 99% of strains, all of which exhibited resistance to R3 host
   plants [[56]9]. A total number of 116 P. infestans isolates from Latvia
   were analysed that more than 80% of the isolates were resistant to R3
   [[57]10]. Virulence gene analysis of P. infestans physiological races
   in Fujian, China, found that 100% of the strains carried vir3 [[58]11].
   Similarly, virulence factor 3 and virulence factor 4 to be predominant
   in Sichuan, China [[59]12]. Furthermore, the dominance frequency of
   virulence factor 3 in up to 91.7% of collected strains [[60]13],
   reinforcing the virulence factor 3 central role in the pathogenic
   virulence. This widespread prevalence has resulted in the complete loss
   of resistance in potato cultivars containing the major R3 resistance
   gene.

   Transcriptome sequencing plays an important role in the study of plant
   responses to pathogen-induced stress, unraveling the pathogenic
   mechanisms of plant pathogens [[61]14], and contributing to
   understanding the interaction between pathogens and plant hosts
   [[62]15]. RNA sequence (RNA-seq) effectively evaluates key
   pathogen-host interaction genes, elucidating molecular mechanisms of
   pathogenesis [[63]16], and enables rapid identification of upregulated
   pathogen effector genes during early infection [[64]17, [65]18]. For
   example, The dynamics of metabolism, signaling and virulence genes of
   P. infestans strains at different developmental stages during the
   mycelium-sporangium transition have significant changes [[66]19].
   Similarly, In response to the production of phenazine-1-carboxylic acid
   ( PCA ) by Pseudomonas fluorescens, the expression of key functional
   genes such as PCA-induced phosphorylation, transmembrane transport and
   redox activity was significant [[67]20]. The transcriptional response
   process of potato leaves and tubers after inoculation with P. infestans
   showed that significant differences in the responses of leaves versus
   tubers, but noted that core genes related to defense were highly
   up-regulated in both tissues [[68]21]. These findings provide a new way
   for the prevention and control of plant diseases, and a theoretical
   basis for the screening of host resistance genes.

   Traditional plant disease studies assess pathogenicity through
   phenotypic and biochemical traits, yet lacks accuracy in predicting
   dynamic pathogenicity changes of P. infestans. Transcriptomic profiling
   overcomes these limitations by sensitively detecting
   pathogenesis-associated genes and pathways, enabling comprehensive
   host-pathogen interaction analysis. This study integrates phenotypic
   characterization with transcriptomics across distinct P. infestans
   physiological races to unravel virulence gene-driven mechanisms
   underlying pathogenic variations during potato infection.

Materials and methods

Phytophthora infestans strains

   Twenty physiological races of Phytophthora infestans were isolated from
   potato leaves showing late blight symptoms in Yunnan, China
   (Table [69]1). The cultures were grown on Rye tomato agar [[70]22]. The
   plant material potato ‘S88’ was provided by the Potato Disease Research
   Laboratory of Yunnan Agricultural University, Kunming, China. Potato
   ‘S88’ were grown for 45 days, and compound leaves from the top to the
   third leaf position were chosen for pathogen inoculation.

Table 1.

   Phytophthora infestans strains used in this study
   Strain number Physiological races / Virulence factor
   MLS13418-2                     8.10
   NLL605                        10.11
   ZTQ907                        1.3.11
   MLS13418-1                  1.2.4.5.10
   HS03                       3.6.8.10.11
   XDL601                     1.2.4.5.9.11
   DQ01                       1.2.3.4.5.10
   JAX207                    1.2.3.4.5.6.11
   LJ05                     1.2.3.4.5.10.11
   HS02                     2.4.5.6.8.10.11
   DSPQ006                 1.2.4.5.6.8.10.11
   ML01-2018               1.2.3.4.5.9.10.11
   XDQ901                 1.2.3.4.5.8.9.10.11
   HZ02                   1.2.4.5.6.8.9.10.11
   LJ04                   1.2.5.6.7.8.9.10.11
   ML01-2019              1.2.3.4.5.6.7.8.9.10
   DL04                  1.2.3.4.5.6.8.9.10.11
   JCZ29                 1.2.3.4.5.6.7.8.10.11
   MLS8802               2.3.4.5.6.7.8.9.10.11
   HJG02                1.2.3.4.5.6.7.8.9.10.11
   [71]Open in a new tab

