Next Article in Journal / Special Issue
Bioinput Inoculation in Common Beans to Mitigate Stresses Caused by a Period of Drought
Previous Article in Journal / Special Issue
Osmotic Stress Alleviation in Saccharomyces cerevisiae for High Ethanol Fermentations with Different Wort Substrates
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Stresses
Volume 3
Issue 4
10.3390/stresses3040056
stresses-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptional Changes during Phytophthora capsici Infection Reveal Potential Defense Mechanisms in Squash

by
Shailesh Raj Acharya
†,
Swati Shrestha
‡,
Vincent Njung’e Michael
,
Yuqing Fu
,
Prerna Sabharwal
,
Shallu Thakur
and
Geoffrey Meru
*
Tropical Research and Education Center, University of Florida, Homestead, FL 33031, USA
*
Author to whom correspondence should be addressed.
†
Current address: Genomics and Bioinformatics Department, North Dakota State University, Fargo, ND 58108, USA.
‡
Current address: Plant and Soil Sciences Department, Oklahoma State University, Stillwater, OK 74078, USA.
Stresses 2023, 3(4), 827-841; https://doi.org/10.3390/stresses3040056
Submission received: 15 October 2023 / Revised: 18 November 2023 / Accepted: 27 November 2023 / Published: 29 November 2023
(This article belongs to the Collection Feature Papers in Plant and Photoautotrophic Stresses)
Browse Figures
Versions Notes

Abstract

:
Phytophthora capsici incites foliar blight, root, fruit, and crown rot in squash (Cucurbita spp.) and limits production worldwide. Resistance to crown rot in C. moschata breeding line #394-1-27-12 is conferred by three dominant genes, but the molecular mechanisms underlying this resistance are poorly understood. In the current study, RNA sequencing was used to investigate transcriptional changes in #394-1-27-12 (resistant) and Butterbush (susceptible) following infection by P. capsici at 12, 24, 48, 72, and 120 h post inoculation (hpi). Overall, the number of differentially expressed genes (DEGs) in Butterbush (2648) exceeded those in #394-1-27-12 (1729), but in both genotypes, the highest number of DEGs was observed at 72 hpi and least at 24 hpi. Our gene ontology (GO) analysis revealed a downregulation of the genes involved in polysaccharide and lignin metabolism in Butterbush but as an upregulation of those associated with regulation of peptidase activity. However, in #394-1-27-12, the downregulated genes were primarily associated with response to stimuli, whereas those upregulated were involved in oxidation–reduction and response to stress. The upregulated genes in #394-1-27-12 included defensin-like proteins, respiratory-burst oxidases, ethylene-responsive transcription factors, cytochrome P450 proteins, and peroxidases. These findings provide a framework for the functional validation of the molecular mechanisms underlying resistance to P. capsici in cucurbits.

1. Introduction

Phytophthora capsici Leonian is a hemibiotrophic soilborne pathogen that causes foliar blight, root, fruit, and crown rot syndromes in squash (Cucurbita spp.) and other vegetable crops [1,2]. The effective management of P. capsici in commercial vegetable production using chemical pesticides is often hindered by the evolution of pathogen populations insensitive to approved fungicides [3]. Cultural management practices such as crop rotation and the use of raised beds can help reduce pathogen inoculum density and dispersal but are often less effective under high disease pressure [4,5]. Host resistance is the best strategy for managing Phytophthora in squash; however, complete resistance is not available in commercial C. pepo cultivars, although partial resistance has been described in Zucchini (cv ‘Spineless Perfection’ and ‘Dark Green’) and Marrow (cv ‘Magda’ and ‘Hurikan’) cultivars [6].
Several disease screening efforts have identified sources of resistance in C. lundeliana [7,8], C. moschata [9,10], and C. pepo [11]. In both C. moschata (breeding line #394-1-27-12 carrying resistance from C. lundeliana) and C. pepo (breeding line #181761-36P), resistance to Phytophthora crown rot is conferred by three independent dominant genes [8,12]. Furthermore, the quantitative trait loci (QTLs) associated with resistance to Phytophthora crown rot have recently been reported in C. moschata [13] and C. pepo [14,15], paving the way for the application of marker-assisted selection in germplasm improvement. However, despite these advancements, the molecular mechanisms underlying resistance to Phytophthora crown rot are still poorly understood.
To effectively infect and colonize plants, P. capsici deploys a multitude of effectors to suppress host defense mechanisms. Therefore, the first plant response against P. capsici involves the activation of basal resistance in the form of pathogen-triggered immunity (PTI) following the detection of pathogen-associated molecular patterns (PAMPs) [16]. This is often followed by effector-triggered immunity (ETI) against pathogen effectors or virulence factors coinciding with signaling pathways mediated by salicylic acid, jasmonates, or ethylene [17,18]. During host infection, P. capsici releases a repertoire of effectors to support biotrophic and necrotrophic growth by suppressing host plant defenses [19,20,21]. These include apoplastic effectors such as necrosis-inducing proteins (NLPs) and elicitins, as well as cytoplasmic effectors containing either N-terminal Arg-Xaa-Leu-Arg (RxLR) or Crinkler (CRN) translocation motifs [22,23]. It is hypothesized that the Phytophthora crown rot resistance genes in squash interact with these effectors or participate in defense signaling pathways against P. capsici infection. In resistant squash genotypes, a form of hypersensitive response often characterized by scarred tissue at the crown restricts lesion development [8,13], and the occlusion of vascular vessels does not occur [6]. On the other hand, the rapid dissolution of epidermal and cortex tissues is evident in susceptible genotypes, with dense mycelia and occlusion material obstructing the vascular bundles [6]. Age-related resistance to Phytophthora fruit rot in winter squash results from the fortification of the cuticle and epidermal tissues in older fruits (≥14 days post pollination). However, in young fruits (7 days post pollination), these physical barriers are easily overcome, resulting in disease development [24].
Transcriptome-based gene expression analysis through RNA sequencing offers a rapid and efficient platform to discover, characterize, and annotate the key candidate genes and pathways underlying biological traits in plants [25]. RNA sequencing analysis in squash has been extensively used to uncover molecular mechanisms underlying responses to biotic stress traits such as powdery mildew [26], potyviruses [27], aphid feeding [28], cold tolerance [29], salinity tolerance [30], and morphology and development traits [31,32,33,34,35].
To further understand the molecular mechanisms underlying Phytophthora crown rot resistance in squash, the current study deployed RNA sequencing to identify differential gene expression patterns between resistant (394-1-27-12) and susceptible (Butterbush) C. moschata genotypes.

2. Results

2.1. Phenotypic Response of Resistant and Susceptible Genotypes

Throughout the experiment, the resistant genotype (394-1-27-12) remained asymptomatic (Figure 1A). In contrast, susceptible Butterbush seedlings exhibited expanding water-soaked lesions that were followed by visible constriction at the crown at 120 h post inoculation (hpi) (Figure 1B). Consequently, the Butterbush seedlings were severely wilted and did not survive past 168 hpi.

