Transcriptome analysis of resistant and susceptible M. truncatula genotypes in response to the necrotrophic fungus A. medicaginicola 

Transcriptome analysis of resistant and susceptible M. truncatula genotypes in response to the necrotrophic fungus A. medicaginicola | 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 Research Article Transcriptome analysis of resistant and susceptible M. truncatula genotypes in response to the necrotrophic fungus A. medicaginicola Jacob Botkin, Shaun Curtin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4426199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2024 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Ascochyta blights cause yield losses in all major legume crops. Spring black stem (SBS) and leaf spot disease is a major foliar disease of Medicago truncatula and M. sativa (alfalfa) caused by the necrotrophic fungus Ascochyta medicaginicola . This present study sought to identify candidate genes for SBS disease resistance for future functional validation. We employed RNA-seq to profile the transcriptomes of a resistant (HM078) and susceptible (A17) genotype of M. truncatula at 24, 48, and 72 hours post inoculation. Preliminary microscopic examination showed reduced pathogen growth on the resistant genotype. In total, 192 and 2,908 differentially expressed genes (DEGs) were observed in the resistant and susceptible genotype, respectively. Functional enrichment analysis revealed the susceptible genotype engaged in processes in the cell periphery and plasma membrane, as well as flavonoid biosynthesis whereas the resistant genotype utilized calcium ion binding, cell wall modifications, and external encapsulating structures. Candidate genes for disease resistance were selected based on criteria, among the top ten upregulated genes in the resistant genotype, upregulated over time in the resistant genotype, hormone pathway genes, plant disease resistance genes, receptor-like kinases, contrasting expression profiles in QTL for disease resistance, and upregulated genes in enriched pathways. Overall, 19 candidate genes for SBS disease resistance were identified with support from the literature. These genes will be sources for future targeted mutagenesis and candidate gene validation potentially helping to improve disease resistance to this devastating foliar pathogen. RNA-seq transcriptome Medicago truncatula Ascochyta medicaginicola necrotroph host response Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spring black stem and leaf spot (SBS) disease is a globally distributed disease of Medicago truncatula and Medicago sativa (alfalfa) [ 1 ]. Notably, SBS disease is one of the most severe foliar disease of alfalfa in Australia, Iran, Europe and Canada [ 1 – 3 ]. The causal agent of SBS disease is Ascochyta medicaginicola , previously known as Phoma medicaginis . With the expansive genomic resources available for M. truncatula , this interaction presents an opportunity to study the host response to necrotrophic fungal pathogens of legumes [ 4 ]. Evaluations of SBS disease in Medicago spp. use A. medicaginicola inoculum concentrations ranging from 1–5 x 10 6 conidia per milliliter [ 2 , 5 ]. However, concentrations as low as 3 x10 5 conidia per milliliter cause severe symptoms [ 6 , 7 ]. The symptoms of SBS disease include necrotic lesions and chlorosis of the foliar tissue as well as the stems, which results in defoliation of the lower canopy. In alfalfa, yield losses are especially pronounced in the first or second harvest after a wet spring, as A. medicaginicola requires high relative humidity for infection and disease development. SBS disease is transmitted by wind, insects, rain, and crop debris. To manage SBS disease, growers will plant disease-free seed and partially resistant cultivars, harvest early to minimize yield loss, and manage crop residue by tilling or grazing. Complete resistance to SBS disease has not been observed, and the majority of cultivars are susceptible. For resistant genotypes of M. sativa and M. truncatula , spore germination, penetration, and pycnidia development are delayed [ 2 , 8 ]. Diseased plant material has higher amounts of the phytoestrogen coumestrol, which can impact the fertility of livestock [ 6 ]. South Australian Research and Development Institute (SARDI) maintains a large diverse collection of M. truncatula . Eighty-six of the SARDI M. truncatula accessions were screened for SBS disease response, and most were found to be susceptible; however, genotype-specific resistance was seen in 16 accessions, including SA27063, also known by the Medicago HapMap identifier HM078. On a 1 to 5 scale increasing in disease severity, HM078 has a mean disease rating of 1.64 against A. medicaginicola isolate OMT5, whereas the susceptible accession A17 (HM101) has a mean disease rating of 4.15 [ 2 ]. SBS-resistant accession SA27063 (HM078) and SBS-susceptible accessions A17 (HM101) and SA3054 were used as parents to generate two populations for quantitative trait locus (QTL) mapping that discovered rnpm1 (HM101 & HM078) and rnpm2 (SA3054 & HM078), two recessively inherited QTL which account for approximately 30% of the phenotypic variance for resistance to SBS disease of M. truncatula [ 9 ]. In addition, SA27063 (HM078) and SA3054 were also used as resistant and susceptible genotypes in a microarray study of the host transcriptome at 12 hours post inoculation (hpi) with A. medicaginicola [ 5 ]. In that study, Kamphuis et al. [ 5 ] found upregulation of the phenylpropanoid and octadecanoid pathways associated with defense responses. Another transcriptome study of SBS disease of alfalfa showed that several pathogenesis-related (PR) proteins were significantly upregulated upon infection with A. medicaginicola [ 10 ]. Furthermore, the genome of A. medicaginicola isolate OMT5 was also studied, and while specific virulence factors have yet to be validated, bioinformatic analysis suggests that A. medicaginicola utilizes a wide range of cell wall degrading enzymes, effectors, and phytoalexin degrading enzymes [ 11 ]. Plant defense responses to necrotrophic pathogens are complex and often differ from the host responses to biotrophic pathogens. There are two general arms of the plant immune system. First, an initial detection of pathogen associated molecular patterns (PAMPs) on the cell surface by transmembrane proteins called pattern recognition receptors (PRRs). PRR proteins are described as receptor-like kinases (RLKs) or receptor-like proteins (RLPs) that bind to PAMP ligands such as lipopolysaccharides, β-glucan, or chitin, which trigger signaling cascades that promote PAMP-triggered immunity (PTI). PTI includes callose deposition, lignification, an oxidative burst by reactive oxygen species (ROS), the production of PR proteins such as chitinase, the synthesis of antimicrobial compounds like phytoalexins, and production of plant hormones [ 12 , 13 ]. Virulent pathogens possess effectors that dampen the PTI response. Plant disease resistance genes, also known as nucleotide-binding site and leucine-rich repeat (NLR) genes, function in sensor-helper pairs to detect effectors and initiate programmed cell death (PCD) [ 14 ]. The second arm of the plant immune system is the detection of these effectors by intracellular NLR proteins [ 15 ]. In the gene-for-gene model, NLR-mediated recognition of effector proteins results in effector-triggered immunity (ETI) and