Transcriptomic profiling of interspecific hybrid in response to Phytophthora nicotianae infection in betelvine

Transcriptomic profiling of interspecific hybrid in response to Phytophthora nicotianae infection in betelvine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptomic profiling of interspecific hybrid in response to Phytophthora nicotianae infection in betelvine Mohankumar G P, Hima Bindu K, Sriram S, Ravishankar K V, Jagadeesha R C, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Phytophthora nicotianae is highly pathogenic and major constraint in betelvine crop production. To examine the resistance of 13 interspecific hybrids to P. nicotianae , revealing significant variability in disease severity. IIHRPBIH9 exhibited the lowest disease severity and infection rate, whereas the IIHRBV170 was susceptible to infection. RNA sequencing was employed in IIHRPBIH9 and IIHBV170, which generated 6.62 GB of data, yielding 27,774 refined transcripts and 16,329 contigs. The leaf samples for RNA isolation were collected at 0 and 72 hours after inoculation (hai). 8561 and 10188 differentially expressed genes (DEGs) were identified in uninoculated and inoculated resistant (IIHRPBIH9) interspecific hybrid and susceptible variety, respectively. Gene ontology (GO) enrichment and KEGG pathway analyses highlighted key defense-related pathways, such as superoxide radicals degradation and jasmonic acid biosynthesis. Up-regulated genes included peroxidase 5-like protein and pathogenesis-related proteins, while down-regulated genes like LRR receptor-like kinase suggested susceptibility mechanisms. Heat maps revealed significant up-regulation of defense genes in inoculated resistant samples. The putative candidate genes were validated through qRT-PCR, confirming significant up-regulation of selected defense genes, and elucidating the molecular mechanisms of resistance and susceptibility. The transcriptome profile and putative candidate resistant and susceptible genes identified can be explored in breeding betelvine for P. nicotianae resistance. Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Microbiology Biological sciences/Molecular biology Biological sciences/Plant sciences Disease resistance differentially expressed genes Phytophthora nicotianae RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Phytophthora nicotianae , a typical oomycete, is known for its broad host range (Meng et al., 2021 ). The pathogen attacks the plant at the collar region and below the soil. Microclimate with humid and moist shade acts as a favorable condition for both the growth of betelvine and P. nicotianae. The characteristic symptom of disease is the disappearance of lustre of leaves, followed by wilting and drooping of vine due to loss of chlorophyll, ultimately leading to drying up of vine, meanwhile underground parts of the plant rot completely. The extent of losses may vary from 5 to 90 per cent due to foot rot (Dasgupta et al., 2008 ) and 20 to 40 per cent in case of leaf rot, leading to almost total crop failure (Dasgupta et al., 2000 ). Betelvine is a vegetatively propagated crop, which preserves high level of heterozygosity that is necessary for more hybrid vigor and any favourable spontaneous and induced mutants can easily be identified and propagated (Glemin et al., 2006 ). Unfortunately few countable efforts have been made till now in identifying resistant sources or developing resistant hybrids for foot rot. Piper colubrinum is a distant relative of cultivated betelvine, showing high resistance to Phytophthora foot rot. Understanding the resistance mechanism involved in pathogen interaction can be aided by knowledge of differential expression profiles. At ICAR-Indian Institute of Horticultural Research, Bengaluru, interspecific hybrids have been developed by crossing Phytophthora resistant species P. colubrinum (Turner, 1971 and Purseglove et al. 1981 ) and P. betle to develop resistant sources for this devastating disease. Plants have evolved with induced defense responses, which rely on the recognition of host-pathogen recognition and its associated molecular like DAMPs, MAMPs and PAMPs and pathogen effectors such as PTI (PAMP-triggered immunity) and ETI (effector-triggered immunity) (Jones and Dangl, 2006 ). Conversely, pathogens rely on host response to establish infection to express disease susceptibility genes aid mainly in infection of pathogen and disruption in these genes can lead to loss in host-pathogen compatibility (Chris and Frank, 2014 ). Recently, there has been increased focus on studying identifying and utilizing susceptible genes in developing durable broad-spectrum resistance (Ziidi et al ., 2018). RNA-seq through exploring genomic tools are powerful tools which facilitate the identification of candidate genes for desired trait. We earlier screened 13 interspecific hybrids for P. nicotianae infection and found IIHRPBIH9 identified as a resistant hybrid and the susceptible variety IIHRBVP170 as models (Mohankumar et al., 2022 ). RNA-seq was employed to analyze the gene expression profiles of leaves from both the resistant hybrid and susceptible variety at 0 and 72 hours after inoculation (hai) with P. nicotianae . Differentially expressed genes in relation to P. nicotianae infection were identified and key genes were validated. Our findings provide insights into the resistance mechanisms of betelvine response to the pathogen and provide a potentially valuable resource for future plant development. Results Transcriptome Sequencing and Assembly RNA sequencing was performed using the Illumina NovoSeq 6000 sequencer with 150 x 2 paired-end sequencing chemistry technique. RNA samples were taken from contrasting resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170) of betelvine, both inoculated and uninoculated leaf samples. A total of 6.62 GB of raw data was obtained. The number of raw reads per sample in KBs were 1992100, 1190220, 1503200, and 1930300 for susceptible uninoculated, susceptible inoculated, resistant uninoculated, and resistant inoculated samples, respectively. The reads were assembled and quality checked (FAST QC), followed by the identification of differentially expressed genes (DEGs), gene ontology (GO) analysis, and KEGG pathway analysis. Up-regulated and down-regulated genes were validated through qRT-PCR. Reads with ≥ 70% bases having a quality score ≥ Q20 were chosen for downstream analysis. Low-quality reads were removed, and the filtered reads were merged and subjected to primary transcriptome assembly, resulting in 97,658 transcripts with an NP50 of 965 bp. Post CD-HIT EST processing, the NP50 value improved to 1,152, and the total number of transcripts reduced to 27,774. The assembly was validated by aligning reads from both uninoculated and inoculated samples to the 27,774 transcripts using Bowtie 2, indicating an approximate transcriptome size of 23 MB. From the combined assembled RNAseq transcriptome data of both the resistant and susceptible samples, 16,329 contigs were obtained with a total length of 33,418,383 bp, the largest contig being around 32,759 bp and a GC content of 50.27 per cent (Table 1 ). Table 1 Summary of assembled transcriptome analysis Sl. No. Parameters Combined Assembly 1 Contigs ( > = 1000 bp) 16329 2 Total length ( > = 1000 bp) 33418383 3 N50 1666 bp 4 N90 767 bp 5 GC (%) 50.27 6 Largest contig 32759 bp Principal component analysis (PCA) PCA was performed using iGEAK (Choi and Ratner, 2019 ) to reduce data dimensionality and distinguish between the resistant and susceptible betelvine samples. The PCA results showed clear separation between resistant interspecific hybrid and susceptible variety of both inoculated and uninoculated samples, indicating substantial transcriptome variation in response to Phytophthora infection. The largest distance observed was between resistant uninoculated and susceptible uninoculated samples, suggesting significant transcriptome differences (Supplementarty Fig. 1 ). Differential gene expression in response to infection Calculations of ΔΔCt values for individual genes based on the ΔCt values of susceptible, resistant, and inoculated samples obtained from their RPKM (reads per kilo base per million) values revealed that an average of 5,000 transcripts were differentially expressed in any one of the four comparisons performed at 72 hai. Between uninoculated and inoculated resistant interspecific hybrids, 8,561 genes were differentially expressed, with 4,619 up-regulated and 3,942 down-regulated. In the susceptible variety, 10,188 genes were differentially expressed, with 5,543 up-regulated and 4,645 down-regulated. Between susceptible inoculated and resistant inoculated samples, 16,547 genes were differentially expressed, with 9,010 up-regulated and 7,537 down-regulated (Supplementary Figs. 2, 3 & 4). Comprehensive analysis of differential gene expression, go enrichment, and pathway interactions in betelvine resistance to P. nicotianae Venn diagram Distribution The Venn diagram revealed significant differential gene expression in the resistant hybrid compared to the susceptible variety, indicating transcriptional changes related to disease resistance. Specifically, 19,263 genes were differentially expressed between uninoculated and inoculated resistant and susceptible betelvine. Notably, 8,561 genes were differentially expressed in resistant uninoculated (RU) versus resistant inoculated (RI), 10,188 in susceptible uninoculated (SU) versus susceptible inoculated (SI), 16,547 in susceptible inoculated versus resistant inoculated, and 12,083 in susceptible uninoculated versus resistant uninoculated (Fig. 1 a and 1 b). Unique up-regulated genes were: 1,635 for RU versus RI, 3,344 for SU versus SI, 4,962 for SI versus RI, and 1,500 for SU versus RU, with 71 common genes. Unique down-regulated genes were: 2,342 for RU versus RI, 1,681 for SU versus SI, 2,713 for SI versus RI, and 1,086 for SU versus RU, with 202 common genes. Gene Ontology (GO) Analysis GO analysis predicted the functions of differentially expressed genes in four groups: RU versus RI, SU versus SI, SI versus RI, and SU versus RU using Blast2GO. GO annotation was performed for biological processes, molecular functions, and cellular components, revealing enriched terms in the betelvine transcriptome interaction with P. nicotianae. In the RU versus RI comparison, frequent biological processes included "other cellular process," "anatomical structure development," "response to external stimulus," and "response to stress." Frequent cellular components were "chloroplast," "cytoplasm," "cytosol," and "plasma membrane," and enriched molecular functions were "protein binding," "signaling receptor activity," and "lipid binding" (Fig. 2 a). Figure 2 b shows GO classification for SU versus SI, with "biosynthetic process" and "embryo development" as predominant biological processes, "nucleus," "chloroplast," "cytoplasm," and "mitochondrion" as major cellular components, and "protein binding," "molecular function," "RNA binding," and "DNA binding transcription factor activity" as key molecular functions. For SI versus RI (Fig. 2 c), biological processes included "other metabolic processes," "anatomical structure development," and "response to external stimulus." Cellular components were "chloroplast," "cytosol," and "cytoplasm," and molecular functions were "protein binding" and "signaling receptor activity." In SU versus RU (Fig. 2 d), predominant GO terms were "biosynthetic process," "chloroplast," and "protein binding," with "secondary metabolic process," "peroxisome," and "signaling receptor activity" being least frequent. Volcano Plots Volcano plots visualized the distribution of up- and down-regulated genes