Phytophthora infestans inoculation on detached potato leaves

   Leaves from potato ‘S88’ plants were removed and rinsed three times
   with sterile water to remove dust and other contaminants from the leaf
   surfaces. They were then placed with the upper surface facing up in a
   petri dish containing 0.8% water agar. Following the method of Wang et
   al. [[72]23], a sporangial suspension at 8,000 sporangia/mL was
   prepared, and 25 µL of the suspension was deposited to either side of
   the leaf vein. Three leaves were inoculated per strain as replicates,
   and the procedure was conducted three times. The leaves were then
   turned over and incubated in an incubator at 19℃ for a cycle between
   light and darkness for 12-hour periods. Observations were recorded
   three days after the incubaton began. The pathogenicity of P. infestans
   on potato leaves was determined by measuring the lesion area to total
   leaf area pixel ratio using Photoshop [[73]24].

Biological characterization of Phytophthora infestans strains

   Phytophthora infestans was cultured on rye tomato medium and incubated
   in the dark at 19 °C for 10 days. Following the method of Mu et al.
   [[74]25], an 8 mm culture plug of P. infestans was cut using a cork
   borer, which was then placed in the center of the plate facing down.
   Each treatment had three replicates, and cultures were incubated at
   19 °C in darkness for 4, 6, and 8 days. Colony diameter was measured
   perpendicularly, and mycelial growth was subsequently analyzed. After
   eight days of incubation, hyphae were scraped off and weighed using an
   analytical balance. The collected mycelia were then homogenized with
   water, and the number of sporangia was determined using a
   hemocytometer.

   To assess the sensitivity analysis of P. infestans to metalaxyl (98%,
   RHAWN, Shanghai, China), the chemical was dissolved in dimethyl
   sulfoxide to create a 100 µg/mL stock solution. Rye tomato culture
   medium was prepared by adding metalaxyl at 5 µg/mL or 100 µg/mL. A
   culture plug (8 mm diameter) was placed in the center of the
   metalaxyl-amended plate. Each strain was treated with three replicates.
   The sensitivity of P. infestans to metalaxyl was evaluated by measuring
   colony diameter, mycelial weight and sporangial number after eight days
   of incubation in the dark at 19 °C.

Transcriptome analysis

   Two Phytophthora infestans strains, HZ02 and DL04 with different
   virulence levels were inoculated onto detached potato leaves.
   Non-inoculated leaves were used as the control group (CK). After three
   days of inoculation, the leaves were collected, and flash-frozen in
   liquid nitrogen, and stored in a refrigerator at -80° C for later use.
   Total RNA was extracted from the leaves using the Total RNA Extractor
   Kit (Trizol, Sangon Biotech, Shanghai, China) according to the
   manufacturer instructions. The total RNA was quantified using the Qubit
   2.0 RNA detection kit (Life Tech, Carlsbad, USA) to determine the
   amount of total RNA for library construction. The integrity and purity
   of total RNA were detected by 1% agarose gel electrophoresis and
   ultraviolet spectrophotometer (Inc SMA4000, Merinton Instrument, USA).
   RNA was considered of sufficient quality when the OD value A260/A280
   was above 2.0.

   The total RNA was subjected to mRNA isolation and fragmentation. The
   Hieff NGS™ Max Up Dual-mode mRNA Library Prep Kit (12301ES96, YEASEN,
   China) was used for reverse transcription to synthesize double-stranded
   cDNA. cDNA fragments were purified and size-selected using Hieff NGS™
   DNA Selection Beads (12601ES56, YEASEN, China). The amplified library
   products underwent additional purification with the same bead-based
   system to ensure fragment size uniformity. The cDNA library clusters
   were generated using the cBot machine (Illumina, San Diego, CA, USA)
   [[75]26].

   Qualified samples were sequenced using the Illumina HiSeq 2500 platform
   (Shanghai Sangon, Shanghai, China). Raw reads were processed using
   FastQC ([76]http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
   to remove adapters and low-quality sequences. Clean reads were then
   mapped to the reference genome downloaded from NCBI
   ([77]http://ncbi.nlm.nih.gov/) using Hisat2
   ([78]https://ccb.jhu.edu/software/hisat2/index.shtml). Reads containing
   adapters, sequences with more than 10% unknown nucleotides, and those
   with an average quality score below Q20 were discarded [[79]27].