PCD, an effective response to constrain the spread of biotrophic pathogens. Specific NLR proteins have been shown to confer susceptibility to toxins of necrotrophic pathogens in the inverse gene-for-gene model also known as effector-triggered susceptibility (ETS) [ 16 , 17 ]. Conversely, NLR proteins have also been found to confer resistance against necrotrophic fungi, such as the Dothideomycete pathogen Leptosphaeria maculans [ 18 ]. Resistance to necrotrophic pathogens has been associated with phytohormones such as salicylic acid (SA), jasmonic acid (JA), abscisic acid (ABA), and ethylene (ET), which regulate stress responses through signaling pathways [ 19 ]. For instance, the accumulation of JA in Arabidopsis thaliana has been associated with resistance to necrotrophic fungus Sclerotinia sclerotiorum [ 20 ]. Overall, plant immune responses need to be investigated in regard to specific pathosystems. Comparative transcriptome analysis has been shown to be an effective method for identifying differentially expressed genes (DEGs) in response to plant-pathogen interactions. For example, the necrotrophic fungus Botrytis cinerea was shown to induce the phenylpropanoid pathway and terpenoid biosynthesis in lettuce ( Lactuca sativa ) [ 21 ]. In that study, RNA was extracted from leaf tissue 12, 24, and 48 hours post inoculation (hpi) and the authors identified 1, 139, and 4,598 upregulated DEGs, and 0, 12, and 1,935 downregulated DEGs, respectively [ 21 ]. An RNA-seq study of soft rot of potato caused by Pectobacterium carotovorum evaluated at 0, 6, 12, 24, and 72 hpi revealed that DEGs in a tolerant cultivar initiated negative regulation of cell death, while DEGs in a susceptible cultivar contributed to cell wall organization and biosynthesis [ 22 ]. Finally, a transcriptome analysis of powdery mildew of M. truncatula at 24 hpi showed the induction of PTI, as well as JA/ET signaling were correlated with resistance [ 23 ]. Overall, advances in omics technologies allow for transcriptome profiling to study molecular mechanisms contributing to disease resistance. In this study, our objective was to identify candidate genes for SBS disease resistance for future validation in functional studies. We examined the host transcriptome in a resistant (HM078) and susceptible (A17) M. truncatula genotype at 24, 48, and 72 hpi with A. medicaginicola . We identified DEGs in the resistant and susceptible cultivar compared to mock-treated samples at each time point and evaluated functionally enriched pathways. However, the number of DEGs was much lower in the resistant genotype. To identify candidate genes for disease resistance we examined the expression of SA and JA pathway genes, genes in QTL regions for disease resistance, RLKs, NLRs, and genes in functionally enriched pathways. We identified specific candidate genes based on five criteria: 1) Among the top ten upregulated genes in the resistant genotype, 2) upregulated DEGs over multiple time points in the resistant genotype, 3) DEGs in the susceptible genotype with higher constitutive expression in the resistant, 4) shared DEGs between resistant and susceptible with variable expression levels, or 5) genes in QTL regions rnpm1 and rnpm2 with contrasting expression profiles. We identified 19 candidate genes for SBS disease resistance based on our comparative transcriptome analysis, functional annotations, and support from the literature. Overall, this study sheds light on the plant immune response to A. medicaginicola using contemporary genomic resources, and provides a number of strong candidate genes for SBS disease resistance to be validated in future studies. Methods Plant growth conditions Germplasm of M. truncatula accessions A17 (HM101) and SA27063 (HM078) were obtained from the Medicago HapMap collection. Seed was scarified with 2 mL of concentrated sulfuric acid for 7 minutes, followed by washes with sterile de-ionized (DI) water. Seedlings were grown in autoclaved potting soil (Sun Gro Professional Growing Mix, Sun Gro Horticulture, Agawam, MA, USA) in a growth chamber at 22–24°C with 16 hours of light per day. Inoculation procedure Fungal cultures were maintained on potato dextrose agar (PDA) and exposed to ambient daylight on the benchtop throughout growth. Inoculum of A. medicaginicola was prepared from 4-week-old cultures by flooding plates with 5 mL of sterile DI water with 50 ppm Tween®20 surfactant (Sigma-Aldrich, St. Louis, MO) and dislodging conidia into suspension. Conidial suspensions were strained using a Falcon™ Cell Strainer with a 40 µm pore (Thermo Fisher Scientific, Waltham, MA, USA) to remove hyphal fragments. Conidial suspensions were quantified using a hemocytometer under 400x magnification and adjusted to 5 x 10 5 conidia/mL. The oldest trifoliate leaf originating from the node of the first secondary branch was marked with a white string tied to the petiole to be designated for inoculation. Approximately 1 mL of inoculum was atomized with a spray bottle at a distance of 15 cm away from the target leaf. Inoculated plants were placed in a humidity chamber at 100% relative humidity in the dark for 72 hours following inoculation. Microscopic evaluation of SBS disease at selected time points Spore germination and fungal growth on the leaf surface was observed for each genotype and time point. Cross sections of infected leaves were made to evaluate hyphal penetration. A sliding microtome was used to take 10 µm cross sections to visualize fungal penetration. The infected leaf material was immersed in GFP Polyclonal Antibody, Alexa Fluor® 488 (496/518 nm) (Thermo Fisher Scientific, Waltham, MA, USA) in a phosphate buffered saline solution, which causes hyphae to fluoresce under GFP (482/524 nm) wavelengths. RNA extraction and sequencing At each time point (24, 48, and 72 hpi), three inoculated leaves and three mock-inoculated leaves were sampled from biological replicates of each genotype. A total of 36 inoculated leaves were harvested from SBS-resistant M. truncatula HM078 (n = 18) and SBS-susceptible A17 (n = 18) from 36 individual plants. Samples were not pooled between biological replicates. Leaves were harvested in low-light conditions. Tissue was stored at -80°C until RNA extraction. Collected tissue was subjected to RNA extraction using the Qiagen RNeasy Mini Kit for Plants (Qiagen Inc., Valencia, CA, USA). For one sample an entire trifoliate leaf was ground in liquid nitrogen. Then, 75 mg of frozen tissue was sub-sampled, and added to 1 mL Buffer RLT (Qiagen). The rest of the protocol was followed according to the manufacturer’s specifications. Illumina RNA sequencing was conducted at the University of Minnesota-Twin Cities Genomics Center. TruSeq unique dual-indexed (UDI) stranded mRNA libraries were prepared, combined in a single pool, and sequenced on a single lane of NovaSeq S4 2x150-bp flow cell. Short-read RNA sequence data was uploaded to the Minnesota Supercomputing Institute for analysis. RNA sequence read alignment and quantification RNA sequence reads derived from mock and inoculated plant tissue samples were processed in a series of steps detailed in the associated code file. First, Cutadadpt v1.18 [ 24 ] was used to trim Illumina sequencing adapters, retaining RNA sequence reads with Phred-scaled quality scores above 30, and reads longer than 50 bp. FastQC reports of RNA sequence data statistics were summarized with MulitQC v1.14 [ 25 ]. The