between conditions. Positive fold change indicates higher expression, while negative fold change indicates lower expression. Significant genes are at the top of the plot. In resistant interspecifc hybrid (IIHRPBIH9) versus susceptible variety (IIHRBV170), peroxidase 5-like protein and disease resistance-responsive proteins were up-regulated, while serine/threonine protein kinase and thaumatin superfamily proteins were down-regulated (Fig. 3 a). In SC versus SI, disease resistance protein (TIR-NBS-LRR class) and calcium-binding EF-hand family protein were up-regulated, while concanavalin A-like lectin protein kinase family proteins were down-regulated (Fig. 3 b). In SI versus RI, concanavalin A-like lectin protein kinase and pathogenesis-related protein 1-like protein were up-regulated, while pathogenesis-related protein 2-like protein and WRKY transcription factor family members were down-regulated (Fig. 3 c). In SU versus RU, mannose-binding protein and ubiquitin-like superfamily protein were up-regulated, while UDP-glucosyltransferase 76B1-like protein was down-regulated (Fig. 3 d). KEGG Pathway Analysis KEGG databases, which include genomic, biological pathway, and disease information, were used to annotate DEGs and map them to pathways, revealing that DEGs in RU versus RI, SU versus SI, and SI versus RI comparisons were involved in several pathways. In RU versus RI, DEGs were significantly enriched in defense-related pathways like superoxide radicals degradation, jasmonic acid biosynthesis, flavonoid biosynthesis, and ethylene biosynthesis I, with the highest number in superoxide radicals degradation. Moderate enrichment was seen in L-glutamine biosynthesis II, phenylalanine biosynthesis, and abscisic acid biosynthesis, while brassinosteroids inactivation and ubiquinol-9 biosynthesis showed the least enrichment (Fig. 4 a). In SU versus SI, pathways like jasmonic acid biosynthesis, superoxide radicals degradation, and glutathione-mediated detoxification II were enriched, with the most genes in glutathione-mediated detoxification II and the fewest in quercetin glycoside biosynthesis (Fig. 4 b). For SI versus RI, the most enriched pathways were glutathione-mediated detoxification II, phenylpropanoid biosynthesis, and superoxide radicals degradation (Fig. 4 c). Heat Maps Heat maps with hierarchical clustering displayed top defense-related DEGs. In the resistant inoculated sample, peroxidase 5-like protein and pathogenesis-related protein PRB1-3 like protein were highly up-regulated, while thaumatin-like protein 1, disease resistance protein (TIR-NBS-LRR class) family, and lipid transfer protein 1 were was down-regulated (Fig. 5 a). However, SI versus RI comparison (Fig. 5 b), glucan endo-1, 3-beta-glucosidase, putative disease resistance protein (RGA1 & RGA4), and pathogenesis-related protein 1-like protein were up-regulated in the resistant inoculated sample. In contrast, LRR and NB-ARC domains containing disease resistance protein, pathogenesis-related protein PR2 like protein, and galactose-binding protein isoform-1 were down-regulated in the resistant inoculated sample during betelvine- P. nicotianae interaction. Cytoscape analysis revealed 8,561 DEGs between RU and RI, with common GO terms in biological processes and cellular components. Pathways related to catalytic activity, stress response, and secondary metabolism were connected, indicating their role in defense mechanisms (Fig. 6 a). In the comparison of 16,547 DEGs between SI and RI, GO analysis categorized genes into biological process, cellular component, and molecular function, with pathways linked to peroxisome, endosome, signaling receptor activity, and biosynthetic process (Fig. 6 b). Validation of Defense-Related Genes Using qRT-PCR Validation of RNA-seq results was performed using qRT-PCR for selected defense-related genes, including peroxidase 5-like protein (POX), pathogenesis-related protein 1-like protein (PR-1), dirigent-like protein (DRR), and LRR protein kinase. The expression patterns confirmed significant up-regulation of these genes in inoculated resistant samples compared to uninoculated ones. POX was up-regulated in most comparisons except SI vs. RI, where it was down-regulated. PR-1 showed significant up-regulation in RU vs. RI and SI vs. RI comparisons. DRR was consistently up-regulated across all comparisons, with RNA-seq showing higher log fold changes than qRT-PCR. LRR protein kinase exhibited significant up-regulation in RU vs. RI and SI vs. RI comparisons, with RNA-seq values exceeding those from qRT-PCR analysis. Discussion High-throughput RNA sequencing (RNA-seq) is a powerful tool for identifying differentially expressed genes, assessing their levels of expression, and elucidating their regulatory mechanisms. Transcriptome analysis is widely recognized for identifying plant growth or developmental stages that are both scientifically and economically significant. Consequently, it is essential for obtaining a genome-scale map that includes the structure of transcripts, their complexity, and expression levels under specific conditions (Trapnell et al., 2010 ). In gene expression or RNA-seq analysis, numerous pathways are involved, although biotic stress pathways are predominantly focused on identifying disease resistance-related genes. The interspecific hybrid IIHRPBIH9 is considered a novel source of resistance to Phytophthora foot rot due to its high level of resistance based on artificial screening. In contrast, another germplasm, IIHRBV170, which is susceptible to Phytophthora foot rot disease, was also used for RNA-seq assay. To thoroughly understand the genes and pathways involved in betelvine response to P. nicotianae infections, four samples were selected for transcriptome sequencing: both uninoculated and inoculated plants were analyzed to identify differential gene expression between the resistant interspecific hybrid and the susceptible check variety of betelvine. The expression pattern regulated by pathogen infection enables the identification of defense-related gene families involved in betelvine immune response. R protein-mediated resistance typically correlates with a hypersensitive (HR) reaction, leading to the death of both the pathogen and the surrounding plant tissue (Hammond-Kosack and Kanyuka, 2007 ). Some R genes associated with disease responses include the WRKY transcription factor, CC-NBS-LRR type gene, TIR-NBS-LRR gene, and putative LRR domains (Malik and George, 2018 ). Using the Illumina HiSeq platform, a total of 6.62 GB of data was obtained. This included 1.99 GB from the uninoculated susceptible variety (IIHRBV170), 1.50 GB from the uninoculated resistant variety (IIHRPBIH9), 1.19 GB from the inoculated susceptible variety, and 1.93 GB from the inoculated resistant variety. A total of 25 million reads were generated for the susceptible uninoculated sample, 19 million reads for the resistant uninoculated sample, 14.8 million reads for the susceptible inoculated sample, and 24.40 million reads for the resistant inoculated sample. Principal component analysis showed substantial variation between P. nicotianae inoculated and uninoculated samples, clearly separating them into two groups, indicating transcriptome variation between the resistant interspecific hybrid (IIHRPBIH9) and the susceptible variety (IIHRBV170). The resistant uninoculated line exhibited the greatest distance from the susceptible uninoculated line, showing significant variation between them. A similar trend was observed in the resistant and susceptible inoculated samples, with a shorter distance between the resistant uninoculated and inoculated samples compared to the susceptible variety. This finding aligns with Mohammadbagheri et al. ( 2021 ), who reported distinct genetic groups in bell pepper in response to P. capsici, and with Zhang et al. ( 2002 ) regarding P. infestans in tomato. A total of 8,561 genes were differentially expressed between uninoculated and inoculated resistant interspecific hybrid (IIHRPBIH9) betelvine samples. Of these, 4,619 genes were up-regulated, and 3,942 genes were down-regulated. Among them, 1,635 and 2,342 genes were highly expressed as up- and down-regulated, respectively. In the uninoculated and inoculated susceptible variety (IIHRBVP170), 10,188 genes were differentially expressed, with 5,543 genes up-regulated and 4,645 genes down-regulated. Among these, 3,344 and 1,681 genes were highly expressed as up- and down-regulated, respectively. A total of 16,547 genes were differentially expressed among the samples of susceptible inoculated and resistant inoculated plants, with 9,010 genes up-regulated and 7,537 genes down-regulated. Additionally, 4,962 and 2,713 genes were uniquely expressed as up- and down-regulated, respectively. These results are consistent with Rabuma et al. ( 2022 ), who identified 57 DEGs related to defense response in bell pepper, and with Hao et al. ( 2016 ), who found higher expression levels of transcripts in resistant P. flaviflorum compared to susceptible P. nigrum L. in response to P. capsici in black pepper. Similar findings were reported by Song et al. ( 2022 ) in tobacco's response to P. nicotianae. A Venn diagram recorded 19,263 DEGs between uninoculated and inoculated resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170) of betelvine. Specifically, 8,561 genes were found between RU versus RI, 10,188 genes in SU versus SI, 16,547 genes in SI versus RI, and 12,083 genes in SU versus RU. In all four comparisons, 1,635, 3,344, 4,962, and 1,500 up-regulated genes were unique to RU versus RI, SU versus SI, SI versus RI, and SU versus RU, respectively, with 71 common DEGs across all four comparisons. Similarly, 2,342, 1,681, 2,713, and 1,086 down-regulated genes were unique to RU versus RI, SU versus SI, SI versus RI, and SU versus RU, respectively, with 202 common DEGs. These results align with Rabuma et al. ( 2022 ), who reported common DEGs in leaf samples of C. annum L., and with Song et al. ( 2022 ) in tobacco. Gene ontology (GO) enrichment analysis was performed on four groups of differentially expressed genes: RU versus RI, SU versus SI, SI versus RI, and SU versus RU. GO annotation was conducted for all three GO terminologies: biological process, molecular function, and cellular component. In the biological process category, the most frequent GO enrichments were "anatomical structure development, response to external stimulus, and response to stress." In molecular functions, "protein binding and signaling receptor activity" were most enriched. In the cellular component category, the predominant terms were "chloroplast, cytoplasm, cytosol, followed by plasma membrane." These results are consistent with Rabuma et al. ( 2022 ), who reported similar dominant GO terms in C. annum L., and with Dia et al. ( 2017 ), who highlighted the plant defense mechanism's role in recognizing cellular signal transduction in response to biological processes. Yoo and Sheen ( 2008 ) also emphasized the importance of plant hormone signal transduction in defense response. Volcano plots were used to visualize the distribution of up- and down-regulated genes between resistant, susceptible uninoculated, and inoculated samples. Key genes identified in this study included peroxidase 5-like protein, CRT-like transporters, disease resistance-responsive proteins, disease resistance protein (TIR-NBS-LRR class) family, pathogenesis-related proteinase inhibitor family, pathogenesis-related protein 1-like protein, cytochrome P450-like protein, and ubiquitin-like superfamily protein, which were differentially up-regulated. Conversely, genes like pathogenesis-related protein 2-like protein, LRR receptor-like serine/threonine-kinase, cotton fiber