   All assembled unigenes were annotated using several databases,
   including NCBI NR ([80]http://ncbi.nlm.nih.gov/), GO
   ([81]http://www.geneontology.org), KEGG ([82]http://www.kegg.jp), Pfam
   ([83]http://pfam.xfam.org/), and KOG
   ([84]https://www.ncbi.nlm.nih.gov/COG/). The sequencing data were
   analyzed to explore the relationship between the pathogenicity
   differences of various physiological races and genomic variations. This
   included Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia
   of Genes and Genomes (KEGG) pathway enrichment analysis to predict the
   potential functions of the identified genes. Gene expression levels
   were estimated as fragments per kilobase per transcript per million
   mapped reads (FPKM values). Principal component analysis (PCA) was
   performed on regularized log values of read counts. Differentially
   expressed genes (DEGs) between different treatments were restricted
   based on a false discovery rate ≤ 0.05 and an absolute value of
   log[2]Ratio ≥ 1 [[85]27].

Validation of gene expression by quantitative real-time PCR (qRT-PCR)

   Total RNA was extracted using the UNlQ-10 column Trizol Total RNA
   Extraction Kit. RNA degradation and contamination were assessed via
   1.5% agarose gel electrophoresis. A total of 1500 ng of RNA was added
   to a nuclease-free PCR tube on ice. To the tube, 1 µL of Random Primer
   p(dN)6 (100 pmol), 1 µL of dNTP mix (0.5 mM final concentration), and
   RNase-free ddH[2]O were added to bring the total volume to 14.5 µL. The
   mixture was gently shaken and briefly centrifuged for 3 to 5 s. The
   reaction was incubated at 65℃ for 5 min, followed by an ice bath for
   2 min, and a brief centrifugation for 3 to 5 s. Following the ice bath,
   4 uL of 5X RT buffer, 0.5 µL of Thermo Scientific RiboLock RNase
   Inhibitor (20 U), and 1 µL of Maxima Reverse Transcriptase (200 U) were
   added to the reaction mixture. The reaction was gently mixed and
   centrifuged for 3 to 5 s. Reverse transcription reaction was then
   performed on a PCR instrument.

   For fluorescence quantitative PCR, the cDNA sample was diluted 5-fold
   and used as a template, with the actin housekeeping gene as the target
   for amplification. The PCR primers were reactin-F2 (5’
   TGCCTGATGGACAAGTTATTACC 3’) and actin-R2 (5’ CCACTGAGCACAATGTTACCG 3’).
   The 10 mL PCR reaction mixture included 5 mL of 2X SybrGreen qPCR
   Master Mix, 0.2 mL of 10 mM primer F, 0.2 mL 10 mM R, 3.6 mL of
   ddH[2]O, and 1.0 mL of cDNA. The thermal cycling conditions were as
   follows: 95 °C for 3 min, followed by 95 °C for 15 s, 60 °C for 30 s,
   for a total of 45 cycles. PCR products were analyzed using a Light
   Cycler480 II fluorescence quantitative PCR instrument (Roche,
   Rish-Rotkreuz, Switzerland). The relative expression of genes was
   calculated using the 2^−ΔΔCT method. Three independent biological and
   technical replicates were conducted throughout the entire RNA
   extraction and sequencing workflow, ensuring methodological robustness.

Statistical analysis

   Statistical analyses were performed using IBM SPSS Statistics version
   25.0 software (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 9.0
   software (GraphPad Inc, San Diego USA). Prior to analyses, the
   normality of distribution and homogeneity of variance were assessed.
   Differences in means between treatments were evaluated using one-way
   ANOVA and Duncan’s multiple range test (P < 0.05).

Results

Pathogenicity of Phytophthora infestans strains

   The tested physiological races of Phytophthora infestans exhibited
   different levels of pathogenicity on detached potato leaves
   (Fig. [86]1) (Supplementary Table [87]S1). Lesion areas ranged from
   3.77 to 90.10%, indicating substantial pathogenic differences among the
   races. Strain HJG02, which harbors virulence factor 11(virulence factor
   reference Table [88]1), resulted in a lesion area of 60.43%, and was
   not significantly different from ML01-2019, which had lesion area of
   56.46%. In contrast, LJ05 containing virulence factor 11 exhibited a
   lesion area of 24.18%, while DQ01 lacking virulence factor 11 showed a
   larger lesion area of 38.46%. DL04, deficient in virulence factor 7,
   displayed extensive lesions (76.26%), whereas HJG02 carrying virulence
   factor 7 developed markedly smaller lesions. MLS8802 demonstrated
   moderate lesions (48.49%), but HJG02 harboring virulence factor 1
   produced significantly larger lesions. Notably, HZ02 lacking virulence
   factor 3 exhibited minimal lesions (12.66%), whereas DL04 with intact
   virulence factor 3 caused substantially larger lesions. Given the
   marked pathogenicity difference between HZ02 and DL04, further analysis
   will focus on biological characteristics and sensitivity to metalaxyl
   of these two strains with distinct virulence gene profiles.