2.2. RNA Sequencing and Differential Gene Expression

RNA library sequencing yielded a total of 1269.91 million reads, of which 1258.69 million remained after quality filtering, with an average of 41.96 million reads per sample. Approximately 93% of the reads mapped to the C. moschata reference genome, with at least 90% mapping uniquely (Supplementary Table S1). A differential expression analysis across the aforementioned time points was performed using the expression profiles of the non-inoculated plants as the baseline. Genes with a log2fold change greater than three were considered differentially expressed and visualized using volcano plots (Supplementary Figure S1). A total of 1729 (1116 upregulated and 613 downregulated) and 2648 (1666 upregulated and 982 downregulated) genes were differentially expressed in 394-1-27-12 and Butterbush, respectively (Supplementary Table S2). The highest differential gene expression in 394-1-27-12 was observed at 72 hpi (714 DEGs), followed by 120 hpi (498 DEGs), 12 hpi (487 DEGs), and least at 24 hpi (30 DEGs) (Figure 2A). A similar pattern was observed for Butterbush, for which the highest differential gene expression was observed at 72 hpi (1546 DEGs), followed by 120 hpi (654 DEGs), 12 hpi (381 DEGs), and least at 24 hpi (67 DEGs) (Figure 2B).
The top 25 upregulated and downregulated differentially expressed genes (DEGs) in 394-1-27-12 and Butterbush were visualized using heat plots across time points (Supplementary Figures S2 and S3). All four time points were measured against 0 hpi, which was used as the baseline treatment. The degree of co-expression for both the upregulated and downregulated DEGs was visualized using Venn diagrams and was highest at 72 and 120 hpi in 394-1-27-12 and Butterbush (Figure 3). In 394-1-27-12, 231 upregulated and 48 downregulated genes were co-expressed at 72 and 120 hpi, respectively (Figure 3A,B). Butterbush, on the other hand, had a total of 348 upregulated and 86 downregulated genes co-expressed at 72 and 120 hpi, respectively (Figure 3C,D). A total of 8.2% of the total detected genes were differentially expressed in Butterbush, of which 4.8% belonged to genes expressed at 72 hpi and 2% belonged to genes expressed at 120 hpi, when compared to 0 hpi. In 394-1-27-12, 5.4% of the total detected genes were differentially expressed, of which 2.2% were expressed at 72 hpi and 1.5% at 120 hpi, respectively.

2.3. Gene Ontology Pathway Enrichment Analysis

Gene ontology (GO) pathway enrichment analysis was performed to understand the transcriptional and co-functional network of the DEGs among resistant and susceptible genotypes (Supplementary Table S3). Twenty-one and eight GO terms were enriched for downregulated genes in 394-1-27-12 (Figure 4) and Butterbush (Figure 5), respectively. In 394-1-27-12, significant GO enrichment for downregulated genes was associated with response to stimuli. However, in Butterbush, significant GO enrichment for downregulated genes was linked to polysaccharide and lignin metabolism.
For the upregulated genes, 16 and 44 GO terms were enriched in 394-1-27-12 (Figure 6) and Butterbush (Figure 7), respectively. Significant GO enrichment for upregulated genes in 394-1-27-12 was primarily associated with oxidation–reduction and response to stress processes. On the other hand, GO enrichment for upregulated genes in Butterbush was primarily associated with the negative regulation of peptidase activity.
Our pathway enrichment analysis for the DEGs (performed using the ‘pathway enrichment’ tool of the Cucurbit genome database) showed multiple enriched pathways for 394-1-27-12 and Butterbush (Table 1). Pathways enriched with (i) Baicalein degradation (hydrogen peroxide detoxification), (ii) Luteolin triglucuronide degradation, and (iii) L-glutamate degradation IX (via 4-aminobutanoate) were associated with the upregulated genes in 394-1-27-12. On the other hand, the enriched pathways associated with the downregulated DEGs in 394-1-27-12 were involved in hydroxylated fatty acid biosynthesis. Similarly, in Butterbush, the pathway enrichment was primarily for (i) Baicalein degradation (hydrogen peroxide detoxification), (ii) Luteolin triglucuronide degradation, and (iii) Phenylpropanoid biosynthesis for the upregulated genes. However, no pathways were significantly enriched for the downregulated genes in Butterbush.

2.4. Candidate Resistant Genes in 394-1-27-12

Our co-expression analysis between 394-1-27-12 (Resistant) and Butterbush (Susceptible) revealed shared DEGs following P. capsici infection. At 12, 24, 72, and 120 hpi, there were 0, 1, 388, and 163 common DEGs between the two genotypes, respectively (Figure 8). Further analysis of the DEGs at 72 and 120 hpi revealed that 31 stress response genes and 26 oxidation–reduction genes were co-expressed in 394-1-27-12 (Figure 9).
The stress response genes included expansin-like A3 (CmoCh19G007900) protein, thaumatin-like protein (CmoCh19G002930), heat stress transcription factor (CmoCh07G002420), Wound-induced protein WIN1 (CmoCh05G012280), Protein PLANT CADMIUM RESISTANCE 8 (CmoCh17G007590), Myb transcription factor (CmoCh08G010590), defensin-like protein 1 (CmoCh15G009030), respiratory-burst oxidase (CmoCh11G011760), Wound-induced protein (CmoCh05G012270), ethylene-responsive transcription factor 1B (CmoCh14G018460), and defensin-like protein 6 (CmoCh04G022620) (Table 2 and Supplementary Table S4). On the other hand, the oxidation–reduction genes co-expressed at 72 and 120 hpi in 394-1-27-12 included respiratory-burst oxidase (CmoCh11G011760), cytochrome P450 protein (CmoCh05G007700, CmoCh20G009870, CmoCh09G001800, CmoCh09G001790, and CmoCh09G002630), and peroxidases (CmoCh11G013370, CmoCh11G013380, and CmoCh20G003430) (Table 3 and Supplementary Table S5).
In summary, the resistant 394-1-27-12 line demonstrated enhanced resistance to P. capsici infection compared to its susceptible counterpart, Butterbush. While the transcriptomic analysis via RNA sequencing revealed more DEGs in Butterbush, the resistant 394-1-27-12 line expressed more genes associated with pathogen stress. Notably, both the resistant and susceptible lines exhibited the highest number of DEGs at 72 hpi, suggesting that this time point is a critical phase in the molecular response against P. capsici infection. However, two of the most notable stress response genes, namely the expansin-like protein and thaumatin-like protein, were highly expressed in the resistant 394-1-27-12 line both at 72 hpi and 120 hpi, suggesting a genotype-specific biotic stress-related gene expression pattern in influencing resistance against P. capsici infection. Our results demonstrated a repertoire of genes in combating P. capsici infection directly through their involvement in stress response pathways or indirectly by acting on the regulatory networks that operate during pathogen infection in C. moschata.