protein, and UDP-glucosyltransferase 76B1-like protein were differentially down-regulated against P. nicotianae infection in both the resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170). Boava et al. ( 2011 ) similarly identified pathogenesis-related genes involved in citrus defense against P. parasitica. KEGG pathway analysis revealed that most DEGs in the RU versus RI comparison were significantly enriched in defense-related compound synthesis pathways, including superoxide radicals degradation, jasmonic acid biosynthesis, flavonoid biosynthesis, ethylene biosynthesis I, and phenylalanine biosynthesis. These pathways respond to various environmental signals (biotic and abiotic stress), increasing essential enzymes' activity in the phenylpropanoid pathway. These findings align with previous work by Li et al. ( 2020 ) in C. annum L., who reported significant enrichment in secondary metabolite biosynthesis pathways. Wang et al. ( 2015 ) also highlighted the importance of secondary metabolites and terpenoids in response to P. capsici , and Jin Kim and Kook Hwang ( 2000 ) emphasized the role of ethylene biosynthesis pathways in defense. A heat map representing the expression of defense-related genes in Phytophthora uninoculated and inoculated resistant interspecific hybrid and susceptible variety showed significant up-regulation of genes like pathogenesis-related protein PRB1-3, disease resistance responsive protein, and peroxidase 5-like protein in the resistant inoculation sample. Conversely, TIR-NBS-LRR class proteins and thaumatin-like protein 1 family were down-regulated. In the comparison between SU versus SI, genes like putative receptor-like protein kinase, heat shock protein 70 (HSP 70) family protein, and pathogenesis-related protein 2-like protein (PR 2) were up-regulated in the susceptible inoculated sample, while NB-ARC domain-containing disease resistance proteins were downregulated in the uninoculated susceptible sample. The expression level between SI and RI showed up-regulation of genes like glucan endo-1, 3-beta-glucosidase and pathogenesis-related protein 1-like protein, whereas pathogenesis-related protein PR2-like protein, LRR, and NB-ARC domains containing disease resistance protein, and galactose-binding protein isoform-1 were down-regulated in the resistant inoculation sample. Zhang et al. ( 2017 ) also reported similar up-regulation of heat shock protein HSP70 and PHE ammonia lyase 1 in soybean's defense against P. sojae. Conclusion Our study used RNA-seq and advanced bioinformatics tools to understand how betelvine responds to Phytophthora nicotianae . We found many differentially expressed genes and pathways involved in defense, both in resistant (IIHRPBIH9) and susceptible (IIHRBV170) varieties. Notable differences were seen between inoculated and uninoculated samples, and between resistant and susceptible variety, highlighting the complex interaction between plant and pathogen. Principal Component Analysis showed distinct sample clustering based on infection status and genetics, revealing significant transcriptional changes due to Phytophthora infection. Key defense genes like peroxidase 5-like protein and pathogenesis-related proteins showed significant up-regulation, aligning with previous research highlighting their importance in plant defense. Gene Ontology and KEGG pathway analyses revealed stress response, cellular processes, and defense-related pathways like superoxide radicals degradation and jasmonic acid biosynthesis as crucial during Phytophthora infection. Validation via qRT-PCR confirmed the up-regulation of selected defense genes, supporting our transcriptomic analysis. Although minor differences were observed between RNA-seq and qRT-PCR, our study provides a comprehensive understanding of betelvine defense mechanisms against Phytophthora. This insight can guide targeted strategies for enhancing disease resistance in this economically important crop. Further research is needed to explore the functional roles of identified genes and pathways, aiding their incorporation into breeding programs aimed at bolstering betelvine resilience to Phytophthora diseases. Materials and Methods Pathogen infection and symptom development Betelvine interspecific resistant hybrid IIHRPBIH9 and susceptible variety IIHRBV170 were used in this study. The culture of P. nictotianae was maintained on carrot agar (CA) medium in the petriplates by culturing mycelial disc previously grown culture to obtain a pure culture of pathogen (Mohankumar et al., 2022 ). Observations made for the disease development on susceptible variety leaves in control condition. RNA was isolated from leaf samples at 0 hai and 72 hai from both resistant interspecific hybrid and susceptible variety. RNA sample preparation Total RNA was extracted using RNAiso Plus reagent from Himedia (#Cat. No. MB566) as per manufacturer’s instructions. RNA samples were used to generate RNA-seq data and for transcriptome analysis. RNA quantification and quality checking was carried out thorough an Agilent 2100 Bioanalyzer (Agilent Technologies Pvt. Ltd) with RNA integrity number of > 6.0. RNA sequencing Trough using Illumina NovoSeq 6000 next generation sequencing platform the cDNA libraries were sequenced. Raw reads were quality checked based on Phred scores > 20 (NGSQC Tool Kit) and high quality (HQ) reads were filtered. De bruijn graph-based Trinity Assembler optimised for Illumina paired-end data was used to map the sequences to the pooled de-novo transcriptome assembly using a (Hao et al., 2016 ). DEGs analysis DESeq software (McDermaid et al., 2019 ) based on R program was used to analyse the differential expression of the expressed transcripts. 2-fold or greater up or down regulation of transcripts (p value of ≤ 0.05) were considered as significant DEGs. Hierarchical clustering of DEGs was carried out through Cluster 3.0 software (bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) and tree was generated using Java Tree View software ( http://jtreeview.sourceforge.net ). Annotation and enrichment analysis of DEGs Unique transcripts (> 200 bp) were annotated and identified through search in NCBI non-redundant (nr) and PlantGDB; Plant Genome Database ( http://www.plantgdb.org/ ) (Deng et al ., 2006). HQ filtered reads were mapped sing Bowtie 2 (Fan et al., 2022 ) and expression level of transcripts were assessed. Reads per kilo base per million (RPKM) method was used to normalise the mapped reads. Gene ontology and KEGG pathways were identified by using GO Elite tool (FDR < 0.05). Unigenes were identified by GO Elite tool (Kuleshov et al., 2016 ) were used as input for Biological Analysis Network (BAN). Genes were clusted using Cytoscape v2.8.3 software (Cui et al., 2017 ) and processed using edge weighted force directed (Bio-layout). qRT-PCR validation The selected candidate genes were validated through qRT PCR assay. The total RNA was used to sytntsise cDNA using cDNA synthesis kit (Takara Bio USA Inc) for validation assays Actin gene was used as internal control (Hu et al., 2015 ). For the selected candidate gene, primers sets were designed (Primer Express software version 3.0.1) (Table 2 ). qRT-PCR assay was performed in independent samples with three replicates using the SYBR Green Quantitative RT-qPCR Kit (USA Biolab). Table 2 Primer sequences of pathogenesis and defence related genes of betelvine used for differential expression analysis using qRT-PCR Sl. No. Gene name Sequence Start Tm GC % Amplicon length (bp) 1 Peroxidase 5-like protein (POX) F-CCCTGTGGACACTGCTGCTT 526 60 60 99 Peroxidase 5-like protein (POX) R-CTTCGCAAACCGGCTCTACA 624 60 51 2 Pathogenesis-related protein 1 (PR-1) F-TGGCACCGTCAAGAAATTCA 328 59 48 147 Pathogenesis-related protein 1 (PR-1) R-CCCCAATCCACCCACCTT 474 59 52 3 Disease resistance-responsive family protein (DR) F-CAACCAAAGAAATCAGCATCCA 132 59 41 102 Disease resistance-responsive family protein (DR) R-GGTTGAGCGCCATTGTTGA 233 59 53 4 Leucine-rich repeat protein (LRR) F-AGAAGGGAGGCAAGCGACAT 140 60 55 108 Leucine-rich repeat protein (LRR) R-AGCAGGCAGTGAACACAAACC 247 59 52 5 Glucan endo-1,3-beta-glucosidase protein (GE) F-CGGCAGTTGACACCTTGATG 135 59 53 143 Glucan endo-1,3-beta-glucosidase protein (GE) R-GGCTGTTCGATTCCGGTACA 277 60 55 6 WRKY-75 F-GCCTCGTTATGCTTTTCAGACA 253 58 45 143 WRKY-75 R-TCACCGTGCACCCTTGGT 395 60 61 Declarations Acknowledgements I would like to express my sincere gratitude to ICAR-IIHR, Bengaluru and UHS, Bagalkot for providing me with the opportunity and resource to pursue this Ph.D research. My deepest thanks go to my guide (Dr. Hima Bindu K) and research advisors, for their invaluable support, guidance, and encouragement throughout this journey. Their expertise and insightful feedback have been critical in shaping the direction and quality of my work. Author contributions The research presented in this thesis was primarily conducted and written by Mohankumar, G.P. The author performed the experiments, data collection and analysis, and interpreted results. Hima Bindu, K, and Sriram, S: Designed the experiments, provided valuable guidance in the conceptualization and Supervision of the study, and assisted with the statistical analysis. Ravishankar K V, Malali Gowda, Umesha, K, Jagadeesha, R.C: Writing- review and editing. Mohankumar, G.P, and Hima Bindu, K: Drafted the manuscript. All authors discussed the results and edited the manuscript. Data availability statement The datasets generated and analysed in this study are available in NCBI database under accession number: PRJNA1338695 and ID: 1338695. Funding This research was supported and funded by ICAR-AICRP MAP and B (Scheme No. 2502). Additional information Competing interests The authors declare that there are no conflicts of interests. Supplementary Information Correspondence and material requests should be addressed to Hima Bindu, K., or Mohankumar, G.P. References Boava, L. P. et al. Expression of defense-related genes in response to mechanical wounding and Phytophthora parasitica infection in Poncirus trifoliata and Citrus sunki. Physiol. Mol. Plant Pathol. 76 (2), 119-125 (2011). Choi, K. & Ratner, N. iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform. BMC genomics . 20 , 1-7 (2019). Chris, C. N. S. & Frank, L. W. T. Susceptibility genes 101: how to be a good host. Annu. Rev. Phytopathol . 52 , 551–581 (2014). Cui, J. et al. Comparative transcriptome analysis between resistant and susceptible tomato allows the identification of lnc RNA 16397 conferring resistance to Phytophthora infestans by co‐expressing glutaredoxin. Plant J . 89 (3), 577-589 (2017). Dasgupta, B. et al. Phytophthora diseases of betelvine ( Piper betle L.): a menace to betelvine crop. SMRC J. Agricul. 6 (1), 71-89 (2008). Dasgupta, B., Roy, J. K. & Sen, C. Two major fungal diseases of betelvine. Diseases of Plantation Crops, Spices, Betelvine and Mulberry . 133-137 (2000). Dia, Q. et al. Comparative transcriptome analysis of the different tissues between the cultivated and wild tomato. PLoS ONE. 12 (3), 2-18 (2017). Fan, R. et al. Comparative transcriptome and metabolome analysis of resistant and susceptible Piper species upon infection by the oomycete Phytophthora Capsici . Front. Plant Sci. 13 , 1-15 (2022). Glemin, S., Baszin, E. & Charlesworth, D. Impact of mating systems on patterns of sequence polymorphism in flowering plants. Proceedings of the Royal Society Series B-Biological Sci. 273 , 3011-3019 (2006). Hammond-Kosack, K. E. & Kanyuka, K. Resistance genes (R genes) in plants. In Encyclopedia of Life Sciences: Wiley . 1-21 (2007). Hao, C. et al. De novo transcriptome sequencing of black pepper ( Piper nigrum L.) and an analysis of genes involved in phenylpropanoid metabolism in response to Phytophthora capsici . BMC genomics. 