Fig. 1.

   [89]Fig. 1
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   Pathogenicity assay of Phytophthora infestans strains on potato
   detached leaves observed three days after inoculation. Each image
   displays representative lesions derived from three biological
   replicates

Biological characterization of Phytophthora infestans strains HZ02 and DL04

   There were significant differences in biological phenotypes between
   HZ02 and DL04 (Fig. [91]2). HZ02 exhibited shorter and sparser hyphae,
   while DL04 had longer and denser hyphae (Fig. [92]2a). Additionally,
   the mycelial growth of the strains diverged significantly over time,
   with DL04 consistently showing faster growth (Fig. [93]2b). The weight
   of mycelium from DL04 were significantly higher than that of HZ02
   (Fig. [94]2c), and the number of sporangia per unit weight of mycelia
   from DL04 was slightly higher than that of HZ02, but the difference was
   not significant (Fig. [95]2d).

Fig. 2.

   [96]Fig. 2
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   Comparisons of Phytophthora infestans strains HZ02 and DL04: (a)
   culture morphology after 8 days of incubation, (b) mycelial growth over
   4, 6, and 8 days, (c) mycelial weight, and (d) sporangia count per unit
   of mycelial weight. Error bars represent standard deviations. Asterisks
   (**) indicate a significant difference between the strains (P < 0.01)

Sensitivity of Phytophthora infestans strains to metalaxyl

   Significant difference in sensitivity to metalaxyl was observed between
   strains HZ02 and DL04, impacting mycelial growth, weight, and
   sporulation (Fig. [98]3). The growth of DL04 was significantly
   inhibited by metalaxyl compared to HZ02, which was more sensitive with
   an inhibition rate significantly higher than that of HZ02 (Fig. [99]3a,
   [100]b).

Fig. 3.

   [101]Fig. 3
   [102]Open in a new tab

   Comparisons of Phytophthora infestans strains HZ02 and DL04 in response
   to metalaxyl: (a, b) mycelial growth, (c) mycelial weight, and (d)
   sporulation. Error bars represent standard deviations. Significant
   differences between the strains were indicated by asterisks **
   (P < 0.01) or * (P < 0.05)

Transcriptomic analysis

   RNA-seq analysis was conducted on potato leaves infected by
   Phytophthora infestans DL04 and HZ02 to elucidate the causes of
   pathogenic differences between the two physiological races. Nine
   libraries (CK, DL04 and HZ02, three biological replicates for each
   treatment) were generated, yielding approximately 75.13 Gb clean
   nucleotide sequences, with average Q20 and Q30 values of 97.38% and
   93.53%, respectively, and an average GC content of 48.00%. Clean data
   matching to the reference genome showed that alignment rates between
   92.96% and 94.16% across the nine samples (Supplementary Table
   [103]S2), indicating high-quality sequencing suitable for further
   analysis.

   The transcriptome profiles of P. infestans changed after P. infestans
   DL04 and HZ02 strains infected potato leaves. Functional annotation was
   performed across multiple databases including NR, GO, KEGG, Pfam, KOG,
   and SwissProt databases, with 20,036, 17,312, 8636, 10,891, 13,021, and
   10,593 annotated uniqueness respectively (Supplementary Table [104]S3).
   GO enrichment analysis categorized the differentially expressed genes
   (DEGs) (Supplementary Figure [105]S1), revealing that 17 terms related
   to biological processes were dominant, particularly those associated
   with metabolic process, cellular process, and biological regulation.
   The cellular component category contained 12 terms, primarily involving
   the cell, cell parts, and organelles. Additionally, two terms in the
   molecular function category, binding and catalytic activities, were
   enriched.