3. Materials and Methods

3.1. Plant Materials and Inoculation

Two C. moschata squash genotypes, a resistant breeding line (394-1-27-12) and a highly susceptible butternut cultivar (Butterbush), were used in the study. The plants were sown in seedling trays containing sterilized potting mix and grown in a growth chamber with supplemental lighting (14 h light, 10 h dark) at room temperature (20–22 °C). For each genotype, 15 individual plants were randomly assigned to a control group (non-inoculated) and a treatment group (inoculated with P. capsici). At the second true leaf stage, the seedlings were inoculated with a virulent P. capsici isolate following the protocol described by Krasnow et al. (2017), with minor modifications [6,12]. Briefly, a 5 mm cornmeal agar mycelial plug was transferred to 14% V8 agar plates (140 mL V8 juice, 3 g CaCO3, 16 g agar per liter) and grown under constant fluorescent light at 28 °C. After 6 days, the plates were flooded with cold sterile distilled water (4 °C) and chilled at 4 °C for 30 min before incubation at 21 °C for 1 h to allow for the synchronous release of zoospores. Zoospores were quantified with a hemocytometer and diluted to 1.0 × 105 zoospores mL−1. A hand spray bottle adjusted to release 0.5 mL volume per spray was used to deliver 1.5 mL of zoospore suspension at the crown of each seedling.

3.2. RNA Extraction and Sequencing

Stem tissues from inoculated and non-inoculated seedlings of both genotypes were harvested from the crown tissue across six time points (0 h, 12 h, 24 h, 72 h, and 120 h) and immediately frozen in liquid nitrogen and stored at −80 °C until further processing. Three independent replicates per time point were made. Total RNA was extracted using the FavorPrep™ Plant Genomic RNA Extraction Mini Kit (Pingtung, Taiwan) following the manufacturer’s protocol. The quantification and quality examination of the extracted RNA were carried out using a Qubit v4 Flourometer (ThermoFisher Scientific, Waltham MA, USA) and via agarose gel electrophoresis. RNA integrity (RIN) was assessed using an Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) to ensure all samples had RIN > 8.0 before sequencing [36]. Sequencing libraries were prepared, and messenger RNA was sequenced on an Illumina NextSeq 500 sequencer (Illumina, San Diego, CA, USA) at Novogene (Sacramento, CA, USA).

3.3. RNA-Seq Data Analysis

Initial quality control of the RNA sequencing data was carried out to remove adaptor sequences and reads with high missing data or low-quality base scores (Qphred < 30). Clean reads from all libraries were aligned to the C. moschata reference genome (http://cucurbitgenomics.org/organism/9, accessed on 15 December 2020) using HISAT 2.0 software [37]. Read counts for aligned transcripts were generated using feature counts [38,39]. A differential gene expression analysis was performed using DeSeq2 utilizing the empirical Bayes shrinkage of gene expression logarithmic fold changes to increase sensitivity and precision, enabling the detection of significant genes with low read counts [39]. The threshold for significant differentially expressed genes was set at a false discovery rate of 0.05 and an absolute value of fold change ≥2. Functional annotation and a biosynthetic pathway analysis of differentially expressed genes were performed in the publicly available Cucurbita genomics database.

4. Discussion

While several sources of resistance to Phytophthora crown rot [7,8,9,10,11] and their associated inheritance mechanisms [8,12] and QTLs [13,14,15] have been described in Cucurbita spp., the molecular mechanisms and gene pathways underlying the resistance remain poorly understood. In the current study, RNA sequencing revealed gene expression patterns in resistant and susceptible genotypes of Cucurbita following P. capsici infection. Overall, a significantly higher number of genes were differentially expressed in the susceptible genotype (Butterbush; 2648 genes) when compared to the resistant genotype (394-1-27-12; 1729 genes). A similar trend was reported in melon, where the susceptible genotype (E31) had a higher number of differentially expressed genes than the resistant genotype (ZQK9) post P. capsici infection [40]. In a separate study, Naveed and Ali (2018) also reported a higher number of DEGs in the susceptible tomato accession (Sp-S) compared to its resistant counterpart (Sp-R) when inoculated with P. parasitica [41]. The higher degree of differentially expressed genes in susceptible plants may be due to extreme transcriptional reprogramming, as well as the presence of diverse cellular and metabolic changes during the pathogen’s interaction with a susceptible host [41].
Our GO analysis of upregulated genes in both susceptible and resistant genotype showed the significant enrichment of the genes involved in defense mechanisms. Most of the defense-related genes were associated with oxidation–reduction and oxidative stress processes, a typical expression in plants under biotic stress. Genes regulating oxidation–reduction processes primarily inhibit the oxidative burst of reactive oxygen species during pathogenesis and further induce genes involved in the hydrogen peroxide metabolic pathways that catalyze compatible–incompatible interactions between the pathogen effector protein and the host [42]. This interaction triggers primary responses such as cell wall polymerization, lignification, and apoptosis [42,43]. The reactive oxygen species (ROS) also trigger distant signaling and initiate a cascade of cell defense-related protein responses, such as phytoalexins, and a series of kinases and phosphatases [44,45].
In both the resistant (394-1-27-12) and susceptible (Butterbush) genotypes, expansin-like protein and thaumatin-like protein were over-expressed (>13 logfold change than the non-inoculated) at 72 and 120 hpi (Supplementary Table S2). One expansin gene homolog (CmoCh19G007900) was found to have a two-fold higher gene expression level in 394-1-27-12 (Log2FC = 28.80) compared to Butterbush (Log2FC = 12.68) at 120 hpi. The role of expansin proteins in triggering ROS production and cell wall structure modification in biotic stress response is well known in plants. Narváez-Barragán et.al (2020) reported the role of the Ex11 expansin protein in Arabidopsis thaliana in defense against pectobacterium through ROS production and the subsequent triggering of the jasmonic acid, salicyclic acid, and ethylene signaling pathways [46]. Additionally, the overexpression of genes regulating hydrolase activity at 72 and 120 hpi further explains the role of expansin proteins in defense since glycoside hydrolase is one of the two functional domains of expansin proteins [47]. On the other hand, thaumatin-like proteins confer anti-fungal activity and have been reported in numerous plant species, including tobacco (osmotin), maize (zeamatin), barley (hordomatin), oat (avematin), and wheat (trimatin) [48,49,50,51,52]. Thaumatin-like proteins include a major class of pathogenesis-related proteins such as oxidases and oxidase-like proteins, chitinases, β-1,3-glucanases, endoproteinases, proteinase inhibitors, lipid-transfer proteins, ribonuclease-like proteins, defensins, and thionins [53,54,55]. Hence, the identification of two crucial genes, expansin-like and thaumatin-like proteins, holds promise for governing C. moschata resistance to P. capsici. However, a more in-depth understanding of the complete molecular pathways and inter-regulator gene networks is required to comprehend the crosstalk of expansin-like and thaumatin-like proteins with plant hormones to facilitate pathogen stress responses.
Stress response genes co-expressed at 72 and 120 hpi in the resistant genotype also included transcription factors such as Myb (CmoCh08G010590) and the ethylene-responsive transcription factor 1B (CmoCh14G018460). The Myb transcription factors modulate the response of plants to biotic and abiotic stress by promoting the biosynthesis of salicylic acid and abscisic acid, two key signaling molecules in plant defense [56]. Similarly, ethylene-responsive transcription factor 1B is an ethylene response factor (ERF) that regulates the response of plants to stresses by facilitating ethylene, jasmonic acid, abscisic acid, and redox signaling [57]. In Nicotiana benthamiana, the AP2/ERF transcription factor was reported to be involved in resistance against Phytophthora infestans by regulating the production of phytoalexins [58]. The regulation of gene expression through transcriptional reprogramming is key in plant defense activation [59], and transcriptional factors can upregulate genes to prevent pathogen penetration and/or degradation. For instance, in Barley, the upregulation of the HvNAC6 transcription factor prevents Blumeria graminis penetration and establishment [60]. The heat stress transcription factors, also called heat shock transcription factors (Hsfs), co-expressed at 72 and 120 hpi, have also been shown to play a dominant role in both abiotic and biotic stresses. In Fragaria vesca, at least eight such Hsfs genes were highly expressed during powdery mildew infection [61]. Additionally, the Hsf OsSPL7 was critical in rice in conferring resistance against Xanthomonas and maintaining ROS balance [62].
Our pathway enrichment analysis showed that the L-Glutamate degradation pathway (PWY0-1305) was significantly enriched in the upregulated DEGs of the resistant genotype (394-1-27-12) compared to the susceptible genotype (Butterbush). Glutamate metabolism plays a key role in amino acid metabolism linked to plant defense processes such as cellular redox regulation, tricarboxylic acid cycle-dependent energy reprogramming, and nitrogen transportation cycle [63], and an increased expression of defense-related genes was seen in rice when exogenous glutamate application was applied to rice roots [64]. On the contrary, the hydroxylated fatty acid biosynthesis pathway (PWY-6433) was significantly enriched in the downregulated DEGs of the resistant genotype (394-1-27-12) compared to the susceptible genotype (Butterbush). The hydroxylated fatty acid biosynthesis pathway leads to the production of very-long-chain fatty acids in response to abiotic or abiotic stress [65]. However, a significant accumulation of very-long-chain fatty acids can result in severe plant growth retardation and cell death, and its suppression in 394-1-27-12 may be needed to facilitate normal plant growth following infection by P. capsici. The complex dynamics of C. moschata differentially expressed genes during P. capsici infection necessitates further investigations into the defense signaling pathways. Additionally, our study’s identification of the genes associated with the putative glutamate metabolic pathways opens avenues for future research on the crosstalk among different biotic stress regulatory pathways. Understanding metabolic processes and such pathways under P. capsici-related stress is crucial in gaining insights into genetic manipulations and creating breeding strategies in squash.