17 (1), 1-14 (2016). Hu, L. et al . De novo assembly and characterization of fruit transcriptome in black pepper ( Piper nigrum L.). PLoS One. 10 (6), 1-14 (2015). Jin Kim, Y. & Kook Hwang, B. Pepper gene encoding a basic pathogenesis‐related 1 protein is pathogen and ethylene inducible. Physiol. Plant . 108 (1), 51-60 (2000). Jones, J. D. G. & Dangl, J. L. The plant immune system. Nature . 444 , 323–329 (2006). Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server. Nucleic Acids Res. 44 (1), 90-97 (2016). Li, Y. et al. The dynamic transcriptome of pepper ( Capsicum annuum ) whole roots reveals an important role for the phenylpropanoid biosynthesis pathway in root resistance to Phytophthora capsici . Gene , 728 , 144288 (2020). Malik, N. & George, J. K. Resistance genes in Piper colubrinum : In silico survey from leaf transcriptome and expression studies upon challenge inoculation with Phytophthora capsici . Appl. Biochem. Biotechnol. 184 (3), 987- 1008 (2018). McDermaid, A. et al . Interpretation of differential gene expression results of RNA-seq data: review and integration. Briefings in bioinformatics . 20 (6), 2044-2054 (2019). Meng, H. et al. Comparative transcriptome analysis reveals resistant and susceptible genes in tobacco cultivars in response to infection by Phytophthora nicotianae . Sci. Rep. 11 (1), 1-13 (2021). Mohammadbagheri, L. et al. Genetic diversity and biochemical analysis of Capsicum annuum (Bell pepper) in response to root and basal rot disease, Phytophthora capsici . Phytochem . 190 , 112884 (2021). Mohankumar, G. P. et al. Screening of betelvine inter-specific hybrids for resistance to Phytophthora nicotianae . Pest Management in Horticultural Ecosystems . 28 (2), 153-159 (2022). Purseglove, J. W. et al. Spices . London and New York: Longman. 1 , 1-439 (1981). Rabuma, T. et al. Integrative RNA-Seq analysis of Capsicum annuum L.- Phytophthora capsici pathosystem reveals molecular cross-talk and activation of host defence response. Physiol. Mol. Biol. Plants . 28 (1), 171-188 (2022). Song, R. et al . Unraveling the expression of differentially expressed proteins and enzymatic activity in response to Phytophthora nicotianae across different flue-cured tobacco cultivars. BMC Microbiol . 22 (1), 1-13 (2022). Trapnell, C., Williams, B. & Pertea, G. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol . 28 , 511–515 (2010). Turner, G. J. Resistance in Piper species and other plants to infection by Phytophthora palmivora from Piper nigrum . Trans. Br. Mycol. Soc. 57 (1), 61–66 (1971). Wang, P. et al. Identification and expression analysis of candidate genes associated with defense responses to Phytophthora capsici in pepper line “PI 201234”. Int. J. Mol. Sci. 16 (5), 11417- 11438 (2015). Yoo, S. D. & Sheen, J. MAPK signaling in plant hormone ethylene signal transduction. Plant Signal. Behav . 3 , 848–849 (2008). Zaidi, S. S. E. A., Mukhtar, M. S. & Mansoor, S. Genome editing: targeting susceptibility genes for plant disease resistance. Trends Biotechnol . 36 , 898–906 (2018). Zhang, C. et al. Phenylalanine ammonia-lyase2.1 contributes to the soybean response towards Phytophthora sojae infection. Sci. Rep . 7 , 1-13 (2017). Zhang, M., Liu, X. & O’Neill, M. Spectral discrimination of Phytophthora infestans infection on tomato based on principal component and cluster analyses. Int. J. Remote Sens . 23 (6), 1095–1107 (2002). Additional Declarations No competing interests reported. Supplementary Files Supplymentaryfigures.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":280258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram showing the commonly (a) up- regulated and (b) down- regulated differentially expressed genes amongst the resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBVP170) in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/8b70cb40af37b499d749b69c.png"},{"id":96340161,"identity":"6b5adf4e-23b1-481e-85ce-ffa5ed56b8d3","added_by":"auto","created_at":"2025-11-20 04:17:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) classification of differentially expressed genes: (a) Resistant (IIHRPBIH9) uninoculated and resistant inoculated, (b) Susceptible (IIHRBVP170) uninoculated and susceptible inoculated, (c) Susceptible inoculated and resistant inoculated, (d) Susceptible uninoculated and resistant uninoculated samples of betelvine in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/ad8831614e817cbbd7d46cde.png"},{"id":96340178,"identity":"2b4f7393-7bac-4ead-afd2-6e3134d0f236","added_by":"auto","created_at":"2025-11-20 04:17:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot of differentially expressed genes: (a) Resistant (IIHRPBIH9) uninoculated and resistant inoculated, (b) Susceptible (IIHRBVP170) uninoculated and susceptible inoculated, (c) Susceptible inoculated and resistant inoculated, (d) Susceptible uninoculated and resistant uninoculated samples of betelvine in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/e1e715df401f646384fe4d05.png"},{"id":96366208,"identity":"8245bef2-5124-4231-9e19-12d85b7f9679","added_by":"auto","created_at":"2025-11-20 10:11:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":220456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG Pathways enrichment analysis plot of differentially expressed genes: (a) Resistant (IIHRPBIH9) uninoculated and resistant inoculated, (b) Susceptible (IIHRBVP170) uninoculated and susceptible inoculated, (c) Susceptible inoculated and resistant inoculated samples of betelvine in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/3dea3cdf6a66edd676ee3bb2.png"},{"id":96367017,"identity":"9dd0286c-00d7-4e42-bc85-9dfb29f2a0bd","added_by":"auto","created_at":"2025-11-20 10:12:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":295196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat map of Pearson correlation between expression levels of genes: (a) Resistant (IIHRPBIH9) uninoculated and resistant inoculated (b) Susceptible inoculated and resistant inoculated samples of betelvine in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/dee553457813322f6d7b6c64.png"},{"id":96367248,"identity":"b905da38-f8d4-43a8-a953-4633a43bd989","added_by":"auto","created_at":"2025-11-20 10:12:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":246733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytoscape network visualization of differentially expressed gene(s): (a) Resistant (IIHRPBIH9) uninoculated and resistant inoculated (b) Susceptible inoculated and resistant inoculated samples of betelvine in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePhytophthora nicotianae\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/595cff3ca8af694653fdfb37.png"},{"id":103381335,"identity":"33865242-85cc-4a27-979c-4e385ec6d2bc","added_by":"auto","created_at":"2026-02-25 05:41:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2824322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/5eac06de-d722-416c-b74e-b70b732bd176.pdf"},{"id":96366855,"identity":"34badb65-c787-48fd-8e28-dd5a7e01adc9","added_by":"auto","created_at":"2025-11-20 10:11:57","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":874496,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymentaryfigures.doc","url":"https://assets-eu.researchsquare.com/files/rs-7634383/v1/fbe90a148d2d9f6e0cd287b4.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic profiling of interspecific hybrid in response to Phytophthora nicotianae infection in betelvine","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003ePhytophthora nicotianae\u003c/em\u003e, a typical oomycete, is known for its broad host range (Meng et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The pathogen attacks the plant at the collar region and below the soil. Microclimate with humid and moist shade acts as a favorable condition for both the growth of betelvine and \u003cem\u003eP. nicotianae.\u003c/em\u003e The characteristic symptom of disease is the disappearance of lustre of leaves, followed by wilting and drooping of vine due to loss of chlorophyll, ultimately leading to drying up of vine, meanwhile underground parts of the plant rot completely. The extent of losses may vary from 5 to 90 per cent due to foot rot (Dasgupta et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and 20 to 40 per cent in case of leaf rot, leading to almost total crop failure (Dasgupta et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBetelvine is a vegetatively propagated crop, which preserves high level of heterozygosity that is necessary for more hybrid vigor and any favourable spontaneous and induced mutants can easily be identified and propagated (Glemin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Unfortunately few countable efforts have been made till now in identifying resistant sources or developing resistant hybrids for foot rot.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePiper colubrinum\u003c/em\u003e is a distant relative of cultivated betelvine, showing high resistance to \u003cem\u003ePhytophthora\u003c/em\u003e foot rot. Understanding the resistance mechanism involved in pathogen interaction can be aided by knowledge of differential expression profiles. At ICAR-Indian Institute of Horticultural Research, Bengaluru, interspecific hybrids have been developed by crossing \u003cem\u003ePhytophthora\u003c/em\u003e resistant species \u003cem\u003eP. colubrinum\u003c/em\u003e (Turner, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1971\u003c/span\u003e and Purseglove et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) and \u003cem\u003eP. betle\u003c/em\u003e to develop resistant sources for this devastating disease.\u003c/p\u003e\u003cp\u003ePlants have evolved with induced defense responses, which rely on the recognition of host-pathogen recognition and its associated molecular like DAMPs, MAMPs and PAMPs and pathogen effectors such as PTI (PAMP-triggered immunity) and ETI (effector-triggered immunity) (Jones and Dangl, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Conversely, pathogens rely on host response to establish infection to express disease susceptibility genes aid mainly in infection of pathogen and disruption in these genes can lead to loss in host-pathogen compatibility (Chris and Frank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recently, there has been increased focus on studying identifying and utilizing susceptible genes in developing durable broad-spectrum resistance (Ziidi \u003cem\u003eet al\u003c/em\u003e., 2018). RNA-seq through exploring genomic tools are powerful tools which facilitate the identification of candidate genes for desired trait.\u003c/p\u003e\u003cp\u003eWe earlier screened 13 interspecific hybrids for \u003cem\u003eP. nicotianae\u003c/em\u003e infection and found IIHRPBIH9 identified as a resistant hybrid and the susceptible variety IIHRBVP170 as models (Mohankumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). RNA-seq was employed to analyze the gene expression profiles of leaves from both the resistant hybrid and susceptible variety at 0 and 72 hours after inoculation (hai) with \u003cem\u003eP. nicotianae\u003c/em\u003e. Differentially expressed genes in relation to \u003cem\u003eP. nicotianae\u003c/em\u003e infection were identified and key genes were validated. Our findings provide insights into the resistance mechanisms of betelvine response to the pathogen and provide a potentially valuable resource for future plant development.