Mt5.0 reference genome of M. truncatula accession A17 was accessed from NCBI under RefSeq identifier GCF_003473485.1. The GFF file was converted to GTF format using the Cufflinks v2.2.1 function ‘gffread’ [ 26 ]. Next, STAR v2.5.3 [ 27 ] was used to perform spliced transcript alignments to the Mt5.0 genome with the parameter ‘sjdbOverhang 149’ and the parameter ‘—sjdbGTFfile’ set to the Mt5.0 GTF file. STAR v2.5.3 [ 27 ] was run in ‘twopassMode Basic’ and default parameters. RNA sequence mapping statistics were quality checked with MulitQC v1.14 [ 25 ]. Samtools v1.9 [ 28 ] was used to merge and filter RNA sequence alignment files to include paired RNA sequence reads with unique alignments. For A17, 94.1% of reads aligned with a mean of 56.4 million reads per sample. For HM078, 91.3% of reads aligned with a mean of 55.9 million reads per sample (Additional file 2: Table S1 ). HTSeq-count v0.11.0 [ 29 ] was used to quantify the number of reads that mapped to each exon. Finally, a feature count matrix was generated showing the number of RNA sequence reads for each tissue sample that mapped uniquely to exons of each gene. qPCR validation of RNA-seq gene expression data To validate the RNA-seq results we performed quantitative RT-PCR (qPCR) on a set of eight genes. RNA was extracted using the Qiagen RNeasy Mini Kit for Plants (Qiagen Inc., Valencia, CA, USA), and synthesis of cDNA was performed using SuperScript IV Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA). qPCR was performed using PerfeCta SYBR Green FastMix (Quanta BioSciences, Beverly Hills, California, USA) following the manufacturer’s recommendations. Amplification was performed for Nepenthesin (MtrunA17_Chr1g0187841), MtKCS12 (MtrunA17_Chr2g0327721), MtPP2C (MtrunA17_Chr3g0105371), MtEDS1L -like (MtrunA17_Chr3g0118251), MtCYP93C19 (MtrunA17_Chr4g0046331), MtIFR (MtrunA17_Chr5g0404481), MtLAC7 (MtrunA17_Chr7g0240991), and MtP21 -like (MtrunA17_Chr8g0386341). Primer pairs are detailed in Additional file 2: Table S2 . qPCR was performed in triplicate for one biological replicate from each genotype at each time point, and mean C t values for each sample were used for calculations. Relative quantification compared to MtACTIN11 (MtrunA17_Chr7g0223901) was calculated using 2^(-ΔΔC T ) and (2^(-Δδlog 2 CPM)) for qPCR and RNA-seq data, respectively. Pearson’s correlation between fold change expression values was performed using R v4.1.2 in R studio v1.4.1717 [ 30 ]. Differential expression and pathway analysis A count-based differential expression analysis was performed as previously described [ 31 ]. The EdgeR v3.36.0 pipeline [ 32 ] was used to conduct the differential expression analysis in R v4.1.2 [ 30 ]. First, the feature count matrix was filtered to remove genes without any expression across all tissue samples. Then, a DGEList object was created from the filtered feature count matrix and a model matrix object was created to store the experimental design variables for each sample. Next, the EdgeR v3.36.0 function ‘filterByExpr’ was used to determine which genes had adequate counts for statistical analysis across all samples. The function ‘calcNormFactors’ was used to determine the scaling factors to normalize counts based on library sizes, and the function ‘estimateDisp’ was used to evaluate common and tagwise dispersions. Then, the ‘glmQLFit’ function was used to fit the negative binomial generalized linear model (GLM) for each gene. Quasi-likelihood F-tests were performed for specified group comparisons using the ‘glmQLFTest’ function to calculate statistical differences in expression. For evaluating DEGs an absolute log 2 FC > 1 and an adjusted p-value (FDR) < 0.05 were used as cutoffs. A lower than standard log 2 FC cutoff was chosen due to relatively low numbers of DEGs in the resistant genotype. Statistical comparisons were made for each accession within each time point between mock and inoculated samples. DEGs were evaluated for enriched pathways using g:Profiler [ 33 , 34 ]. Representative GO (Gene Ontology) terms were evaluated by Biological Processes (BP), Molecular Function (MF), Cellular Component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Significantly enriched terms were retained if the adjusted p-value was below 0.05, or -log 10 (adjusted p-value) > 1.3. Results Microscopic evaluation reveals invasive hyphae penetrating leaf tissue samples To establish optimal harvest time points to capture the host-pathogen interaction, infected leaf cross sections of resistant (HM078) and susceptible (A17) M. truncatula were examined at 24, 48, and 72 hpi (Fig. 1 ). Invasive hyphae penetrating epidermal cells in cross sections of leaf samples were observed at each time point, except for the resistant genotype at 24 hpi. Notably, fewer invasive hyphae were observed on the resistant genotype in all samples along with reduced fungal growth on the leaf surface (Additional file 1: Figure S1 ). Overall, we concluded that the selected harvest time points were sufficient to capture the host response. Transcriptome analysis showed the largest differences in expression occurred at 72 hpi Mock and inoculated M. truncatula leaf tissue of an SBS-resistant and SBS-susceptible genotype was collected at 24, 48, and 72 hpi for RNA sequencing. A total of 5.7 billion paired-end reads, with a mean library depth of 61.9 million reads per sample (Q > 30), were generated. After mapping reads to the Mt5.0 reference genome, 25,084 genes had sufficient expression for statistical analysis across all time points. Across all samples, log 2 counts per million (CPM) values ranged from − 4.87 to 15.54, with a mean of 1.95 log 2 CPM. Principal component analysis (PCA) of all samples revealed that genotype was the primary separating factor, followed by hpi (Additional file 1: Figure S2 A). For both genotypes, PCA showed significant separation between mock and inoculated at 72 hpi. For the resistant genotype at 24 hpi, mock and inoculated samples overlap and show little variation, which may indicate transcription shifts due to pathogen inoculation were delayed (Additional file 1: Figure S2 B). For the resistant genotype at 48 hpi, there was variability in mock and inoculated samples. For the susceptible genotype at 48 hpi, a single mock-inoculated sample appears to be an outlier, as it overlaps with the inoculated replicates (Additional file 1: Figure S2 C). Differential expression analysis shows less response in the resistant genotype Differential gene expression from mock and inoculated, resistant and susceptible leaf tissue from three time points was analyzed (Fig. 2 ). Relevant statistics for the differential expression results at each time point are summarized for both host genotypes (Additional file 2: Table S3). Gene names and functional annotations are included when available. The resistant genotype HM078 had a total of 192 DEGs, which increased over time with 5, 27, and 150 DEGs at 24, 48, and 72 hpi, respectively (Additional file 1: Figure S3) (Additional file 2: Table S4). The susceptible genotype A17 had a total of 2,908 DEGs, with 393, 17, and 2,498 DEGs at 24, 48, and 72 hpi, respectively. The number of DEGs detected fits with observations made in the PCA. For instance, the high variability between biological replicates at 48 hpi likely contributed to low numbers of DEGs detected at this time point. Unique DEGs in the resistant genotype highlight potential genetic factors