5. Conclusions

The current study uncovered significant defense-related genes and pathways involved in plant response against P. capsici in resistant (394-1-27-12) and susceptible (Butterbush) C. moschata genotypes. Taken together, the results reveal that resistance in the 394-1-27-12 genotype occurs primarily through the activation and upregulation of the genes involved in oxidation–reduction and the response to stress pathways. These findings provide a platform for the further exploration of the role played by these key genes in conferring resistance against P. capsici in squash to generate resistant squash breeding lines.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/stresses3040056/s1, Figure S1A: Volcano plots showing differential gene expression for 394-1-27-12 (resistant) at 12, 24, 72, and 120 h post inoculation; Figure S1B: Volcano plots showing differential gene expression for Butterbush (susceptible) at 12, 24, 72 and 120 h post inoculation (hpi); Figure S2A: Heat map for the top 25 upregulated and downregulated genes in 394-1-27-12 (coded as SS30) at 12 h post inoculation; Figure S2B: Heat map for the top 25 upregulated and downregulated genes in 394-1-27-12 (coded as SS30) at 24 h post inoculation; Figure S2C: Heat map for the top 25 upregulated and downregulated genes in 394-1-27-12 (coded as SS30) at 72 h post inoculation; Figure S2D: Heat map for the top 25 upregulated and downregulated genes in 394-1-27-12 (coded as SS30) at 120 h post inoculation; Figure S3A: Heat map for the top 25 upregulated and downregulated genes in Butterbush (coded as BBT) at 12 h post inoculation; Figure S3B: Heat map for the top 25 upregulated and downregulated genes in Butterbush (coded as BBT) at 24 h post inoculation; Figure S3C: Heat map for the top 25 upregulated and downregulated genes in Butterbush (coded as BBT) at 72 h post inoculation; Figure S3D: Heat map for the top 25 upregulated and downregulated genes in Butterbush (coded as BBT) at 120 h post inoculation; Table S1: RNA library sequencing statistics, mapping rate, and quality scores; Table S2: Differentially expressed genes in 394-1-27-12 (coded as SS30) and Butterbush (coded as BBT); Table S3: Gene ontology enrichment analysis revealed a transcriptional and co-functional network of the DEGs among resistant and susceptible genotypes; Table S4: List of stress response genes co-expressed at 72 and 120 h post inoculation in 394-1-27-12; Table S5: List of oxidation–reduction genes co-expressed at 72 and 120 h post inoculation in 394-1-27-12.