   PCA showed that DL04 and HZ02 inoculations were significantly separated
   from the control group. PC1 and PC2 accounted for 48.79% and 20.03% of
   the variation, respectively, indicating substantial gene expression
   differences between the treatments (Fig. [106]4a). Differential
   expression analysis using DESeq and based on FPKM identified 1,482,
   9,525, and 1,851 DEGs for the HZ vs. CK, DL vs. CK and DL vs. HZ
   comparisons, respectively. Interestingly, the number of genes that were
   significantly upregulated and downregulated differed across each
   comparison group (Fig. [107]4b, Supplementary Figure [108]S2),
   highlighting a significant shift in gene expression post-inoculation
   with DL04, which contains virulence factor 3.

Fig. 4.

   [109]Fig. 4
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   Transcriptomic analysis of potato leaves inoculated with different
   physiological races of Phytophthora infestans. (a) Principal component
   analysis (PCA) showing the distribution of transcriptomic data points.
   (b) Volcano plot of gene expression variance of P. infestans strains
   HZ02 and DL04. Red indicates significant upregulation, green indicates
   significant downregulation, and black represents gene expressions with
   no significant differences. The horizontal axis displays log2 fold
   change in gene expression, while the vertical axis represents the
   q-value. (c) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   enrichment analysis for DL and HZ inoculations. The vertical axis
   indicates pathways, and the horizontal axis indicates the enrichment
   factor (Ratio of differentially expressed genes (DEGs) to total
   annotated genes in each KEGG pathway) corresponding for each pathway.
   Dotted points represent P-value significance, with darker red
   indicating higher significance. The size of each dot represents the
   number of differentially expressed metabolites. (d) Differential gene
   expression in enrichment pathways comparing DL04 and HZ02 inoculations.
   Red bars indicate upregulated gene expression and blue bars show
   downregulated gene expression

   KEGG pathway enrichment analysis for the DL vs. HZ comparison
   identified 30 significantly enriched pathways (P < 0.05), including
   carbon metabolism, biosynthesis of amino acids,
   glycolysis/gluconeogenesis, peroxisome, fatty acid metabolism, fatty
   acid degradation, valine, leucine and isoleucine degradation,
   glyoxylate and dicarboxylate metabolism, and tyrosine metabolism
   (Fig. [111]4c). In the carbon metabolic pathway, 38 genes were
   upregulated and two downregulated genes; and the
   glycolysis/gluconeogenesis pathway had 19 upregulated and one
   downregulated genes (Fig. [112]4d). These pathways, relating to sugar
   and acid metabolism and degradation, suggest that DL04 induces
   significant metabolic changes upon infecting potato leaves.

   After inoculation with P. infestans DL04, a strain with strong
   pathogenicity that carries virulence factor 3, the gene expression
   significantly changed in the infected potato leaves. This variation in
   gene expression was linked to difference in the pathogenicity of the
   leaves. Further analysis showed a significant upregulation of genes in
   key pathways, including carbon metabolism, biosynthesis of amino acids,
   and glycolysis/gluconeogenesis, with a total of 82 genes significantly
   upregulated across these pathways (Fig. [113]5, Supplementary Table
   [114]S5). In the carbon metabolism pathway, genes with the higher fold
   increase included threonine dehydratase (TdcB), formate dehydrogenase
   (FDH), Aminomethyltransferase (AMT), isocitrate dehydrogenase (IDH),
   acyl-CoA dehydrogenase (CADD), and acetyl-coenzyme A synthetase
   (Acetyl-CoA).

Fig. 5.

   [115]Fig. 5
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   Schematic diagram of patterns related to significantly enriched
   pathways in potato infected by Phytophthora infestans strains DL04 and
   HZ02. The blue ellipse represents genes that are significantly enriched
   in DL04 and HZ02 inoculations. The heatmap shows differential gene
   expression, with each square representing comparisons: HZ02 vs. CK
   (non-inoculated control), DL04 vs. CK, and DL04 vs. HZ02. The
   expression levels of annotated genes are represented in the heatmap,
   with a color scale ranging from blue (-20) to red (20), indicating the
   extent of gene expression changes

   In the biosynthesis of amino acids pathway, notable upregulations were
   observed in genes such as threonine dehydratase catabolic (TdcB),
   acetylornithine aminotransferase (argD), dihydroxy-acid dehydratase
   (IIvD), aspartate aminotransferase (AST), chorismate synthase, tyrosine
   aminotransferase (TAT), aldehyde dehydrogenase (ALDH), and isocitrate
   dehydrogenase (IDH). In the glycolysis/gluconeogenesis pathway,
   significant upregulations included genes such as aldehyde dehydrogenase
   (ALDH), aldose 1-epimerase, and acetyl-coenzyme A synthetase
   (Acetyl-CoA).