involved in disease resistance The majority of the top ten most upregulated DEGs in the resistant genotype in response to pathogen infection were not differential expressed in the susceptible genotype. These included MtPBP1 , MtPrx28 , a MATH domain-containing protein, and a MtRPP13 -like coiled-coil plant disease resistance protein (Table 1). Both calcium-binding protein MtPBP1 and peroxidase MtPrx28 are involved in ROS signaling and defense responses [ 35 – 38 ]. MATH domain-containing proteins have been shown to regulate NLR turnover in A. thaliana [ 39 , 40 ]. The top ten most upregulated DEGs in the resistant genotype occurred at 72 hpi, while the top ten downregulated DEGs occurred at all time points. Notably, MtPBP1 had a 600-fold increase in expression between mock and inoculated samples at 72 hpi. Overall, the identified genes are potentially strong candidates for SBS disease resistance. Upregulated DEGs in the resistant genotype across multiple time points were identified, and the majority were not differentially expressed in the susceptible genotype (Table 2). These included the JA biosynthesis gene linoleate 9S-lipoxygenase, MtLOX1-5 , were upregulated across all three time points [ 41 , 42 ]. A member of the 3-ketoacyl-CoA synthase family, MtKCS12 , as well as a receptor-like kinase of the RLK-Pelle-DLSV family, were upregulated at 48 and 72 hpi. Genes in the 3-ketoacyl-CoA synthase family perform biosynthesis of very long chain fatty acids (VLCFA) [ 43 , 44 ]. Transmembrane RLKs can act to recognize apoplastic pathogen effectors [ 45 , 46 ]. No DEGs were shown to be downregulated in HM078 across multiple time points, although several were downregulated at 48 hpi and later upregulated at 72 hpi (Table 2). Overall, DEGs unique to the resistant genotype that were among the top ten most upregulated or upregulated across multiple time points in response to pathogen infection highlight potential genetic factors involved in disease resistance. Unique DEGs in the susceptible genotype provide insight into the compatible host response When evaluating candidate genes for disease resistance from transcriptome data, it is beneficial to compare expression levels between contrasting host genotypes. Furthermore, analyzing DEGs in the susceptible genotype can provide insight into the compatible plant immune response to a necrotrophic fungus. In the susceptible genotype, the top ten most upregulated DEGs included MtMYB and MtCHS-1A (Table 3). The MYB transcription factor family is involved in regulating a variety of stress responses, while chalcone synthases ( CHS ) are a vital component of flavonoid biosynthesis. The most downregulated DEG in the susceptible genotype was a major facilitator superfamily (MFS) transporter, which transport a variety of substrates across membranes (Table 3). Only three DEGs were upregulated across all time points in the susceptible genotype; cytochrome P450 monooxygenase MtCYP76X2 , chalcone synthase MtCHS-1A , and alcohol dehydrogenase MtADH6. Cytochrome P450s conduct NADPH or O 2 dependent hydroxylation, while alcohol dehydrogenase oxidizes ethanol. An additional 67 DEGs were upregulated across multiple time points (Additional file 2: Table S5). Overall, the majority of DEGs in the susceptible genotype were not shared by the resistant genotype, and reveal a drastically different host response. Shared DEGs by both host genotypes include a variety of transcription factors A total of 65 genes were differentially expressed in both the resistant and susceptible genotypes (Additional file 2: Table S6). The top upregulated DEG in both genotypes was RNA-binding protein ARP1. A variety of transcription factor gene families were among the shared DEGs, which included C2H2, AS2-LOB, Calmodulin-binding, Homeobox-WOX, WD40, WRKY, C2C2-Dof, and AP2-EREBP. Ten TFs were differentially expressed in both genotypes; seven were upregulated and two were downregulated. A WD40-type strictosidine synthase-like 10 transcription factor, MtSTR10- like, was downregulated in the susceptible genotype while upregulated in the resistant genotype. Strictosidine synthases have been implicated in terpenoid biosynthesis of phytoalexins [ 47 , 48 ]. The resistant genotype showed higher upregulation for six of the seven upregulated transcription factors. For example, the C2H2 transcription factor MtZAT11 had a 36-fold increase in the resistant genotype, but only a 6-fold increase in the susceptible genotype at 72 hpi. C2H2 transcription factors have been shown to be involved in regulation of hormone pathways in response to biotic stress [ 49 , 50 ]. Finally, for the two downregulated TFs, the resistant genotype showed less downregulation. Functional enrichment analysis provides insight into contrasting host responses Functional enrichment analysis of DEGs revealed that the resistant and susceptible genotypes activate distinct pathways. We examined upregulated and downregulated DEGs across all time points for each genotype, and reported significantly enriched terms (Fig. 3 ). In the resistant genotype, the most significant cellular components included ‘cell wall’, ‘external encapsulating structure’, and ‘extracellular region’ (Fig. 3 A). The most significant biological processes were ‘cell wall organization’ and ‘external encapsulating structure or organization’. The most significant molecular function was ‘calcium ion binding’. Overall, due to fewer DEGs, the functional enrichment analysis in the resistant genotype was limited. For instance, KEGG enrichment analysis resulted in one significantly term, ‘Plant-pathogen interaction’. For the susceptible genotype, the most significant cellular components were ‘cell periphery’ and ’plasma membrane’ (Fig. 3 B). The most significant biological processes were ‘secondary metabolite biosynthetic process’ and ‘flavonoid biosynthetic process’. The most significant molecular function was ‘oxidoreductase activity’. KEGG enrichment analysis showed engagement in ‘Biosynthesis of secondary metabolites’, ‘Metabolic pathways’, and ‘Flavonoid biosynthesis’. Regulation of hormone pathways in response to A. medicaginicola The SA and JA pathways are crucial for plant immune responses. Previous studies have shown that in response to A. medicaginicola , defense mechanisms relying on these pathways are activated. In the compatible interaction with the susceptible genotype, there is a greater induction in the SA pathways, whereas the resistant genotype shows a rapid induction of the JA pathway [ 5 ]. Across all three time points, the resistant genotype upregulated the JA biosynthesis gene MtLOX1-5 (Table 4). MtLOX1-5 was highlighted earlier for being one of a few DEGs upregulated in the resistant genotype over multiple time points. At 72 hpi, the susceptible genotype upregulated SA biosynthesis and signaling genes, such as numerous PR proteins, as well as genes in the JA pathway. Interestingly, isoflavone reductase ( IFR ) and phenylalanine ammonia lyase ( PAL ) were upregulated in the susceptible genotype, while the resistant genotype had higher constitutive expression in mock-inoculated samples (Additional file 1: Figure S4). These genes are known to participate in isoflavonoid biosynthesis of anti-fungal phytoalexins [ 51 , 52 ]. Contrasting expression profiles in QTL provide candidate genes for disease resistance QTL regions rnpm1 and rnpm2 described by Kamphuis et al. [ 9 ] for SBS disease resistance