Author Contributions

Conceptualization, G.M. and S.S.; methodology, V.N.M., S.R.A., S.S., Y.F., P.S. and S.T.; formal analysis, V.N.M., S.R.A., S.S. and G.M.; resources, G.M.; data curation, V.N.M., S.R.A., S.S. and G.M.; writing—original draft preparation, V.N.M., S.R.A. and S.S.; writing—review and editing, V.N.M., S.R.A., S.S., G.M., Y.F., P.S., S.T. and G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research article and the APC was funded by USDA-NIFA SCRI, grant number #2020-51181-32139.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to further analytical studies.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Babadoost, M.; Islam, S.Z. Fungicide Seed Treatment Effects on Seedling Damping-off of Pumpkin Caused by Phytophthora capsici. Plant Dis. 2003, 87, 63–68. [Google Scholar] [CrossRef]
  2. Krasnow, C.S.; Hausbeck, M.K. Pathogenicity of Phytophthora Capsici to Brassica Vegetable Crops and Biofumigation Cover Crops (Brassica Spp.). Plant Dis. 2015, 99, 1721–1726. [Google Scholar] [CrossRef] [PubMed]
  3. Lamour, K.H.; Hausbeck, M.K. Mefenoxam insensitivity and the sexual stage of Phytophthora capsici in Michigan cucurbit fields. Phytopathology 2000, 90, 396–400. [Google Scholar] [CrossRef] [PubMed]
  4. Hausbeck, M.K.; Lamour, K.H. Phytophthora capsici on Vegetable Crops: Research Progress and Management Challenges. Plant Dis. 2004, 88, 1292–1303. [Google Scholar] [CrossRef] [PubMed]
  5. Meyer, M.D.; Hausbeck, M.K. Using cultural practices and cultivar resistance to manage Phytophthora crown rot on summer squash. HortScience 2012, 47, 1080–1084. [Google Scholar] [CrossRef]
  6. Krasnow, C.S.; Hammerschmidt, R.; Hausbeck, M.K. Characteristics of resistance to Phytophthora Root and Crown rot in Cucurbita pepo. Plant Dis. 2017, 101, 659–665. [Google Scholar] [CrossRef] [PubMed]
  7. Kabelka, E.A.; Padley, L.D., Jr.; Roberts, P.; Ramos, L.; Martinez, M.; Klassen, W. Resistance to Phytophthora capsici within winter squash (Cucurbita moschata) derived from a wild Cucurbita species. HortScience 2007, 42, 1014. [Google Scholar]
  8. Padley, L.D.; Kabelka, E.A.; Roberts, P.D. Inheritance of resistance to Crown rot caused by Phytophthora capsici in Cucurbita. HortScience 2009, 44, 211–213. [Google Scholar] [CrossRef]
  9. Chavez, D.J.; Kabelka, E.A.; Chaparro, J.X. Screening of Cucurbita moschata Duchesne Germplasm for Crown Rot Resistance to Floridian Isolates of Phytophthora capsici Leonian. HortSci. Horts 2011, 46, 536–540. [Google Scholar] [CrossRef]
  10. Kousik, C.S.; Ikerd, J.L.; Wechter, W.P.; Branham, S.; Turechek, W. Broad Resistance to Post-Harvest Fruit Rot in USVL Watermelon Germplasm Lines to Isolates of Phytophthora capsici Across the United States. Plant Dis. 2022, 106, 711–719. [Google Scholar] [CrossRef]
  11. Padley, L.D.; Kabelka, E.A.; Roberts, P.D.; French, R. Evaluation of Cucurbita pepo accessions for Crown rot resistance to isolates of Phytophthora capsici. HortScience 2008, 43, 1996–1999. Available online: http://hortsci.ashspublications.org/content/43/7/1996.short (accessed on 15 September 2020). [CrossRef]
  12. Michael, V.N.; Fu, Y.; Meru, G. Inheritance of Resistance to Phytophthora Crown Rot in Cucurbita pepo. HortSci. Horts 2019, 54, 1156–1158. [Google Scholar] [CrossRef]
  13. Ramos, A.; Fu, Y.; Michael, V.; Meru, G. QTL-seq for identification of loci associated with resistance to Phytophthora crown rot in squash. Sci. Rep. 2020, 10, 5326. [Google Scholar] [CrossRef] [PubMed]
  14. Michael, V.N.; Fu, Y.; Shrestha, S.; Meru, G. A Novel QTL for Resistance to Phytophthora Crown Rot in Squash. Plants 2021, 10, 2115. [Google Scholar] [CrossRef] [PubMed]
  15. Vogel, G.; LaPlant, K.E.; Mazourek, M.; Gore, M.A.; Smart, C.D. A combined BSA-Seq and linkage mapping approach identifies genomic regions associated with Phytophthora root and crown rot resistance in squash. Theor. Appl. Genet. 2021, 134, 1015–1031. [Google Scholar] [CrossRef] [PubMed]
  16. Zipfel, C.; Robatzek, S. Pathogen-associated molecular pattern-triggered immunity: Veni, vidi…? Plant Physiol. 2010, 154, 551–554. [Google Scholar] [CrossRef] [PubMed]
  17. Jones, J.D.G.; Dangl, J.L. The plant immune system. Nature 2006, 444, 323–329. [Google Scholar] [CrossRef]
  18. Lo Presti, L.; Lanver, D.; Schweizer, G.; Tanaka, S.; Liang, L.; Tollot, M.; Zuccaro, A.; Reissmann, S.; Kahmann, R. Fungal effectors and plant susceptibility. Annu. Rev. Plant Biol. 2015, 66, 513–545. [Google Scholar] [CrossRef]
  19. Bozkurt, T.O.; Schornack, S.; Banfield, M.J.; Kamoun, S. Oomycetes, effectors, and all that jazz. Curr. Opin. Plant Biol. 2012, 15, 483–492. [Google Scholar] [CrossRef]
  20. Chen, X.-R.; Xing, Y.-P.; Li, Y.-P.; Tong, Y.-H.; Xu, J.-Y. RNA-Seq Reveals Infection-Related Gene Expression Changes in Phytophthora capsici. PLoS ONE 2013, 8, e74588. [Google Scholar] [CrossRef]
  21. Fawke, S.; Doumane, M.; Schornack, S. Oomycete Interactions with Plants: Infection Strategies and Resistance Principles. Microbiol. Mol. Biol. Rev. 2015, 79, 263–280. [Google Scholar] [CrossRef] [PubMed]
  22. Stam, R.; Jupe, J.; Howden, A.J.M.; Morris, J.A.; Boevink, P.C.; Hedley, P.E.; Huitema, E. Identification and Characterisation CRN Effectors in Phytophthora capsici Shows Modularity and Functional Diversity. PLoS ONE 2013, 8, e59517. [Google Scholar] [CrossRef]
  23. Feng, B.-Z.; Zhu, X.-P.; Fu, L.; Lv, R.-F.; Storey, D.; Tooley, P.; Zhang, X.-G. Characterization of necrosis-inducing NLP proteins in Phytophthora capsici. BMC Plant Biol. 2014, 14, 126. [Google Scholar] [CrossRef] [PubMed]