   Additionally, pathway enrichment analysis identified several
   cell-wall-hydrolysis genes that were significantly upregulated (Table
   [117]S6), including pectinesterase (PE), glycoside hydrolase (GH),
   polygalacturonase (PG), cell 5 A endo-1.4-betaglucanase (EG), aldehyde
   dehydrogenase (ALDH), fructokinase (FRK), glucose-6-phosphate isomerase
   (GPI), cellulose synthase (CESA), chitinase (CHT), and cysteine
   protease (CPs). Compared to the control, the pectinesterase genes PEs
   (PITG_06560 and PITG_08912) were upregulated 18.17- and 17.44-fold,
   respectively. The polygalacturonase gene PG (PITG_19624) exhibited a
   17.19-fold increase, and glycoside hydrolase genes GH (PITG_17546 and
   PITG_04135) were upregulated 17.15- and 18.24-fold. The results showed
   that these genes involved in cell wall degradation were significantly
   expressed during the infestation of P. infestans.

   From the pool of candidate DEGs, genes encoding PE, PG, GH, ALDH, and
   EG were selected for qRT-PCR analysis to validate the transcriptomic
   data. The relative expression of PITG-01029 encoding PE and PITG03136
   encoding GH was significantly upregulated (Fig. [118]6b, [119]e).
   Compared with strain HZ02, the relative expression of DEGs encoding PG,
   ALDH and EG was upregulated, but the increase was not statistically
   significant (Fig. [120]6d, [121]f, [122]g). These results corroborated
   the expression trends observed in the transcriptome sequencing,
   affirming the reliability of the sequencing results.

Fig. 6.

   [123]Fig. 6
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   Relative gene expression in potato leaves treated with Phytophthora
   infestans strains DL04 and HZ02, as verified by quantitative polymerase
   chain reaction (qPCR). Significant differences are indicated by
   asterisks ** (P < 0.01) and * (P < 0.05)

Discussion

   Metabolic pathways play a key role in pathogen infection, affecting
   energy acquisition and metabolic processes [[125]28, [126]29]. Our
   analysis revealed that carbon metabolism, biosynthesis of amino acids,
   and glycolysis/gluconeogenesis pathways were significantly enriched in
   potato leaves following infection by Phytophthora infestans strain
   DL04, suggesting their potential involvement in pathogenic processes.
   Emerging evidence indicates that metabolic pathways play regulatory
   roles in modulating the virulence mechanisms of phytopathogens during
   host colonization. Similar studies have identified carbon metabolism as
   a significantly enriched pathway in Xanthomonas oryzae, demonstrating
   that carbon source acquisition critically facilitates pathogen-host
   interaction establishment and proliferative growth [[127]30]. Carbon
   storage is also vital for oomycetes, particularly in oospores. Unlike
   most fungal spores, oospores possess metabolic activity. These reserves
   are necessary to support germ tube formation and host penetration
   [[128]31]. Through transcriptomic profiling revealed that distinct
   enrichment of glycolysis/gluconeogenesis pathways in KEGG functional
   annotation when comparing differential gene expression between
   pathogenic variants Sporisorium scitamineum Ss16 and Ss47 [[129]32].
   The bE/bW heterodimeric transcription factor encoded by the b locus
   modulates sexual mating and/or filamentous growth in S. scitamineum
   through coordinated regulation of sugar metabolism and Hog1-mediated
   oxidative responses [[130]33]. Glycolysis is recognized as a central
   metabolic pathway essential for pathogenesis in several pathogens
   [[131]34], including Candida albicans, where it supports surface
   adhesion, virulence, and are responsiveness under specific
   environmental conditions [[132]35]. These related pathways are
   indirectly involved in the pathogenic process. These studies reported
   similar results to the present study, suggesting that carbon metabolism
   and glycolysis/glycolysis pathways are associated with the infestation
   process of pathogenic. We hypothesize that during late blight
   pathogenesis, P. infestans may assimilate carbon sources to fuel
   carbohydrate metabolism and related pathways, thereby supplying carbon
   skeletons for biosynthesis of amino acids while potentially mediating
   direct or indirect signaling cascades critical to virulence.
   Subsequently, the metabolic pathway will be further analyzed and its
   main role in the pathogenesis will be verified.