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eTranscriptome Sequencing and Assembly\u003c/h2\u003e\n \u003cp\u003eRNA sequencing was performed using the Illumina NovoSeq 6000 sequencer with 150 x 2 paired-end sequencing chemistry technique. RNA samples were taken from contrasting resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170) of betelvine, both inoculated and uninoculated leaf samples. A total of 6.62 GB of raw data was obtained. The number of raw reads per sample in KBs were 1992100, 1190220, 1503200, and 1930300 for susceptible uninoculated, susceptible inoculated, resistant uninoculated, and resistant inoculated samples, respectively.\u003c/p\u003e\n \u003cp\u003eThe reads were assembled and quality checked (FAST QC), followed by the identification of differentially expressed genes (DEGs), gene ontology (GO) analysis, and KEGG pathway analysis. Up-regulated and down-regulated genes were validated through qRT-PCR. Reads with \u0026ge;\u0026thinsp;70% bases having a quality score\u0026thinsp;\u0026ge;\u0026thinsp;Q20 were chosen for downstream analysis. Low-quality reads were removed, and the filtered reads were merged and subjected to primary transcriptome assembly, resulting in 97,658 transcripts with an NP50 of 965 bp. Post CD-HIT EST processing, the NP50 value improved to 1,152, and the total number of transcripts reduced to 27,774. The assembly was validated by aligning reads from both uninoculated and inoculated samples to the 27,774 transcripts using Bowtie 2, indicating an approximate transcriptome size of 23 MB. From the combined assembled RNAseq transcriptome data of both the resistant and susceptible samples, 16,329 contigs were obtained with a total length of 33,418,383 bp, the largest contig being around 32,759 bp and a GC content of 50.27 per cent (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of assembled transcriptome analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSl. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCombined Assembly\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContigs (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1000 bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal length (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1000 bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33418383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1666 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e767 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLargest contig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32759 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrincipal component analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePCA was performed using iGEAK (Choi and Ratner, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) to reduce data dimensionality and distinguish between the resistant and susceptible betelvine samples. The PCA results showed clear separation between resistant interspecific hybrid and susceptible variety of both inoculated and uninoculated samples, indicating substantial transcriptome variation in response to \u003cem\u003ePhytophthora\u003c/em\u003e infection. The largest distance observed was between resistant uninoculated and susceptible uninoculated samples, suggesting significant transcriptome differences (Supplementarty Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDifferential gene expression in response to infection\u003c/h3\u003e\n\u003cp\u003eCalculations of \u0026Delta;\u0026Delta;Ct values for individual genes based on the \u0026Delta;Ct values of susceptible, resistant, and inoculated samples obtained from their RPKM (reads per kilo base per million) values revealed that an average of 5,000 transcripts were differentially expressed in any one of the four comparisons performed at 72 hai. Between uninoculated and inoculated resistant interspecific hybrids, 8,561 genes were differentially expressed, with 4,619 up-regulated and 3,942 down-regulated. In the susceptible variety, 10,188 genes were differentially expressed, with 5,543 up-regulated and 4,645 down-regulated. Between susceptible inoculated and resistant inoculated samples, 16,547 genes were differentially expressed, with 9,010 up-regulated and 7,537 down-regulated (Supplementary Figs.\u0026nbsp;2, 3 \u0026amp; 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive analysis of differential gene expression, go enrichment, and pathway interactions in betelvine resistance to\u003c/strong\u003e \u003cstrong\u003eP. nicotianae\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eVenn diagram Distribution\u003c/h3\u003e\n\u003cp\u003eThe Venn diagram revealed significant differential gene expression in the resistant hybrid compared to the susceptible variety, indicating transcriptional changes related to disease resistance. Specifically, 19,263 genes were differentially expressed between uninoculated and inoculated resistant and susceptible betelvine. Notably, 8,561 genes were differentially expressed in resistant uninoculated (RU) versus resistant inoculated (RI), 10,188 in susceptible uninoculated (SU) versus susceptible inoculated (SI), 16,547 in susceptible inoculated versus resistant inoculated, and 12,083 in susceptible uninoculated versus resistant uninoculated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Unique up-regulated genes were: 1,635 for RU versus RI, 3,344 for SU versus SI, 4,962 for SI versus RI, and 1,500 for SU versus RU, with 71 common genes. Unique down-regulated genes were: 2,342 for RU versus RI, 1,681 for SU versus SI, 2,713 for SI versus RI, and 1,086 for SU versus RU, with 202 common genes.\u003c/p\u003e\n\u003ch3\u003eGene Ontology (GO) Analysis\u003c/h3\u003e\n\u003cp\u003eGO analysis predicted the functions of differentially expressed genes in four groups: RU versus RI, SU versus SI, SI versus RI, and SU versus RU using Blast2GO. GO annotation was performed for biological processes, molecular functions, and cellular components, revealing enriched terms in the betelvine transcriptome interaction with P. nicotianae. In the RU versus RI comparison, frequent biological processes included \u0026quot;other cellular process,\u0026quot; \u0026quot;anatomical structure development,\u0026quot; \u0026quot;response to external stimulus,\u0026quot; and \u0026quot;response to stress.\u0026quot; Frequent cellular components were \u0026quot;chloroplast,\u0026quot; \u0026quot;cytoplasm,\u0026quot; \u0026quot;cytosol,\u0026quot; and \u0026quot;plasma membrane,\u0026quot; and enriched molecular functions were \u0026quot;protein binding,\u0026quot; \u0026quot;signaling receptor activity,\u0026quot; and \u0026quot;lipid binding\u0026quot; (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb shows GO classification for SU versus SI, with \u0026quot;biosynthetic process\u0026quot; and \u0026quot;embryo development\u0026quot; as predominant biological processes, \u0026quot;nucleus,\u0026quot; \u0026quot;chloroplast,\u0026quot; \u0026quot;cytoplasm,\u0026quot; and \u0026quot;mitochondrion\u0026quot; as major cellular components, and \u0026quot;protein binding,\u0026quot; \u0026quot;molecular function,\u0026quot; \u0026quot;RNA binding,\u0026quot; and \u0026quot;DNA binding transcription factor activity\u0026quot; as key molecular functions. For SI versus RI (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec), biological processes included \u0026quot;other metabolic processes,\u0026quot; \u0026quot;anatomical structure development,\u0026quot; and \u0026quot;response to external stimulus.\u0026quot; Cellular components were \u0026quot;chloroplast,\u0026quot; \u0026quot;cytosol,\u0026quot; and \u0026quot;cytoplasm,\u0026quot; and molecular functions were \u0026quot;protein binding\u0026quot; and \u0026quot;signaling receptor activity.\u0026quot; In SU versus RU (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed), predominant GO terms were \u0026quot;biosynthetic process,\u0026quot; \u0026quot;chloroplast,\u0026quot; and \u0026quot;protein binding,\u0026quot; with \u0026quot;secondary metabolic process,\u0026quot; \u0026quot;peroxisome,\u0026quot; and \u0026quot;signaling receptor activity\u0026quot; being least frequent.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eVolcano Plots\u003c/h2\u003e\n \u003cp\u003eVolcano plots visualized the distribution of up- and down-regulated genes between conditions. Positive fold change indicates higher expression, while negative fold change indicates lower expression. Significant genes are at the top of the plot. In resistant interspecifc hybrid (IIHRPBIH9) versus susceptible variety (IIHRBV170), peroxidase 5-like protein and disease resistance-responsive proteins were up-regulated, while serine/threonine protein kinase and thaumatin superfamily proteins were down-regulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). In SC versus SI, disease resistance protein (TIR-NBS-LRR class) and calcium-binding EF-hand family protein were up-regulated, while concanavalin A-like lectin protein kinase family proteins were down-regulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). In SI versus RI, concanavalin A-like lectin protein kinase and pathogenesis-related protein 1-like protein were up-regulated, while pathogenesis-related protein 2-like protein and WRKY transcription factor family members were down-regulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). In SU versus RU, mannose-binding protein and ubiquitin-like superfamily protein were up-regulated, while UDP-glucosyltransferase 76B1-like protein was down-regulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eKEGG Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eKEGG databases, which include genomic, biological pathway, and disease information, were used to annotate DEGs and map them to pathways, revealing that DEGs in RU versus RI, SU versus SI, and SI versus RI comparisons were involved in several pathways. In RU versus RI, DEGs were significantly enriched in defense-related pathways like superoxide radicals degradation, jasmonic acid biosynthesis, flavonoid biosynthesis, and ethylene biosynthesis I, with the highest number in superoxide radicals degradation. Moderate enrichment was seen in L-glutamine biosynthesis II, phenylalanine biosynthesis, and abscisic acid biosynthesis, while brassinosteroids inactivation and ubiquinol-9 biosynthesis showed the least enrichment (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). In SU versus SI, pathways like jasmonic acid biosynthesis, superoxide radicals degradation, and glutathione-mediated detoxification II were enriched, with the most genes in glutathione-mediated detoxification II and the fewest in quercetin glycoside biosynthesis (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). For SI versus RI, the most enriched pathways were glutathione-mediated detoxification II, phenylpropanoid biosynthesis, and superoxide radicals degradation (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003ch3\u003eHeat Maps\u003c/h3\u003e\n\u003cp\u003eHeat maps with hierarchical clustering displayed top defense-related DEGs. In the resistant inoculated sample, peroxidase 5-like protein and pathogenesis-related protein PRB1-3 like protein were highly up-regulated, while thaumatin-like protein 1, disease resistance protein (TIR-NBS-LRR class) family, and lipid transfer protein 1 were was down-regulated (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). However, SI versus RI comparison (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), glucan endo-1, 3-beta-glucosidase, putative disease resistance protein (RGA1 \u0026amp; RGA4), and pathogenesis-related protein 1-like protein were up-regulated in the resistant inoculated sample. In contrast, LRR and NB-ARC domains containing disease resistance protein, pathogenesis-related protein PR2 like protein, and galactose-binding protein isoform-1 were down-regulated in the resistant inoculated sample during betelvine-\u003cem\u003eP. nicotianae\u003c/em\u003e interaction.