  24. Alzohairy, S.A.; Hammerschmidt, R.; Hausbeck, M.K. Changes in winter squash fruit exocarp structure associated with age-related resistance to Phytophthora capsici. Phytopathology 2020, 110, 447–455. [Google Scholar] [CrossRef] [PubMed]
  25. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef]
  26. Guo, W.-L.; Chen, B.-H.; Chen, X.-J.; Guo, Y.-Y.; Yang, H.-L.; Li, X.-Z.; Wang, G.-Y. Transcriptome profiling of pumpkin (Cucurbita moschata Duch.) leaves infected with powdery mildew. PLoS ONE 2018, 13, e0190175. [Google Scholar] [CrossRef] [PubMed]
  27. Amoroso, C.G.; Andolfo, G.; Capuozzo, C.; Di Donato, A.; Martinez, C.; Tomassoli, L.; Ercolano, M.R. Transcriptomic and genomic analysis provides new insights in molecular and genetic processes involved in zucchini ZYMV tolerance. BMC Genom. 2022, 23, 371. [Google Scholar] [CrossRef]
  28. Vitiello, A.; Rao, R.; Corrado, G.; Chiaiese, P.; Digilio, M.C.; Cigliano, R.A.; D’Agostino, N. De novo transcriptome assembly of Cucurbita pepo L. leaf tissue infested by Aphis gossypii. Data 2018, 3, 36. [Google Scholar] [CrossRef]
  29. Carvajal, F.; Rosales, R.; Palma, F.; Manzano, S.; Cañizares, J.; Jamilena, M.; Garrido, D. Transcriptomic changes in Cucurbita pepo fruit after cold storage: Differential response between two cultivars contrasting in chilling sensitivity. BMC Genom. 2018, 19, 125. [Google Scholar] [CrossRef]
  30. Xie, J.; Lei, B.; Niu, M.; Huang, Y.; Kong, Q.; Bie, Z. High throughput sequencing of small RNAs in the two Cucurbita germplasm with different sodium accumulation patterns identifies novel microRNAs involved in salt stress response. PLoS ONE 2015, 10, e0127412. [Google Scholar] [CrossRef]
  31. Huang, H.-X.; Yu, T.; Li, J.-X.; Qu, S.-P.; Wang, M.-M.; Wu, T.-Q.; Zhong, Y.-J. Characterization of Cucurbita maxima Fruit Metabolomic Profiling and Transcriptome to Reveal Fruit Quality and Ripening Gene Expression Patterns. J. Plant Biol. 2019, 62, 203–216. [Google Scholar] [CrossRef]
  32. Pomares-Viciana, T.; Del Río-Celestino, M.; Román, B.; Die, J.; Pico, B.; Gómez, P. First RNA-seq approach to study fruit set and parthenocarpy in zucchini (Cucurbita pepo L.). BMC Plant Biol. 2019, 19, 61. [Google Scholar] [CrossRef]
  33. Wyatt, L.E.; Strickler, S.R.; Mueller, L.A.; Mazourek, M. An acorn squash (Cucurbita pepo ssp. ovifera) fruit and seed transcriptome as a resource for the study of fruit traits in Cucurbita. Hortic. Res. 2015, 2, 14070. [Google Scholar] [CrossRef]
  34. Wyatt, L.E.; Strickler, S.R.; Mueller, L.A.; Mazourek, M. Comparative analysis of Cucurbita pepo metabolism throughout fruit development in acorn squash and oilseed pumpkin. Hortic. Res. 2016, 3, 16045. [Google Scholar] [CrossRef] [PubMed]
  35. Xanthopoulou, A.; Montero-Pau, J.; Picó, B.; Boumpas, P.; Tsaliki, E.; Paris, H.S.; Tsaftaris, A.; Kalivas, A.; Mellidou, I.; Ganopoulos, I. A comprehensive RNA-Seq-based gene expression atlas of the summer squash (Cucurbita pepo) provides insights into fruit morphology and ripening mechanisms. BMC Genom. 2021, 22, 341. [Google Scholar] [CrossRef] [PubMed]
  36. Schroeder, A.; Mueller, O.; Stocker, S.; Salowsky, R.; Leiber, M.; Gassmann, M.; Lightfoot, S.; Menzel, W.; Granzow, M.; Ragg, T. The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 2006, 7, 3. [Google Scholar] [CrossRef]
  37. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [PubMed]
  38. Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef]
  39. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  40. Wang, P.; Wu, H.; Zhao, G.; He, Y.; Kong, W.; Zhang, J.; Liu, S.; Liu, M.; Hu, K.; Liu, L.; et al. Transcriptome analysis clarified genes involved in resistance to Phytophthora capsici in melon. PLoS ONE 2020, 15, e0227284. [Google Scholar] [CrossRef]
  41. Naveed, Z.A.; Ali, G.S. Comparative Transcriptome Analysis between a Resistant and a Susceptible Wild Tomato Accession in Response to Phytophthora parasitica. Int. J. Mol. Sci. 2018, 19, 3735. [Google Scholar] [CrossRef] [PubMed]
  42. Regente, M.; Pinedo, M.; Clemente, H.S.; Balliau, T.; Jamet, E.; de la Canal, L. Plant extracellular vesicles are incorporated by a fungal pathogen and inhibit its growth. J. Exp. Bot. 2017, 68, 5485–5495. [Google Scholar] [CrossRef]
  43. Slesak, I.; Libik, M.; Karpinska, B.; Karpinski, S.; Miszalski, Z. The role of hydrogen peroxide in regulation of plant metabolism and cellular signalling in response to environmental stresses. Acta Biochim. Pol. 2007, 54, 39–50. [Google Scholar] [CrossRef] [PubMed]
  44. Smirnoff, N.; Arnaud, D. Hydrogen peroxide metabolism and functions in plants. New Phytol. 2019, 221, 1197–1214. [Google Scholar] [CrossRef] [PubMed]
  45. Hung, S.-H.; Yu, C.-W.; Lin, C.H. Hydrogen peroxide functions as a stress signal in plants. Bot. Bull. Acad. Sin. 2005, 46, 1–10. [Google Scholar]
  46. Narváez-Barragán, D.A.; Tovar-Herrera, O.E.; Torres, M.; Rodríguez, M.; Humphris, S.; Toth, I.K.; Segovia, L.; Serrano, M.; Martínez-Anaya, C. Expansin-like Exl1 from Pectobacterium is a virulence factor required for host infection, and induces a defence plant response involving ROS, and jasmonate, ethylene and salicylic acid signalling pathways in Arabidopsis thaliana. Sci. Rep. 2020, 10, 7747. [Google Scholar] [CrossRef]
  47. Ahn, Y.O.; Zheng, M.; Bevan, D.R.; Esen, A.; Shiu, S.-H.; Benson, J.; Peng, H.-P.; Miller, J.T.; Cheng, C.-L.; Poulton, J.E.; et al. Functional genomic analysis of Arabidopsis thaliana glycoside hydrolase family 35. Phytochemistry 2007, 68, 1510–1520. [Google Scholar] [CrossRef]
  48. Hejgaard, J.; Jacobsen, S.; Svendsen, I. Two antifungal thaumatin-like proteins from barley grain. FEBS Lett. 1991, 291, 127–131. [Google Scholar] [CrossRef]