   The plant cell wall serves as the first line of defense against
   pathogen infection [[133]36]. To overcome this obstacle, plant
   pathogens produce various cell wall-degrading enzymes during the
   pathogenic process. Plant pathogens deploy extracellular hydrolases
   such as endopolygalacturonase (endo-PG) and pectin methylesterase
   (PME), which are essential for pathogen colonization [[134]37]. In this
   study, infection of potato leaves by P. infestans DL04, which harbors
   virulence factor 3, led to the significant upregulation of genes
   encoding cell wall-degrading enzymes. These included pectin
   methylesterase (PE), endopolygalacturonase (PG), glycoside hydrolase
   (GH), glucanase (EG), and cellulose synthase (CESA). These genes play a
   key role in plant growth and development, and it is speculated that the
   pathogenic differences observed may be due to the differential
   expression of genes involved in cell wall degradation. Several studies
   have shown that cell wall degrading enzymes play a key role in
   pathogenicity, for example, the pectin methylesterase Pcpme6 from
   Phytophthora capsici has been shown to exhibit strong virulence and
   diverse transcriptional responses when infecting different hosts
   [[135]38]. Plant pathogens can degrade plant cell walls by secreting
   glycoside hydrolase XEG1 to promote infection in the early stage of
   infection [[136]39]. PG penetrates plant cell walls and accelerates
   host invasion [[137]40]. Expression of a gene encoding a pectin
   hydrolase was found to be associated with pathogenicity of Fusarium
   solani, and pathogenicity was reduced only when expression was
   suppressed [[138]41]. Similarly, mutations in the endogenous
   polygalacturonase gene of Alternaria alternata infecting citrus fruits
   weakened the fungus’ pathogenicity [[139]42]. These findings indicate
   that cell wall-degrading enzymes are crucial pathogenic factors in the
   process of pathogen infection. Therefore, the difference in
   pathogenicity among different physiological races of P. infestans may
   be partially due to the different expressions of genes encoding cell
   wall-degrading enzymes. Previously, we have identified pectinesterase
   as a key factor responsible for the virulence differences, as
   pectinesterase activity and its gene expression are positively
   correlated with the pathogenicity of P. infestans [[140]43]. In
   addition to pectinesterase, other cell wall-degrading genes should be
   investigated in subsequent studies. Reportedly, the effector proteins
   PcPL1, PcPL15, PcPL16, and PcPL20 of P. capsici can induce plant cell
   death [[141]44].

   With the ongoing evolution of P. infestans physiological races,
   previously effective resistance genes have lost their functionality
   [[142]45]. Effectors play a crucial role in the interaction between
   pathogen and hosts, directly affecting the invasion, expansion, and
   disease occurrence [[143]46]. To successfully invade and reproduce in
   host plants, avirulence genes (Avr) in P. infestans can mutate to evade
   recognition by these resistance genes [[144]47, [145]48]. For example,
   the host R3 haplotype, corresponding to virulence factor 3 contained
   two major resistance gene clusters R3a and R3b, which have been
   confirmed with their corresponding avirulence genes, Avr3a [[146]49]
   and Avr3b in P. infestans [[147]50]. Interestingly, the avirulence gene
   Avr3a, which corresponds to the host major resistance gene R3, was
   found in the sequencing results, and the expression level was
   significantly up-regulated after DL04 infection, with an upregulated
   21.59-fold. However, Avr3b was not found in the data, indicating that
   Avr3a may also be involved in the pathogenesis (Supplementary Table
   [148]S4). It is speculated that the difference in virulence factors may
   not only be related to the pathogenicity of P. infestans, but also to
   the participation of avirulence genes in plant-pathogen interactions.
   Avr3a is known to trigger plant immune responses and manipulate defense
   pathways [[149]51]. Avr3a-like effectors, widely conserved in
   Phytophthora species, orchestrate multifaceted immune suppression in
   host plants. These effectors stabilize Arabidopsis CAD7 subfamily
   member AtCAD7, directly impairing PAMP-triggered immunity (PTI) by
   reducing callose deposition, ROS bursts, and WRKY33 expression
   [[150]52]. Additionally, AVR3a stabilizes the host E3 ligase CMPG1 to
   block programmed cell death (PCD) during biotrophic growth, ensuring
   pathogen survival [[151]53]. Beyond PTI/PCD suppression, AVR3a disrupts
   immune receptor endocytosis by targeting a membrane-trafficking
   complex, thereby inhibiting BAK1/SERK3-mediated immunity through
   distinct pathways, exemplifying its multifunctional virulence
   [[152]54]. In addition, dsRNA targeting Avr3a effectively inhibited P.
   infestans infection, enabling plants to obtain balanced immunity and
   enhanced defense [[153]55]. Specifically, Avr3a can inhibit host
   defense signals and enhance pathogen infection by regulating the
   interaction of immune factors in host plants. Although the study of the
   effector Avr3a has shed light on its role in pathogenicity and its
   mechanisms, the full scope of interactions between avirulence gene and
   host proteins are still unclear. Subsequent studies will focus on
   elucidating the molecular mechanism of the interaction between the
   Avr3a effector and the host. Mutations in Avr3a may lead to the
   breakdown of R3a-mediated resistance, thus necessitating continuous
   monitoring of the pathogen population’s genetic structure. The
   transcriptome markers obtained in this study can be utilized to monitor
   Avr3a variants for predicting resistance efficacy and formulating
   rotation strategies for potato cultivars carrying different R genes.
   Assisted rapid screening enables efficient identification of potato
   germplasm resources harboring the R3a resistance gene.