were examined for differentially expressed genes. We examined the expression of 130 genes within a 1 Mbp region across rnpm1 , and 69 genes across approximately 440 kbp in the fine-mapped rnpm2 region [ 53 ]. Overall, differential expression of genes in these regions was only identified in the susceptible genotype (Additional file 2: Table S7). While no differential expression was observed across the QTL in the resistant genotype, there were genes with contrasting expression profiles between the resistant and susceptible genotypes (Fig. 4 ). In rnpm1 , these include a Toll/Interleukin1 receptor-nucleotide binding site-leucine-rich repeat (TIR-NBS-LRR) disease resistance protein (MtrunA17_Chr4g0008981), which is constitutively expressed in HM078 at much higher levels than observed in A17. A Blast2GO annotation shows this gene has high similarity (78.79%) to the disease resistance protein RPS6 isoform X1. Conversely, TIR-NBS-LRRs (MtrunA17_Chr4g0009001, MtrunA17_Chr4g0009011) were expressed in A17 but had little to no expression in HM078. Finally, in rnpm2 , a PAM16 -like (MtrunA17_Chr4g0064871) gene was identified as having a contrasting expression profile between the resistant and susceptible genotype. Overall, genes with contrasting expression in the QTL regions for disease resistance may point to structural variation or transcriptional repression, and warrant further investigation. Plant immune system receptors feature notable candidate genes for disease resistance We examined RLKs among the DEGs of both genotypes (Additional file 2: Table S8) (Additional file 1: Figure S5). For the resistant genotype, these included nine RLKs from five classes (Table 5). Of these, an RLK-Pelle-LRR was the most upregulated, and an RLK-Pelle-DLSV was upregulated over time. Neither were differentially expressed in the susceptible genotype. In the susceptible genotype, there were 166 differentially expressed RLK genes, and only three shared between both genotypes (Additional file 2: Table S8). Among the DEGs, we evaluated plant disease resistance genes (Additional file 1: Figure S6). In the resistant genotype, these included a TIR-NBS-LRR and three coiled-coil NLRs (Table 6). Notably, a CC-NBS plant disease resistance gene had the highest upregulation, with approximately a 75-fold increase in expression. This coiled-coil NLR gene has homology to RPP13 -like protein 1 in Pisum sativum (amino acid identity = 73.35%), which lacks an LRR domain. A total of 64 plant disease resistance proteins were differentially expressed in the susceptible genotype, and none were shared between both genotypes (Additional file 2: Table S9). Selection of candidate genes for SBS disease resistance After evaluating differential expression in a resistant and susceptible genotype of M. truncatula in response to A. medicaginicola , candidate genes for SBS disease resistance were selected (Table 7). Genes were chosen based on the following criteria: among the top ten upregulated genes in the resistant genotype, upregulation across multiple time points in the resistant genotype, differentially expressed in the susceptible genotype with higher constitutive expression in the resistant genotype, shared DEGs between both genotypes with different expression levels, genes that had contrasting expression profiles in QTL regions, uniquely upregulated RLKs or NLRs in the resistant genotype, and upregulated genes in functionally enriched pathways. Finally, candidate genes that have not been linked to plant disease resistance in the literature were excluded. Validation of transcriptome sequencing data with qPCR To validate our transcriptome sequencing results we performed qPCR on cDNA synthesized from the RNA samples collected throughout our experiment. We selected eight genes for validation across all time points for both genotypes. Relative quantification of target genes was performed compared to MtACTIN11 , and RNA-seq fold change had a significant positive correlation ( R = 0.8–0.98) with qPCR fold change for each gene tested (Additional file 1: Figure S7). Discussion In this study, we analyzed the transcriptomes of both resistant and susceptible M. truncatula accessions in response to inoculation with the necrotrophic pathogen A. medicaginicola at three time points. We observed an approximate 24-hour delay in fungal penetration of the resistant genotype, possibly due to physical barriers or antimicrobial compounds [ 54 , 55 ]. Invasive hyphae were more difficult to identify at earlier time points on both genotypes, likely resulting in less dramatic transcriptional shifts in whole-leaf samples and overlap between mock and inoculated samples observed on PCA plots at 24 and 48 hpi. Variability between individual plants and uneven inoculation application may have contributed to dispersal between biological replicates, particularly at 48 hpi, reducing statistical power and limiting our ability to detect differential expression. PCA analysis showed the largest degree of separation between mock and inoculated samples at 72 hpi, which corresponded to the highest numbers of DEGs for both genotypes. Similar studies have also noted large differences in DEGs between host genotypes in response to fungal pathogens, likely due to the number of invaded cells being sampled, highlighting a limitation of RNA-seq experiments [ 56 ]. Functional enrichment in the resistant genotype suggest antifungal mechanisms Functional enrichment analysis of DEGs revealed differences in the host response of resistant and susceptible genotypes to A. medicaginicola . In the resistant genotype, significantly enriched terms related to the extracellular region, cell wall, and calcium ion binding, which were all unique to the resistant genotype. Upregulated DEGs in extracellular region included peroxidase 28 ( Prx28 ) and ribonuclease T2 ( RNASET2 ). Prx28 regulates redox signaling pathways for defense responses including cell wall thickening and PCD [ 37 , 38 ]. RNASET2 is thought to inhibit pathogen colonization at infection sites [ 57 , 58 ]. Upregulated DEGs in the cell wall included xyloglucan/xyloglucosyl transferase ( XET ), β-galactosidase ( BGAL ), and ascorbate oxidase ( AAO ). XET cross-links xyloglucans to strengthen the cell wall [ 59 – 61 ]. BGAL hydrolyze β-galactosides to modify the cell wall [ 62 , 63 ]. AAO produces ROS that mediate defense signaling [ 64 , 65 ]. In the resistant genotype, the most upregulated DEG overall was PINOID-BINDING PROTEIN 1 (PBP1), which similar to calmodulin ( CML ), contains domains that bind calcium ions [ 66 , 67 ]. Calcium ion elevations activate MAPK signaling cascades, the oxidative burst, and the hypersensitive response [ 68 – 70 ]. Resistant M. truncatula genotype HM078 has been observed to have a hypersensitive-like response after inoculation with A. medicaginicola that could be attributed to an oxidative burst [ 9 ]. Overall, the function of genes in these pathways shed light on potential antifungal mechanisms in the resistant genotype. Plant hormone pathways engaged during the host response to A. medicaginicola Plant hormones, such as SA and JA, are crucial signaling molecules to regulate defense responses [ 71 ]. JA is synthesized from fatty acids in the octadecanoid pathway by enzymes including OPDA reductase, lipoxygenase, allene oxide synthase, and lipase [ 72 ]. JA signaling mediates defense responses against necrotrophic fungi, resulting in lignin