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eCytoscape analysis revealed 8,561 DEGs between RU and RI, with common GO terms in biological processes and cellular components. Pathways related to catalytic activity, stress response, and secondary metabolism were connected, indicating their role in defense mechanisms (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). In the comparison of 16,547 DEGs between SI and RI, GO analysis categorized genes into biological process, cellular component, and molecular function, with pathways linked to peroxisome, endosome, signaling receptor activity, and biosynthetic process (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eValidation of Defense-Related Genes Using qRT-PCR\u003c/h2\u003e\n \u003cp\u003eValidation of RNA-seq results was performed using qRT-PCR for selected defense-related genes, including peroxidase 5-like protein (POX), pathogenesis-related protein 1-like protein (PR-1), dirigent-like protein (DRR), and LRR protein kinase. The expression patterns confirmed significant up-regulation of these genes in inoculated resistant samples compared to uninoculated ones. POX was up-regulated in most comparisons except SI vs. RI, where it was down-regulated. PR-1 showed significant up-regulation in RU vs. RI and SI vs. RI comparisons. DRR was consistently up-regulated across all comparisons, with RNA-seq showing higher log fold changes than qRT-PCR. LRR protein kinase exhibited significant up-regulation in RU vs. RI and SI vs. RI comparisons, with RNA-seq values exceeding those from qRT-PCR analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHigh-throughput RNA sequencing (RNA-seq) is a powerful tool for identifying differentially expressed genes, assessing their levels of expression, and elucidating their regulatory mechanisms. Transcriptome analysis is widely recognized for identifying plant growth or developmental stages that are both scientifically and economically significant. Consequently, it is essential for obtaining a genome-scale map that includes the structure of transcripts, their complexity, and expression levels under specific conditions (Trapnell et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In gene expression or RNA-seq analysis, numerous pathways are involved, although biotic stress pathways are predominantly focused on identifying disease resistance-related genes.\u003c/p\u003e\u003cp\u003eThe interspecific hybrid IIHRPBIH9 is considered a novel source of resistance to \u003cem\u003ePhytophthora\u003c/em\u003e foot rot due to its high level of resistance based on artificial screening. In contrast, another germplasm, IIHRBV170, which is susceptible to \u003cem\u003ePhytophthora\u003c/em\u003e foot rot disease, was also used for RNA-seq assay. To thoroughly understand the genes and pathways involved in betelvine response to \u003cem\u003eP. nicotianae\u003c/em\u003e infections, four samples were selected for transcriptome sequencing: both uninoculated and inoculated plants were analyzed to identify differential gene expression between the resistant interspecific hybrid and the susceptible check variety of betelvine.\u003c/p\u003e\u003cp\u003eThe expression pattern regulated by pathogen infection enables the identification of defense-related gene families involved in betelvine immune response. R protein-mediated resistance typically correlates with a hypersensitive (HR) reaction, leading to the death of both the pathogen and the surrounding plant tissue (Hammond-Kosack and Kanyuka, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Some R genes associated with disease responses include the WRKY transcription factor, CC-NBS-LRR type gene, TIR-NBS-LRR gene, and putative LRR domains (Malik and George, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing the Illumina HiSeq platform, a total of 6.62 GB of data was obtained. This included 1.99 GB from the uninoculated susceptible variety (IIHRBV170), 1.50 GB from the uninoculated resistant variety (IIHRPBIH9), 1.19 GB from the inoculated susceptible variety, and 1.93 GB from the inoculated resistant variety. A total of 25\u0026nbsp;million reads were generated for the susceptible uninoculated sample, 19\u0026nbsp;million reads for the resistant uninoculated sample, 14.8\u0026nbsp;million reads for the susceptible inoculated sample, and 24.40\u0026nbsp;million reads for the resistant inoculated sample.\u003c/p\u003e\u003cp\u003ePrincipal component analysis showed substantial variation between \u003cem\u003eP. nicotianae\u003c/em\u003e inoculated and uninoculated samples, clearly separating them into two groups, indicating transcriptome variation between the resistant interspecific hybrid (IIHRPBIH9) and the susceptible variety (IIHRBV170). The resistant uninoculated line exhibited the greatest distance from the susceptible uninoculated line, showing significant variation between them. A similar trend was observed in the resistant and susceptible inoculated samples, with a shorter distance between the resistant uninoculated and inoculated samples compared to the susceptible variety. This finding aligns with Mohammadbagheri et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported distinct genetic groups in bell pepper in response to P. capsici, and with Zhang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) regarding \u003cem\u003eP. infestans\u003c/em\u003e in tomato.\u003c/p\u003e\u003cp\u003eA total of 8,561 genes were differentially expressed between uninoculated and inoculated resistant interspecific hybrid (IIHRPBIH9) betelvine samples. Of these, 4,619 genes were up-regulated, and 3,942 genes were down-regulated. Among them, 1,635 and 2,342 genes were highly expressed as up- and down-regulated, respectively. In the uninoculated and inoculated susceptible variety (IIHRBVP170), 10,188 genes were differentially expressed, with 5,543 genes up-regulated and 4,645 genes down-regulated. Among these, 3,344 and 1,681 genes were highly expressed as up- and down-regulated, respectively. A total of 16,547 genes were differentially expressed among the samples of susceptible inoculated and resistant inoculated plants, with 9,010 genes up-regulated and 7,537 genes down-regulated. Additionally, 4,962 and 2,713 genes were uniquely expressed as up- and down-regulated, respectively. These results are consistent with Rabuma et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who identified 57 DEGs related to defense response in bell pepper, and with Hao et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who found higher expression levels of transcripts in resistant \u003cem\u003eP. flaviflorum\u003c/em\u003e compared to susceptible \u003cem\u003eP. nigrum\u003c/em\u003e L. in response to \u003cem\u003eP. capsici\u003c/em\u003e in black pepper. Similar findings were reported by Song et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in tobacco's response to \u003cem\u003eP. nicotianae.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA Venn diagram recorded 19,263 DEGs between uninoculated and inoculated resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170) of betelvine. Specifically, 8,561 genes were found between RU versus RI, 10,188 genes in SU versus SI, 16,547 genes in SI versus RI, and 12,083 genes in SU versus RU. In all four comparisons, 1,635, 3,344, 4,962, and 1,500 up-regulated genes were unique to RU versus RI, SU versus SI, SI versus RI, and SU versus RU, respectively, with 71 common DEGs across all four comparisons. Similarly, 2,342, 1,681, 2,713, and 1,086 down-regulated genes were unique to RU versus RI, SU versus SI, SI versus RI, and SU versus RU, respectively, with 202 common DEGs. These results align with Rabuma et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who reported common DEGs in leaf samples of \u003cem\u003eC. annum\u003c/em\u003e L., and with Song et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in tobacco.\u003c/p\u003e\u003cp\u003eGene ontology (GO) enrichment analysis was performed on four groups of differentially expressed genes: RU versus RI, SU versus SI, SI versus RI, and SU versus RU. GO annotation was conducted for all three GO terminologies: biological process, molecular function, and cellular component. In the biological process category, the most frequent GO enrichments were \"anatomical structure development, response to external stimulus, and response to stress.\" In molecular functions, \"protein binding and signaling receptor activity\" were most enriched. In the cellular component category, the predominant terms were \"chloroplast, cytoplasm, cytosol, followed by plasma membrane.\" These results are consistent with Rabuma et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who reported similar dominant GO terms in C. annum L., and with Dia et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who highlighted the plant defense mechanism's role in recognizing cellular signal transduction in response to biological processes. Yoo and Sheen (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) also emphasized the importance of plant hormone signal transduction in defense response.\u003c/p\u003e\u003cp\u003eVolcano plots were used to visualize the distribution of up- and down-regulated genes between resistant, susceptible uninoculated, and inoculated samples. Key genes identified in this study included peroxidase 5-like protein, CRT-like transporters, disease resistance-responsive proteins, disease resistance protein (TIR-NBS-LRR class) family, pathogenesis-related proteinase inhibitor family, pathogenesis-related protein 1-like protein, cytochrome P450-like protein, and ubiquitin-like superfamily protein, which were differentially up-regulated. Conversely, genes like pathogenesis-related protein 2-like protein, LRR receptor-like serine/threonine-kinase, cotton fiber protein, and UDP-glucosyltransferase 76B1-like protein were differentially down-regulated against \u003cem\u003eP. nicotianae\u003c/em\u003e infection in both the resistant interspecific hybrid (IIHRPBIH9) and susceptible variety (IIHRBV170). Boava et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) similarly identified pathogenesis-related genes involved in citrus defense against \u003cem\u003eP. parasitica.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eKEGG pathway analysis revealed that most DEGs in the RU versus RI comparison were significantly enriched in defense-related compound synthesis pathways, including superoxide radicals degradation, jasmonic acid biosynthesis, flavonoid biosynthesis, ethylene biosynthesis I, and phenylalanine biosynthesis. These pathways respond to various environmental signals (biotic and abiotic stress), increasing essential enzymes' activity in the phenylpropanoid pathway. These findings align with previous work by Li et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in \u003cem\u003eC. annum\u003c/em\u003e L., who reported significant enrichment in secondary metabolite biosynthesis pathways. Wang et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also highlighted the importance of secondary metabolites and terpenoids in response to \u003cem\u003eP. capsici\u003c/em\u003e, and Jin Kim and Kook Hwang (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) emphasized the role of ethylene biosynthesis pathways in defense.