Conclusion

   Phytophthora infestans DL04 demonstrated strong pathogenicity,
   alongside fast growth, substantial mycelial production, and high
   sensitivity to metalaxyl. The pathogenicity of DL04 was closely linked
   to the expression of virulence factor 3, which activated key metabolic
   pathways related to the pathogenesis, including carbon metabolism,
   glycolysis, and biosynthesis of amino acids. This resulted in the
   upregulation of genes involved in cell hydrolysis, significantly
   contributing to its enhanced ability to infect and damage potato
   leaves. The study also highlighted the crucial role of cell
   wall-degrading enzymes, such as pectinesterase, polygalacturonase, and
   glycoside hydrolases, in the infection process. Furthermore, the
   upregulation of the avirulence gene Avr3a suggests that the interaction
   between virulence and avirulence factors plays an important role in the
   pathogen’s ability to evade host defense responses and facilitate
   infection. Transcriptome markers enable Avr3a variant tracking to
   optimize R gene-based cultivar rotation and predict resistance
   durability. Transcriptomics reveals P. infestans pathogenesis
   mechanisms, underscoring the need to target Avr3a effectors and cell
   wall-degrading enzymes for durable resistance breeding. These findings
   are of great significance for effector recognition and modulating cell
   wall-degrading enzymes activity to enhance disease management.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [154]12870_2025_6736_MOESM1_ESM.docx^ (12.7KB, docx)

   Supplementary Material 1: Table S1. Lesion area of potato leaves
   infected by different Phytophthora infestans strains.
   [155]12870_2025_6736_MOESM2_ESM.docx^ (15.3KB, docx)

   Supplementary Material 2: Table S2. Sequencing output statistics table.
   [156]12870_2025_6736_MOESM3_ESM.docx^ (16.3KB, docx)

   Supplementary Material 3: Table S3. Unigenes corresponding to single
   genes annotated in the database.
   [157]12870_2025_6736_MOESM4_ESM.docx^ (7.8KB, docx)

   Supplementary Material 4: Table S4. The expression of Avr3a gene in
   three treatments.
   [158]12870_2025_6736_MOESM5_ESM.docx^ (29.1KB, docx)

   Supplementary Material 5: Table S5. Significantly up- and
   down-regulated genes in the three pathways after inoculation of potato
   leaves with Phytophthora infestans DL04 strain.
   [159]12870_2025_6736_MOESM6_ESM.docx^ (20.5KB, docx)

   Supplementary Material 6: Table S6. Comparison of expression levels of
   hydrolysis-related genes in different treatments.
   [160]12870_2025_6736_MOESM7_ESM.pdf^ (459.8KB, pdf)

   Supplementary Material 7: Figure S1. GO functional classification of
   leaves infected by Phytophthora infestans.
   [161]12870_2025_6736_MOESM8_ESM.pdf^ (239.4KB, pdf)

   Supplementary Material 8: Figure S2. Volcano plot of differentially
   expressed genes.

Acknowledgements