formation, synthesis of PR proteins, flavonoids, terpenoids, and phytoalexins [ 73 ]. In the resistant genotype, MtLOX1-5 , was upregulated from 24–72 hpi, while the susceptible genotype, upregulated JA biosynthesis genes at 72 hpi. On the other hand, SA is synthesized from phenylalanine in the phenylpropanoid pathway by a series of enzymes resulting in isoflavonoid phytoalexins, lignin, benzoic acid, phenylpropenes, and coumarins [ 52 ]. Chalcone synthase ( CHS ), isoflavone reductase ( IFR ), 4-coumarate-CoA ligase ( 4CL ), and phenylalanine ammonia lyase ( PAL ) mediate flavonoid biosynthesis. In the susceptible genotype, phenylpropanoid pathway genes were enriched, including MtCHS-1A , MtCHS-1B , MtIFR , MtPAL , and Mt4CL-2 . Kamphuis et al. [ 5 ] found an induction of SA in resistant and susceptible genotypes, but found the resistant genotype HM078 contained constitutively higher levels of isoflavonoids. This was supported by our finding that MtPAL and MtIFR were upregulated at 72 hpi in the susceptible genotype, however, the resistant genotype had higher constitutive expression. Overall, both SA and JA likely play crucial roles as signaling molecules and the host response to A. medicaginicola . Candidate genes in QTL identified based on contrasting expression profiles QTL rnpm1 and rnpm2 were examined for their role in SBS disease resistance, focusing on gene expression patterns. DEGs were only detected at 72 hpi in the susceptible genotype. These QTL are known to be inherited recessively, which may support the inverse gene-for-gene model [ 9 ]. This paradigm is illustrated by the LOV1 gene in oat that confers sensitivity to the fungal toxin victorin, while also providing resistance to the crown rust fungus [ 17 ]. Genes expressed in the susceptible genotype and not expressed in the resistant genotype are of particular interest. In rnpm1 , TIR-NBS-NLR genes MtrunA17_Chr4g0009001 and MtrunA17_Chr4g0009011 were found to be expressed only in the susceptible genotype, supporting this concept. In rnpm2 , which does not contain NLRs, a gene orthologous to AtPAM16 showed no expression in the resistant genotype and high expression in the susceptible genotype. PAM16 is known to play a role in plant immunity, as shown in A. thaliana mutants lacking this gene, which exhibit enhanced disease resistance [ 74 , 75 ]. Backcrossing studies with an AtPam16 knockout mutant ( muse5-1 ) indicated that the recessive inheritance of the resistant phenotype aligns with the recessive inheritance pattern observed for rnpm2 . Overall, genes with differing expression patterns between the resistant and susceptible genotypes in these QTL regions are candidate genes for further study. RPP13 -like plant disease protein likely involved in incompatible host response A promising plant disease resistance gene, potentially involved in the incompatible host response was identified. This coiled-coil class NLR gene was uniquely upregulated in the resistant genotype. Notably, this disease resistant protein is similar to RPP13 -like protein 1, which confers broad spectrum resistance to biotrophic pathogens Melampsora lini (flax rust), as well as Hyaloperonospora arabidopsidis and Peronospora parasitica (downy mildew) in A. thaliana [ 76 , 77 ]. Coiled-coil class NLRs have also been shown to play important roles in plant immunity to necrotrophic pathogens, for example, overexpression of GbCNL130 confers resistance to Verticillium dahliae in cotton [ 78 ]. Pathogen ligand binding to these proteins results in downstream defense responses. For instance, the activation of HOPZ-ACTIVATED RESISTANCE 1 (ZAR1) causes a calcium ion influx, ROS production, and cell death conferring resistance to Pseudomonas syringae in A. thaliana [ 79 , 80 ]. Future directions will include investigating the specific role of this coiled-coil class NLR. Conclusion Examining host-pathogen interactions between M. truncatula and the necrotrophic fungal pathogen A. medicaginicola has the potential to illuminate molecular factors that could be used to enhance disease resistance to Ascochyta blights in legumes. We performed a transcriptome analysis for a resistant (HM078) and susceptible (A17) M. truncatula genotype infected with A. medicaginicola to evaluate the host response and identify candidate genes for disease resistance. We examined DEGs, functionally enriched pathways, hormone pathways, RLKs, NLRs, and QTL regions for SBS disease resistance. We identified a number of candidate genes for disease resistance with support from the literature. After functional validation of candidate genes, future studies will explore engineering SBS disease resistance in the economically important forage crop alfalfa. Abbreviations SBS Spring black stem SARDI South Australian Research and Development Institute QTL Quantitative trait locus hpi Hours post inoculation PR Pathogenesis-related rnpm1 Resistance to the necrotroph Phoma medicaginis one rnpm2 Resistance to the necrotroph Phoma medicaginis two PCD Programmed cell death PAMPs Pathogen associated molecular patterns PRR Pattern recognition receptors RLK Receptor-like kinases RLP Receptor-like proteins PTI PAMP-triggered immunity ROS Reactive oxygen species NLR Nucleotide-binding site and leucine-rich repeat ETI Effector-triggered immunity ETS effector-triggered susceptibility SA Salicylic acid JA Jasmonic acid ABA Abscisic acid ET Ethylene DEGs Differentially expressed genes qPCR Quantitative RT-PCR DI De-ionized PDA Potato dextrose agar GO Gene Ontology BP Biological Processes MF Molecular Function CC Cellular Component KEGG Kyoto Encyclopedia of Genes and Genomes PCA Principal component analysis CPM Counts per million VLCFA Very long chain fatty acids CHS Chalcone synthase MFS Major facilitator superfamily IFR Isoflavone reductase PAL Phenylalanine ammonia lyase TIR-NBS-LRR Toll/Interleukin1 receptor-nucleotide binding site-leucine-rich repeat Prx28 Peroxidase 28 RNASET2 Ribonuclease T2 XET Xyloglucan/xyloglucosyl transferase BGAL β-galactosidase AAO Ascorbate oxidase PBP1 PINOID-BINDING PROTEIN 1 CML Calmodulin 4CL 4-coumarate-CoA ligase LOV1 LOCUS ORCHESTRATING VICTORIN EFFECTS1 PAM16 Presequence translocase-associated motor 16 ZAR1 HOPZ-ACTIVATED RESISTANCE 1 Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Availability of data and materials All raw sequence data has been deposited in the NCBI database under BioProject PRJNA975868. SRA numbers SRR24775309, SRR24775310, SRR24775311, SRR24775312, SRR24775313, SRR24775314, SRR24775315, SRR24775316, SRR24775317, SRR24775318, SRR24775319, SRR24775320, SRR24775321, SRR24775322, SRR24775326, SRR24775327, SRR24775328, SRR24775329, SRR24775330, SRR24775331, SRR24775332, SRR24775333, SRR24775334, SRR24775338, SRR24775339, SRR24775341, SRR24775342, SRR24775343, SRR24775344, SRR24775345, SRR24775349, SRR24775350, SRR24793325, SRR24793326, SRR24793327, SRR24793328 contain the RNA-seq reads used throughout this study. The code run throughout this study (RNA-seq_associated_code.html) and the RNA-seq feature count data (feature_count_matrix.txt) is available on GitHub (https://github.com/shaun-curtin/RNA-seq-analysis-of-Spring-Black-Stem-Disease-SBS-). Germplasm of M. truncatula used in this study can be requested at (https://medicago.legumeinfo.org/tools/germplasm/). Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Funding This work was supported by USDA-ARS project 5062-21000-035-000D. Authors' contributions J.B. conducted the analysis and wrote the original manuscript with input from S.J.C. J.B. and S.J.C conceived the study, planned experiments, and edited the manuscript. Acknowledgements This research was supported by the U.S. Department of Agriculture, Agricultural Research Service. Mention of any trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U. S. Department of Agriculture. 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The ten most upregulated and downregulated DEGs in resistant genotype HM078. Table2.xlsx Table 2. DEGs in the resistant genotype across multiple time points. Table3.xlsx Table 3. The ten most upregulated and downregulated DEGs in susceptible genotype A17. Table4.xlsx Table 4. Differentially expressed genes in SA and JA pathways for the resistant and susceptible genotype. Table5.xlsx Table 5. Differentially expressed RLKs in the resistant genotype Table6.xlsx Table 6. Differentially expressed NLR plant disease resistance genes in the resistant genotype. Table7.xlsx Table 7. Candidate genes for SBS disease resistance in M. truncatula HM078. Additionalfile1.docx Additional file 1: Supplementary Figures S1-S7. Figure S1. A. medicaginicola hyphal growth on inoculated leaf surface of M. truncatula . Images were taken under GFP fluorescence and overlaid on an RGB image of (A-B) A17 at 24 hpi, (C-D) A17 at 48 hpi, (E-F) A17 at 72 hpi, (G-H) HM078 at 24 hpi, (I-J) HM078 at 48 hpi, and (K-L) HM078 at 72 hpi. Figure S2. PCA plots of the biological coefficient of variation. PCA plots of RNA-seq samples for (A) resistant (HM078) and susceptible (A17) genotypes together, (B) the resistant genotype, and (C) the susceptible genotype. Sample naming conventions are R: resistant, S: susceptible, I: inoculated, M: mock-inoculated, followed by 24, 48, or 72 to indicate the time point. Figure S3. DEGs of HM078 and A17 at 24, 48, and 72 hours post inoculation (hpi). Statistical comparisons were inoculated versus mock inoculated within each accession at each time point. Volcano diagrams of (A) DEGs of HM078 at 24 hpi, (B) DEGs of A17 at 24 hpi, (C) DEGs of HM078 at 48 hpi, (D) DEGs of A17 at 48 hpi, (E) DEGs of HM078 at 72 hpi, and (F) DEGs of A17 at 72 hpi. Figure S4. Mean expression of SA biosynthesis and signaling genes. (A) Isoflavone reductase (MtrunA17_Chr5g0404511) and (B) phenylalanine ammonia lyase (MtrunA17_Chr1g0181091) are upregulated in the susceptible genotype, but have higher constitutive expression in the resistant genotype. Figure S5. RLK expression profiles among DEGs for HM078 and A17. Differentially expressed genes in resistant genotype HM078 are marked with an asterisk. Sample ID abbreviations are SM: susceptible mock-inoculated, SI: susceptible inoculated, RM: resistant mock-inoculated, RI: resistant inoculated, followed by hours post inoculation (24, 48, or 72 hpi). Figure S6. Plant disease resistance gene expression profiles for DEGs of HM078 and A17. DEGs in the resistant genotype HM078 are marked with an asterisk. Sample ID abbreviations are SM: susceptible mock-inoculated, SI: susceptible inoculated, RM: resistant mock-inoculated, RI: resistant inoculated, followed by hours post inoculation (24, 48, or 72 hpi). Figure S7. Pearson’s correlation of fold change values was performed to compare RNA-seq and qPCR expression data. Genes included were Nepenthesin (MtrunA17_Chr1g0187841), MtKCS12 (MtrunA17_Chr2g0327721), MtPP2C (MtrunA17_Chr3g0105371), MtEDS1L -like (MtrunA17_Chr3g0118251), MtCYP93C19 (MtrunA17_Chr4g0046331), MtIFR (MtrunA17_Chr5g0404481), MtLAC7 (MtrunA17_Chr7g0240991), and MtP21 -like (MtrunA17_Chr8g0386341). Additionalfile2.xlsx Additional file 2: Supplementary Tables S1-S9. Table S1. RNA sequence alignment statistics for all samples. Table S2. Primer pairs used for qPCR validation of RNA-seq data. Table S3. All DEGs in the resistant and susceptible genotype from 24-72 hpi. Table S4. Number of DEGs in each condition. Table S5. DEGs in susceptible genotype across multiple time points. Table S6. Shared DEGs in the resistant and susceptible genotype. Table S7. Differentially expressed genes in QTL regions. Table S8. Differentially expressed RLKs in the resistant and susceptible genotype. Table S9. Differentially expressed NLRs in the resistant and susceptible genotype. Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 08 Jul, 2024 Reviews received at journal 07 Jul, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviews received at journal 07 Jun, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 27 May, 2024 Editor invited by journal 27 May, 2024 Submission checks completed at journal 27 May, 2024 Editor assigned by journal 27 May, 2024 First submitted to journal 15 May, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4426199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309360645,"identity":"285b32a7-dfba-49fe-b969-ffb497f22d2c","order_by":0,"name":"Jacob Botkin","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Botkin","suffix":""},{"id":309360646,"identity":"aba153ae-bfda-4c2a-bb3d-bf349fc5057c","order_by":1,"name":"Shaun Curtin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCTiLsYHhA5gkWgsbYwPjDBK1MDAw8xCjRX528zPpAga7xO3zm9se27bVyfY3sF98zINHi8GdY2bSMxiSE+ccY2w3zm07bDzjAE+xMV4tEglm0jwMBxJnsDG2See2HUhsOMCTJjkDn8NmpH9DaLFsq0ucT0gLw40cJFsY25gTNxxgPybxAZ/DbuQUW/MYJBvPYEtsk+w5d9h442EeZgN8WoAO23ibp8JOdgbz8WcSP8rqZOcdb3/4IAGfwyB2IXOYeQxwqcMJ2B+QrGUUjIJRMAqGNQAAMwdIdcwEDQIAAAAASUVORK5CYII=","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Shaun","middleName":"","lastName":"Curtin","suffix":""}],"badges":[],"createdAt":"2024-05-15 15:32:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4426199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4426199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05444-3","type":"published","date":"2024-07-29T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57906217,"identity":"5905d755-1fd7-4c4b-8562-075cb4ad17c1","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6877074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross sections of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. truncatula \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eleaves infected with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. medicaginicola\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Images were taken under GFP fluorescence (left) and RGB (right) for (A) A17 at 24 hpi, (B) A17 at 48 hpi, (C) A17 at 72 hpi, (D) HM078 at 24 hpi, (E) HM078 at 48 hpi, and (F) HM078 at 72 hpi. Red arrows indicate invasive hyphae penetrating leaf epidermal cells. Scale bars for (A-F) are 75, 50, 150, 50, 150, and 150 micrometers, respectively.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/10b6b918b0f850edcda2eccb.png"},{"id":57906216,"identity":"be6c7494-ce01-4fd0-9527-2969f6591df1","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":580175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of DEGs for resistant and susceptible \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. truncatula\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. medicaginicola\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Venn diagrams of (A) Upregulated DEGs of resistant genotype HM078, (B) Upregulated DEGs of susceptible genotype A17, (C) Downregulated DEGs of resistant genotype HM078, and (D) Downregulated DEGs of susceptible genotype A17.