  49. Alison, J.V.; Roberts, W.K.; Selitrennikoff, C.P. A new family of plant antifungal proteins. Mol. Plant Microbe. Interact. 1991, 4, 315–323. [Google Scholar]
  50. Roberts, W.K.; Selitrennikoff, C.P. Zeamatin, an antifungal protein from maize with membrane-permeabilizing activity. Microbiology 1990, 136, 1771–1778. [Google Scholar] [CrossRef]
  51. Abad, L.R.; D’Urzo, M.P.; Liu, D.; Narasimhan, M.L.; Reuveni, M.; Zhu, J.K.; Niu, X.; Singh, N.K.; Hasegawa, P.M.; Bressan, R.A. Antifungal activity of tobacco osmotin has specificity and involves plasma membrane permeabilization. Plant Sci. 1996, 118, 11–23. [Google Scholar] [CrossRef]
  52. Skadsen, R.; Sathish, P.; Kaeppler, H. Expression of thaumatin-like permatin PR-5 genes switches from the ovary wall to the aleurone in developing barley and oat seeds. Plant Sci. 2000, 156, 11–22. [Google Scholar] [CrossRef] [PubMed]
  53. Van Loon, L.C.; Van Strien, E.A. The families of pathogenesis-related proteins, their activities, and comparative analysis of PR-1 type proteins. Physiol. Mol. Plant Pathol. 1999, 55, 85–97. [Google Scholar] [CrossRef]
  54. Saboki Ebrahim, K.U.; Singh, B. Pathogenesis related (PR) proteins in plant defense mechanism. Sci. Microb. Pathog. 2011, 2, 1043–1054. [Google Scholar]
  55. Sinha, M.; Singh, R.P.; Kushwaha, G.S.; Iqbal, N.; Singh, A.; Kaushik, S.; Kaur, P.; Sharma, S.; Singh, T.P. Current overview of allergens of plant pathogenesis related protein families. Sci. World J. 2014, 2014, 543195. [Google Scholar] [CrossRef] [PubMed]
  56. Zhu, X.; Li, X.; He, Q.; Guo, D.; Liu, C.; Cao, J.; Wu, Z.; Kang, Z.; Wang, X. TaMYB29: A Novel R2R3-MYB Transcription Factor Involved in Wheat Defense against Stripe Rust. Front. Plant Sci. 2021, 12, 783388. [Google Scholar] [CrossRef] [PubMed]
  57. Müller, M.; Munné-Bosch, S. Ethylene Response Factors: A Key Regulatory Hub in Hormone and Stress Signaling. Plant Physiol. 2015, 169, 32–41. [Google Scholar] [CrossRef]
  58. Imano, S.; Fushimi, M.; Camagna, M.; Tsuyama-Koike, A.; Mori, H.; Ashida, A.; Tanaka, A.; Sato, I.; Chiba, S.; Kawakita, K.; et al. AP2/ERF transcription factor NbERF-IX-33 is involved in the regulation of phytoalexin production for the resistance of Nicotiana benthamiana to Phytophthora infestans. Front. Plant Sci. 2022, 12, 821574. [Google Scholar] [CrossRef]
  59. Qiu, J.-L.; Fiil, B.K.; Petersen, K.; Nielsen, H.B.; Botanga, C.J.; Thorgrimsen, S.; Palma, K.; Suarez-Rodriguez, M.C.; Sandbech-Clausen, S.; Lichota, J.; et al. Arabidopsis MAP kinase 4 regulates gene expression through transcription factor release in the nucleus. EMBO J. 2008, 27, 2214–2221. [Google Scholar] [CrossRef]
  60. Chen, Y.-J.; Perera, V.; Christiansen, M.W.; Holme, I.B.; Gregersen, P.L.; Grant, M.R.; Collinge, D.B.; Lyngkjær, M.F. The barley HvNAC6 transcription factor affects ABA accumulation and promotes basal resistance against powdery mildew. Plant Mol. Biol. 2013, 83, 577–590. [Google Scholar] [CrossRef]
  61. Hu, Y.; Han, Y.-T.; Wei, W.; Li, Y.-J.; Zhang, K.; Gao, Y.-R.; Zhao, F.-L.; Feng, J.-Y. Identification, isolation, and expression analysis of heat shock transcription factors in the diploid woodland strawberry Fragaria vesca. Front. Plant Sci. 2015, 6, 736. [Google Scholar] [CrossRef] [PubMed]
  62. Hoang, T.V.; Vo, K.T.X.; Rahman, M.; Choi, S.-H.; Jeon, J.-S. Heat stress transcription factor OsSPL7 plays a critical role in reactive oxygen species balance and stress responses in rice. Plant Sci. 2019, 289, 110273. [Google Scholar] [CrossRef] [PubMed]
  63. Kan, C.-C.; Chung, T.-Y.; Wu, H.-Y.; Juo, Y.-A.; Hsieh, M.-H. Exogenous glutamate rapidly induces the expression of genes involved in metabolism and defense responses in rice roots. BMC Genom. 2017, 18, 186. [Google Scholar] [CrossRef] [PubMed]
  64. Seifi, H.S.; Van Bockhaven, J.; Angenon, G.; Höfte, M. Glutamate metabolism in plant disease and defense: Friend or foe? Mol. Plant-Microbe Interact. 2013, 26, 475–485. [Google Scholar] [CrossRef] [PubMed]
  65. Granrut, A.D.B.D.; Cacas, J.-L. How Very-Long-Chain Fatty Acids Could Signal Stressful Conditions in Plants? Front. Plant Sci. 2016, 7, 1490. [Google Scholar] [CrossRef]
Stresses 03 00056 g001
Figure 1. Asymptomatic plants of the resistant Cucurbita moschata breeding line 394-1-27-12 (A) and disease progression in the susceptible Butterbush (B) cultivars at 0, 12, 24, 72, and 120 h post inoculation (hpi).
Figure 1. Asymptomatic plants of the resistant Cucurbita moschata breeding line 394-1-27-12 (A) and disease progression in the susceptible Butterbush (B) cultivars at 0, 12, 24, 72, and 120 h post inoculation (hpi).
Stresses 03 00056 g001
Stresses 03 00056 g002
Figure 2. Differentially expressed genes (DEGs) upregulated (blue bar) or downregulated (orange bar) at 12, 24, 72, and 120 h post inoculation in (A) 394-1-27-12 and (B) Butterbush genotypes of Cucurbita moschata infected with Phytophthora capsici.
Figure 2. Differentially expressed genes (DEGs) upregulated (blue bar) or downregulated (orange bar) at 12, 24, 72, and 120 h post inoculation in (A) 394-1-27-12 and (B) Butterbush genotypes of Cucurbita moschata infected with Phytophthora capsici.
Stresses 03 00056 g002
Stresses 03 00056 g003
Figure 3. Venn diagrams showing the co-expression of differentially expressed genes (DEGs) across 12 (purple), 24 (yellow), 72 (green), and 120 (pink) hours post inoculation (hpi) in 394-1-27-12 (A,B) and Butterbush (C,D).