\u003c/p\u003e\u003cp\u003eA heat map representing the expression of defense-related genes in \u003cem\u003ePhytophthora\u003c/em\u003e uninoculated and inoculated resistant interspecific hybrid and susceptible variety showed significant up-regulation of genes like pathogenesis-related protein PRB1-3, disease resistance responsive protein, and peroxidase 5-like protein in the resistant inoculation sample. Conversely, TIR-NBS-LRR class proteins and thaumatin-like protein 1 family were down-regulated. In the comparison between SU versus SI, genes like putative receptor-like protein kinase, heat shock protein 70 (HSP 70) family protein, and pathogenesis-related protein 2-like protein (PR 2) were up-regulated in the susceptible inoculated sample, while NB-ARC domain-containing disease resistance proteins were downregulated in the uninoculated susceptible sample. The expression level between SI and RI showed up-regulation of genes like glucan endo-1, 3-beta-glucosidase and pathogenesis-related protein 1-like protein, whereas pathogenesis-related protein PR2-like protein, LRR, and NB-ARC domains containing disease resistance protein, and galactose-binding protein isoform-1 were down-regulated in the resistant inoculation sample. Zhang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also reported similar up-regulation of heat shock protein HSP70 and PHE ammonia lyase 1 in soybean's defense against \u003cem\u003eP. sojae.\u003c/em\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study used RNA-seq and advanced bioinformatics tools to understand how betelvine responds to \u003cem\u003ePhytophthora nicotianae\u003c/em\u003e. We found many differentially expressed genes and pathways involved in defense, both in resistant (IIHRPBIH9) and susceptible (IIHRBV170) varieties. Notable differences were seen between inoculated and uninoculated samples, and between resistant and susceptible variety, highlighting the complex interaction between plant and pathogen. Principal Component Analysis showed distinct sample clustering based on infection status and genetics, revealing significant transcriptional changes due to \u003cem\u003ePhytophthora\u003c/em\u003e infection. Key defense genes like peroxidase 5-like protein and pathogenesis-related proteins showed significant up-regulation, aligning with previous research highlighting their importance in plant defense. Gene Ontology and KEGG pathway analyses revealed stress response, cellular processes, and defense-related pathways like superoxide radicals degradation and jasmonic acid biosynthesis as crucial during \u003cem\u003ePhytophthora\u003c/em\u003e infection. Validation via qRT-PCR confirmed the up-regulation of selected defense genes, supporting our transcriptomic analysis. Although minor differences were observed between RNA-seq and qRT-PCR, our study provides a comprehensive understanding of betelvine defense mechanisms against Phytophthora. This insight can guide targeted strategies for enhancing disease resistance in this economically important crop. Further research is needed to explore the functional roles of identified genes and pathways, aiding their incorporation into breeding programs aimed at bolstering betelvine resilience to Phytophthora diseases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePathogen infection and symptom development\u003c/h2\u003e\u003cp\u003eBetelvine interspecific resistant hybrid IIHRPBIH9 and susceptible variety IIHRBV170 were used in this study. The culture of \u003cem\u003eP. nictotianae\u003c/em\u003e was maintained on carrot agar (CA) medium in the petriplates by culturing mycelial disc previously grown culture to obtain a pure culture of pathogen (Mohankumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Observations made for the disease development on susceptible variety leaves in control condition. RNA was isolated from leaf samples at 0 hai and 72 hai from both resistant interspecific hybrid and susceptible variety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eRNA sample preparation\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using RNAiso Plus reagent from Himedia (#Cat. No. MB566) as per manufacturer\u0026rsquo;s instructions. RNA samples were used to generate RNA-seq data and for transcriptome analysis. RNA quantification and quality checking was carried out thorough an Agilent 2100 Bioanalyzer (Agilent Technologies Pvt. Ltd) with RNA integrity number of \u0026gt;\u0026thinsp;6.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eRNA sequencing\u003c/h2\u003e\u003cp\u003eTrough using Illumina NovoSeq 6000 next generation sequencing platform the cDNA libraries were sequenced. Raw reads were quality checked based on Phred scores\u0026thinsp;\u0026gt;\u0026thinsp;20 (NGSQC Tool Kit) and high quality (HQ) reads were filtered. De bruijn graph-based Trinity Assembler optimised for Illumina paired-end data was used to map the sequences to the pooled \u003cem\u003ede-novo\u003c/em\u003e transcriptome assembly using a (Hao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDEGs analysis\u003c/h2\u003e\u003cp\u003eDESeq software (McDermaid et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) based on R program was used to analyse the differential expression of the expressed transcripts. 2-fold or greater up or down regulation of transcripts (p value of \u0026le;\u0026thinsp;0.05) were considered as significant DEGs. Hierarchical clustering of DEGs was carried out through Cluster 3.0 software (bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) and tree was generated using Java Tree View software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://jtreeview.sourceforge.net\u003c/span\u003e\u003cspan address=\"http://jtreeview.sourceforge.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eAnnotation and enrichment analysis of DEGs\u003c/h2\u003e\u003cp\u003eUnique transcripts (\u0026gt;\u0026thinsp;200 bp) were annotated and identified through search in NCBI non-redundant (nr) and PlantGDB; Plant Genome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.plantgdb.org/\u003c/span\u003e\u003cspan address=\"http://www.plantgdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Deng \u003cem\u003eet al\u003c/em\u003e., 2006). HQ filtered reads were mapped sing Bowtie 2 (Fan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and expression level of transcripts were assessed. Reads per kilo base per million (RPKM) method was used to normalise the mapped reads. Gene ontology and KEGG pathways were identified by using\u003c/p\u003e\u003cp\u003eGO Elite tool (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Unigenes were identified by GO Elite tool (Kuleshov et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were used as input for Biological Analysis Network (BAN). Genes were clusted using Cytoscape v2.8.3 software (Cui et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and processed using edge weighted force directed (Bio-layout).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eqRT-PCR validation\u003c/h2\u003e\u003cp\u003eThe selected candidate genes were validated through qRT PCR assay. The total RNA was used to sytntsise cDNA using cDNA synthesis kit (Takara Bio USA Inc) for validation assays Actin gene was used as internal control (Hu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For the selected candidate gene, primers sets were designed (Primer Express software version 3.0.1) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). qRT-PCR assay was performed in independent samples with three replicates using the SYBR Green Quantitative RT-qPCR Kit (USA Biolab).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences of pathogenesis and defence related genes of betelvine used for differential expression analysis using qRT-PCR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStart\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGC %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAmplicon length (bp)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeroxidase 5-like protein (POX)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-CCCTGTGGACACTGCTGCTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeroxidase 5-like protein (POX)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-CTTCGCAAACCGGCTCTACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathogenesis-related protein 1 (PR-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-TGGCACCGTCAAGAAATTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathogenesis-related protein 1 (PR-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-CCCCAATCCACCCACCTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisease resistance-responsive family protein (DR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-CAACCAAAGAAATCAGCATCCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisease resistance-responsive family protein (DR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-GGTTGAGCGCCATTGTTGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeucine-rich repeat protein (LRR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-AGAAGGGAGGCAAGCGACAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeucine-rich repeat protein (LRR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-AGCAGGCAGTGAACACAAACC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlucan endo-1,3-beta-glucosidase protein (GE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-CGGCAGTTGACACCTTGATG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlucan endo-1,3-beta-glucosidase protein (GE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-GGCTGTTCGATTCCGGTACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWRKY-75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-GCCTCGTTATGCTTTTCAGACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWRKY-75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-TCACCGTGCACCCTTGGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to express my sincere gratitude to ICAR-IIHR, Bengaluru and UHS, Bagalkot for providing me with the opportunity and resource to pursue this Ph.D research. My deepest thanks go to my guide (Dr. Hima Bindu K) and research advisors, for their invaluable support, guidance, and encouragement throughout this journey. Their expertise and insightful feedback have been critical in shaping the direction and quality of my work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research presented in this thesis was primarily conducted and written by Mohankumar, G.P. The author performed the experiments, data collection and analysis, and interpreted results. Hima Bindu, K, and Sriram, S: \u0026nbsp;Designed the experiments, provided valuable guidance in the conceptualization and Supervision of the study, and assisted with the statistical analysis. Ravishankar K V, Malali Gowda, Umesha, K, Jagadeesha, R.C: Writing- review and editing. Mohankumar, G.P, and Hima Bindu, K: Drafted the manuscript. All authors discussed the results and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed in this study are available in NCBI database under accession number: PRJNA1338695 and ID: 1338695.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported and funded by ICAR-AICRP MAP and B (Scheme No. 2502).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and material requests should be addressed to Hima Bindu, K., or Mohankumar, G.P.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBoava, L. P. \u003cem\u003eet al.\u003c/em\u003e Expression of defense-related genes in response to mechanical wounding and \u003cem\u003ePhytophthora parasitica\u003c/em\u003e infection in \u003cem\u003ePoncirus trifoliata\u003c/em\u003e and Citrus sunki. \u003cem\u003ePhysiol. Mol. Plant Pathol.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e(2), 119-125 (2011).\u003c/li\u003e\n \u003cli\u003eChoi, K. \u0026amp; Ratner, N. iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform. \u003cem\u003eBMC genomics\u003c/em\u003e. \u003cstrong\u003e20\u003c/strong\u003e, 1-7 (2019).\u003c/li\u003e\n \u003cli\u003eChris, C. N. S. \u0026amp; Frank, L. W. T. Susceptibility genes 101: how to be a good host. \u003cem\u003eAnnu. Rev. Phytopathol\u003c/em\u003e. \u003cstrong\u003e52\u003c/strong\u003e, 551\u0026ndash;581 (2014).