Figure 3. Venn diagrams showing the co-expression of differentially expressed genes (DEGs) across 12 (purple), 24 (yellow), 72 (green), and 120 (pink) hours post inoculation (hpi) in 394-1-27-12 (A,B) and Butterbush (C,D).
Stresses 03 00056 g003
Stresses 03 00056 g004
Figure 4. Gene ontology enrichment analysis for the downregulated genes in the 394-1-27-12 (resistant) Cucurbita moschata genotype.
Figure 4. Gene ontology enrichment analysis for the downregulated genes in the 394-1-27-12 (resistant) Cucurbita moschata genotype.
Stresses 03 00056 g004
Stresses 03 00056 g005
Figure 5. Gene ontology enrichment analysis for the downregulated genes in the Butterbush (susceptible) Cucurbita moschata genotype.
Figure 5. Gene ontology enrichment analysis for the downregulated genes in the Butterbush (susceptible) Cucurbita moschata genotype.
Stresses 03 00056 g005
Stresses 03 00056 g006
Figure 6. Gene ontology enrichment analysis for the upregulated genes in the 394-1-27-12 (resistant) Cucurbita moschata genotype.
Figure 6. Gene ontology enrichment analysis for the upregulated genes in the 394-1-27-12 (resistant) Cucurbita moschata genotype.
Stresses 03 00056 g006
Stresses 03 00056 g007
Figure 7. Gene ontology enrichment analysis for the upregulated genes in the Butterbush (susceptible) Cucurbita moschata genotype.
Figure 7. Gene ontology enrichment analysis for the upregulated genes in the Butterbush (susceptible) Cucurbita moschata genotype.
Stresses 03 00056 g007
Stresses 03 00056 g008
Figure 8. Co-expression pattern in the 394-1-27-12 (resistant) and Butterbush (susceptible) genotypes at 12, 24, 72, and 120 h post inoculation.
Figure 8. Co-expression pattern in the 394-1-27-12 (resistant) and Butterbush (susceptible) genotypes at 12, 24, 72, and 120 h post inoculation.
Stresses 03 00056 g008
Stresses 03 00056 g009
Figure 9. Total genes co-expressed at 72 and 120 h post inoculation in 394-1-27-12 and the corresponding proportion of stress response (A) and oxidation–reduction (B) DEGs.
Figure 9. Total genes co-expressed at 72 and 120 h post inoculation in 394-1-27-12 and the corresponding proportion of stress response (A) and oxidation–reduction (B) DEGs.
Stresses 03 00056 g009
Table 1. Pathway enrichment analysis for the upregulated and downregulated DEGs.
Table 1. Pathway enrichment analysis for the upregulated and downregulated DEGs.
Pathway IDEnriched Pathwayp-ValueNumber of Genes
Upregulated DEGs
394-1-27-12
PWY-7214Baicalein degradation 0.0005836
PWY-7445Luteolin triglucuronide degradation0.0005836
PWY0-1305L-glutamate degradation IX 0.00282
Butterbush
PWY-7214Baicalein degradation 0.00000030315
PWY-7445Luteolin triglucuronide degradation0.00000030315
PWY-361Phenylpropanoid biosynthesis0.04487
Downregulated DEGs
394-1-27-12
PWY-6433Hydroxylated fatty acid biosynthesis0.0002914
Table 2. Differentially co-expressed genes at 72 hpi and 120 hpi associated with stress response in the 394-1-27-12 (resistant) genotype.
Table 2. Differentially co-expressed genes at 72 hpi and 120 hpi associated with stress response in the 394-1-27-12 (resistant) genotype.
Gene AnnotationStress Responsive Gene394-1-27-12
Log2FC
72 hpi120 hpi
CmoCh19G007900Expansin-like A316.428.8
CmoCh19G002930Thaumatin-like protein14.112
CmoCh07G002420Heat Stress Transcription Factor B-2a5.63.3
CmoCh05G012280Wound-Induced Protein WIN16.16.3
CmoCh17G007590Plant Cadmium Resistance 853.4
CmoCh08G010590Myb Transcription Factor5.65.6
CmoCh15G009030Defensin-Like Protein 18.98.4
CmoCh11G011760Respiratory-Burst Oxidase, Putative4.33.8
CmoCh05G012270Wound-Induced Protein6.67.7
CmoCh14G018460Ethylene-Responsive Transcription Factor 1B4.43.1
CmoCh04G022620Defensin-Like Protein 689.1
Table 3. Differentially co-expressed genes at 72 hpi and 120 hpi associated with oxidation–reduction in the 394-1-27-12 (resistant) genotype.
Table 3. Differentially co-expressed genes at 72 hpi and 120 hpi associated with oxidation–reduction in the 394-1-27-12 (resistant) genotype.
Gene AnnotationOxidation Reduction Gene394-1-27-12
Log2FC
72 hpi120 hpi
CmoCh11G011760Respiratory-burst oxidase4.33.8
CmoCh05G007700(Cinnamate-4-hydroxylase) (Cytochrome P450 protein)4.93.1
CmoCh20G009870Cytochrome P450 family protein4.75.1
CmoCh09G001800Cytochrome P4505.84.9
CmoCh09G001790Cytochrome P4504.93.9
CmoCh09G002630Cytochrome P4505.75.2
CmoCh11G013370Putative heme-binding peroxidase4.64.6
CmoCh11G013380Putative heme-binding peroxidase4.94.7
CmoCh20G003430Peroxidase 6.34.8
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Acharya, S.R.; Shrestha, S.; Michael, V.N.; Fu, Y.; Sabharwal, P.; Thakur, S.; Meru, G. Transcriptional Changes during Phytophthora capsici Infection Reveal Potential Defense Mechanisms in Squash. Stresses 2023, 3, 827-841. https://doi.org/10.3390/stresses3040056

AMA Style

Acharya SR, Shrestha S, Michael VN, Fu Y, Sabharwal P, Thakur S, Meru G. Transcriptional Changes during Phytophthora capsici Infection Reveal Potential Defense Mechanisms in Squash. Stresses. 2023; 3(4):827-841. https://doi.org/10.3390/stresses3040056

Chicago/Turabian Style

Acharya, Shailesh Raj, Swati Shrestha, Vincent Njung’e Michael, Yuqing Fu, Prerna Sabharwal, Shallu Thakur, and Geoffrey Meru. 2023. "Transcriptional Changes during Phytophthora capsici Infection Reveal Potential Defense Mechanisms in Squash" Stresses 3, no. 4: 827-841. https://doi.org/10.3390/stresses3040056

APA Style

Acharya, S. R., Shrestha, S., Michael, V. N., Fu, Y., Sabharwal, P., Thakur, S., & Meru, G. (2023). Transcriptional Changes during Phytophthora capsici Infection Reveal Potential Defense Mechanisms in Squash. Stresses, 3(4), 827-841. https://doi.org/10.3390/stresses3040056

Article Metrics

Back to TopTop