\u003c/li\u003e\n \u003cli\u003eCui, J. \u003cem\u003eet al.\u003c/em\u003e Comparative transcriptome analysis between resistant and susceptible tomato allows the identification of lnc RNA 16397 conferring resistance to \u003cem\u003ePhytophthora infestans\u003c/em\u003e by co‐expressing glutaredoxin. \u003cem\u003ePlant J\u003c/em\u003e. \u003cstrong\u003e89\u003c/strong\u003e(3), 577-589 (2017).\u003c/li\u003e\n \u003cli\u003eDasgupta, B. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003ePhytophthora\u003c/em\u003e diseases of betelvine (\u003cem\u003ePiper betle\u003c/em\u003e L.): a menace to betelvine crop. \u003cem\u003eSMRC J. Agricul.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e(1), 71-89 (2008).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDasgupta, B., Roy, J. K. \u0026amp; Sen, C. Two major fungal diseases of betelvine. \u003cem\u003eDiseases of Plantation Crops, Spices, Betelvine and Mulberry\u003c/em\u003e. 133-137 (2000).\u003c/li\u003e\n \u003cli\u003eDia, Q. \u003cem\u003eet al.\u003c/em\u003e Comparative transcriptome analysis of the different tissues between the cultivated and wild tomato. \u003cem\u003ePLoS ONE.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e(3), 2-18 (2017).\u003c/li\u003e\n \u003cli\u003eFan, R. \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003eComparative transcriptome and metabolome analysis of resistant and susceptible \u003cem\u003ePiper\u003c/em\u003e species upon infection by the oomycete \u003cem\u003ePhytophthora Capsici\u003c/em\u003e. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1-15 (2022).\u003c/li\u003e\n \u003cli\u003eGlemin, S., Baszin, E. \u0026amp; Charlesworth, D. Impact of mating systems on patterns of sequence polymorphism in flowering plants. \u003cem\u003eProceedings of the Royal Society Series B-Biological Sci.\u003c/em\u003e \u003cstrong\u003e273\u003c/strong\u003e, 3011-3019 (2006).\u003c/li\u003e\n \u003cli\u003eHammond-Kosack, K. E. \u0026amp; Kanyuka, K. Resistance genes (R genes) in plants. \u003cem\u003eIn Encyclopedia of Life Sciences: Wiley\u003c/em\u003e. 1-21 (2007).\u003c/li\u003e\n \u003cli\u003eHao, C. \u003cem\u003eet al.\u003c/em\u003e De novo transcriptome sequencing of black pepper (\u003cem\u003ePiper nigrum\u003c/em\u003e L.) and an analysis of genes involved in phenylpropanoid metabolism in response to \u003cem\u003ePhytophthora capsici\u003c/em\u003e. \u003cem\u003eBMC genomics.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e(1), 1-14 (2016).\u003c/li\u003e\n \u003cli\u003eHu, L. \u003cem\u003eet al\u003c/em\u003e. De novo assembly and characterization of fruit transcriptome in black pepper (\u003cem\u003ePiper nigrum\u003c/em\u003e L.). \u003cem\u003ePLoS One.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e(6), 1-14 (2015).\u003c/li\u003e\n \u003cli\u003eJin Kim, Y. \u0026amp; Kook Hwang, B. Pepper gene encoding a basic pathogenesis‐related 1 protein is pathogen and ethylene inducible. \u003cem\u003ePhysiol. Plant\u003c/em\u003e. \u003cstrong\u003e108\u003c/strong\u003e(1), 51-60 (2000).\u003c/li\u003e\n \u003cli\u003eJones, J. D. G. \u0026amp; Dangl, J. L. The plant immune system. \u003cem\u003eNature\u003c/em\u003e. \u003cstrong\u003e444\u003c/strong\u003e, 323\u0026ndash;329 (2006).\u003c/li\u003e\n \u003cli\u003eKuleshov, M. V. \u003cem\u003eet al.\u003c/em\u003e Enrichr: a comprehensive gene set enrichment analysis web server. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e(1), 90-97 (2016).\u003c/li\u003e\n \u003cli\u003eLi, Y. \u003cem\u003eet al.\u003c/em\u003e The dynamic transcriptome of pepper (\u003cem\u003eCapsicum annuum\u003c/em\u003e) whole roots reveals an important role for the phenylpropanoid biosynthesis pathway in root resistance to \u003cem\u003ePhytophthora capsici\u003c/em\u003e. \u003cem\u003eGene\u003c/em\u003e, \u003cstrong\u003e728\u003c/strong\u003e, 144288 (2020).\u003c/li\u003e\n \u003cli\u003eMalik, N. \u0026amp; George, J. K. Resistance genes in \u003cem\u003ePiper colubrinum\u003c/em\u003e: In silico survey from leaf transcriptome and expression studies upon challenge inoculation with \u003cem\u003ePhytophthora capsici\u003c/em\u003e. \u003cem\u003eAppl. Biochem. Biotechnol.\u003c/em\u003e \u003cstrong\u003e184\u003c/strong\u003e(3), 987- 1008 (2018).\u003c/li\u003e\n \u003cli\u003eMcDermaid, A. \u003cem\u003eet al\u003c/em\u003e. Interpretation of differential gene expression results of RNA-seq data: review and integration. \u003cem\u003eBriefings in bioinformatics\u003c/em\u003e. \u003cstrong\u003e20\u003c/strong\u003e(6), 2044-2054 (2019).\u003c/li\u003e\n \u003cli\u003eMeng, H. \u003cem\u003eet al.\u003c/em\u003e Comparative transcriptome analysis reveals resistant and susceptible genes in tobacco cultivars in response to infection by \u003cem\u003ePhytophthora nicotianae\u003c/em\u003e. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e(1), 1-13 (2021).\u003c/li\u003e\n \u003cli\u003eMohammadbagheri, L. \u003cem\u003eet al.\u003c/em\u003e Genetic diversity and biochemical analysis of \u003cem\u003eCapsicum annuum\u003c/em\u003e (Bell pepper) in response to root and basal rot disease, \u003cem\u003ePhytophthora capsici\u003c/em\u003e. \u003cem\u003ePhytochem\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;190\u003c/strong\u003e, 112884 (2021).\u003c/li\u003e\n \u003cli\u003eMohankumar, G. P. \u003cem\u003eet al.\u003c/em\u003e Screening of betelvine inter-specific hybrids for resistance to \u003cem\u003ePhytophthora nicotianae\u003c/em\u003e. \u003cem\u003ePest Management in Horticultural Ecosystems\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;28\u003c/strong\u003e(2), 153-159 (2022).\u003c/li\u003e\n \u003cli\u003ePurseglove, J. W. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eSpices\u003c/em\u003e. London and New York: Longman. \u003cstrong\u003e1\u003c/strong\u003e, 1-439 (1981).\u003c/li\u003e\n \u003cli\u003eRabuma, T. \u003cem\u003eet al.\u003c/em\u003e Integrative RNA-Seq analysis of \u003cem\u003eCapsicum annuum\u003c/em\u003e L.-\u003cem\u003ePhytophthora capsici\u003c/em\u003e pathosystem reveals molecular cross-talk and activation of host defence response. \u003cem\u003ePhysiol. Mol. Biol. Plants\u003c/em\u003e. \u003cstrong\u003e28\u003c/strong\u003e(1), 171-188 (2022).\u003c/li\u003e\n \u003cli\u003eSong, R. \u003cem\u003eet al\u003c/em\u003e. Unraveling the expression of differentially expressed proteins and enzymatic activity in response to \u003cem\u003ePhytophthora nicotianae\u0026nbsp;\u003c/em\u003eacross different flue-cured tobacco cultivars. \u003cem\u003eBMC Microbiol\u003c/em\u003e. \u003cstrong\u003e22\u003c/strong\u003e(1), 1-13 (2022).\u003c/li\u003e\n \u003cli\u003eTrapnell, C., Williams, B. \u0026amp; Pertea, G. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. \u003cem\u003eNat. Biotechnol\u003c/em\u003e. \u003cstrong\u003e28\u003c/strong\u003e, 511\u0026ndash;515 (2010).\u003c/li\u003e\n \u003cli\u003eTurner, G. J. Resistance in \u003cem\u003ePiper\u003c/em\u003e species and other plants to infection by \u003cem\u003ePhytophthora palmivora\u003c/em\u003e from \u003cem\u003ePiper nigrum\u003c/em\u003e. Trans. Br. Mycol. Soc. \u003cstrong\u003e57\u003c/strong\u003e(1), 61\u0026ndash;66 (1971).\u003c/li\u003e\n \u003cli\u003eWang, P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Identification and expression analysis of candidate genes associated with defense responses to \u003cem\u003ePhytophthora capsici\u003c/em\u003e in pepper line \u0026ldquo;PI 201234\u0026rdquo;. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e(5), 11417- 11438 (2015).\u003c/li\u003e\n \u003cli\u003eYoo, S. D. \u0026amp; Sheen, J. MAPK signaling in plant hormone ethylene signal transduction. \u003cem\u003ePlant Signal. Behav\u003c/em\u003e. \u003cstrong\u003e3\u003c/strong\u003e, 848\u0026ndash;849 (2008).\u003c/li\u003e\n \u003cli\u003eZaidi, S. S. E. A., Mukhtar, M. S. \u0026amp; Mansoor, S. Genome editing: targeting susceptibility genes for plant disease resistance. \u003cem\u003eTrends Biotechnol\u003c/em\u003e. \u003cstrong\u003e36\u003c/strong\u003e, 898\u0026ndash;906 (2018).\u003c/li\u003e\n \u003cli\u003eZhang, C. \u003cem\u003eet al.\u003c/em\u003e Phenylalanine ammonia-lyase2.1 contributes to the soybean response towards \u003cem\u003ePhytophthora sojae\u003c/em\u003e infection. \u003cem\u003eSci. Rep\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;7\u003c/strong\u003e, 1-13 (2017).\u003c/li\u003e\n \u003cli\u003eZhang, M., Liu, X. \u0026amp; O\u0026rsquo;Neill, M. Spectral discrimination of \u003cem\u003ePhytophthora infestans\u003c/em\u003e infection on tomato based on principal component and cluster analyses. \u003cem\u003eInt. J. Remote Sens\u003c/em\u003e. \u003cstrong\u003e23\u003c/strong\u003e(6), 1095\u0026ndash;1107 (2002).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Disease resistance, differentially expressed genes, Phytophthora nicotianae, RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7634383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePhytophthora nicotianae\u003c/em\u003e is highly pathogenic and major constraint in betelvine crop production. To examine the resistance of 13 interspecific hybrids to \u003cem\u003eP. nicotianae\u003c/em\u003e, revealing significant variability in disease severity. IIHRPBIH9 exhibited the lowest disease severity and infection rate, whereas the IIHRBV170 was susceptible to infection. RNA sequencing was employed in IIHRPBIH9 and IIHBV170, which generated 6.62 GB of data, yielding 27,774 refined transcripts and 16,329 contigs. The leaf samples for RNA isolation were collected at 0 and 72 hours after inoculation (hai). 8561 and 10188 differentially expressed genes (DEGs) were identified in uninoculated and inoculated resistant (IIHRPBIH9) interspecific hybrid and susceptible variety, respectively. Gene ontology (GO) enrichment and KEGG pathway analyses highlighted key defense-related pathways, such as superoxide radicals degradation and jasmonic acid biosynthesis. Up-regulated genes included peroxidase 5-like protein and pathogenesis-related proteins, while down-regulated genes like LRR receptor-like kinase suggested susceptibility mechanisms. Heat maps revealed significant up-regulation of defense genes in inoculated resistant samples. The putative candidate genes were validated through qRT-PCR, confirming significant up-regulation of selected defense genes, and elucidating the molecular mechanisms of resistance and susceptibility. The transcriptome profile and putative candidate resistant and susceptible genes identified can be explored in breeding betelvine for \u003cem\u003eP. nicotianae\u003c/em\u003e resistance.\u003c/p\u003e","manuscriptTitle":"Transcriptomic profiling of interspecific hybrid in response to Phytophthora nicotianae infection in betelvine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 04:17:54","doi":"10.21203/rs.3.rs-7634383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fd450ae-31fc-4996-a0fa-49ef51b2bb3b","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57900338,"name":"Biological sciences/Biotechnology"},{"id":57900339,"name":"Biological sciences/Genetics"},{"id":57900340,"name":"Biological sciences/Microbiology"},{"id":57900341,"name":"Biological sciences/Molecular biology"},{"id":57900342,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-02-25T05:40:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 04:17:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7634383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634383","identity":"rs-7634383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}