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/5d584f8230253ec1347cc90f.png"},{"id":57906218,"identity":"fcef6213-2fa5-4c27-aed9-117dd0cff61f","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of resistant and susceptible \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. truncatula\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in response to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. medicaginicola\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Significantly enriched GO terms were analyzed for (A) DEGs in HM078, and (B) DEGs in A17. Upregulated and downregulated DEGs across all time points were included for each genotype. GO (Gene Ontology) terms were grouped by Biological Processes (BP), Molecular Function (MF), Cellular Component (CC), or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/19cdf2c79c605ccd77e75ffe.png"},{"id":57906220,"identity":"f0626ea8-2e4e-49ed-ae90-de9be90d57f2","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":882606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression profiles for QTL regions\u003c/strong\u003e. Heatmaps are displayed in\u003cstrong\u003e \u003c/strong\u003elog\u003csub\u003e2\u003c/sub\u003eCPM for QTL (A) \u003cem\u003ernpm1 \u003c/em\u003eand (B) \u003cem\u003ernpm2\u003c/em\u003e. Genes with contrasting expression profiles between resistant and susceptible genotypes are outlined with a box. Differentially expressed genes in specific tissues are indicated with asterisks. Sample ID abbreviations are SM: susceptible mock-inoculated, SI: susceptible inoculated, RM: resistant mock-inoculated, RI: resistant inoculated, followed by hours post inoculation (24, 48, or 72 hpi).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/17810160e9587a278f9175c9.png"},{"id":61793801,"identity":"95ee165d-fba6-4abf-b58d-2b7f6bb28087","added_by":"auto","created_at":"2024-08-05 16:15:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8792796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/40c3098d-986b-4355-b502-c2c75fb29534.pdf"},{"id":57906708,"identity":"12bc3937-efbe-4b5f-bdb2-e0a91fd33341","added_by":"auto","created_at":"2024-06-07 10:00:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. The ten most upregulated and downregulated DEGs in resistant genotype HM078.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/b35a453ab2efa92bd05cb289.xlsx"},{"id":57906227,"identity":"336274bc-bb5c-4051-943d-f481a85d3681","added_by":"auto","created_at":"2024-06-07 09:52:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2. DEGs in the resistant genotype across multiple time points.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/b9ae4458779cab0df45c20b1.xlsx"},{"id":57906214,"identity":"426d4898-c670-4863-9545-cded64404450","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3. The ten most upregulated and downregulated DEGs in susceptible genotype A17.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/f03b14be5a30585cba2eb62e.xlsx"},{"id":57906711,"identity":"03dd2619-e12b-45c7-b5aa-fcacf0171263","added_by":"auto","created_at":"2024-06-07 10:00:44","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 4. Differentially expressed genes in SA and JA pathways for the resistant and susceptible genotype.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/58cb2bb4e04d4387af02d672.xlsx"},{"id":57906710,"identity":"71641fe7-99a2-4c25-8d86-3dde19ed5af4","added_by":"auto","created_at":"2024-06-07 10:00:44","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 5. Differentially expressed RLKs in the resistant genotype\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/7f0f68ec9f5a64b84fd6bb29.xlsx"},{"id":57906226,"identity":"371e9550-8d6b-45ea-b22e-b4a2cf66f5d4","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 6. Differentially expressed NLR plant disease resistance genes in the resistant genotype.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/81d180859d2238e112670d6e.xlsx"},{"id":57906709,"identity":"9b723f6a-f5d9-4e10-aa1f-21e927a2324d","added_by":"auto","created_at":"2024-06-07 10:00:44","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 7. Candidate genes for SBS disease resistance in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. truncatula\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HM078.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Table7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/b8b99244b562a24224c7e71d.xlsx"},{"id":57906224,"identity":"e9a1d17f-a330-4df3-9152-4dfd72ac9376","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":4184205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Figures S1-S7. \u003cstrong\u003eFigure S1. \u003c/strong\u003e\u003cem\u003eA. medicaginicola\u003c/em\u003ehyphal growth on inoculated leaf surface of \u003cem\u003eM. truncatula\u003c/em\u003e. Images were taken under GFP fluorescence and overlaid on an RGB image of (A-B) A17 at 24 hpi, (C-D) A17 at 48 hpi, (E-F) A17 at 72 hpi, (G-H) HM078 at 24 hpi, (I-J) HM078 at 48 hpi, and (K-L) HM078 at 72 hpi. \u003cstrong\u003eFigure S2. \u003c/strong\u003ePCA plots of the biological coefficient of variation. PCA plots of RNA-seq samples for (A) resistant (HM078) and susceptible (A17) genotypes together, (B) the resistant genotype, and (C) the susceptible genotype. Sample naming conventions are R: resistant, S: susceptible, I: inoculated, M: mock-inoculated, followed by 24, 48, or 72 to indicate the time point. \u003cstrong\u003eFigure S3.\u003c/strong\u003e DEGs of HM078 and A17 at 24, 48, and 72 hours post inoculation (hpi). Statistical comparisons were inoculated versus mock inoculated within each accession at each time point. Volcano diagrams of (A) DEGs of HM078 at 24 hpi, (B) DEGs of A17 at 24 hpi, (C) DEGs of HM078 at 48 hpi, (D) DEGs of A17 at 48 hpi, (E) DEGs of HM078 at 72 hpi, and (F) DEGs of A17 at 72 hpi. \u003cstrong\u003eFigure S4. \u003c/strong\u003eMean expression of SA biosynthesis and signaling genes. (A) Isoflavone reductase (MtrunA17_Chr5g0404511) and (B) phenylalanine ammonia lyase (MtrunA17_Chr1g0181091) are upregulated in the susceptible genotype, but have higher constitutive expression in the resistant genotype. \u003cstrong\u003eFigure S5.\u003c/strong\u003e RLK expression profiles among DEGs for HM078 and A17. Differentially expressed genes in resistant genotype HM078 are marked with an asterisk. Sample ID abbreviations are SM: susceptible mock-inoculated, SI: susceptible inoculated, RM: resistant mock-inoculated, RI: resistant inoculated, followed by hours post inoculation (24, 48, or 72 hpi). \u003cstrong\u003eFigure S6. \u003c/strong\u003ePlant disease resistance gene expression profiles for DEGs of HM078 and A17. DEGs in the resistant genotype HM078 are marked with an asterisk. Sample ID abbreviations are SM: susceptible mock-inoculated, SI: susceptible inoculated, RM: resistant mock-inoculated, RI: resistant inoculated, followed by hours post inoculation (24, 48, or 72 hpi). \u003cstrong\u003eFigure S7. \u003c/strong\u003ePearson’s correlation of fold change values was performed to compare RNA-seq and qPCR expression data. Genes included were Nepenthesin (MtrunA17_Chr1g0187841), \u003cem\u003eMtKCS12\u003c/em\u003e(MtrunA17_Chr2g0327721), \u003cem\u003eMtPP2C\u003c/em\u003e (MtrunA17_Chr3g0105371), \u003cem\u003eMtEDS1L\u003c/em\u003e-like (MtrunA17_Chr3g0118251), \u003cem\u003eMtCYP93C19\u003c/em\u003e (MtrunA17_Chr4g0046331), \u003cem\u003eMtIFR\u003c/em\u003e(MtrunA17_Chr5g0404481), \u003cem\u003eMtLAC7\u003c/em\u003e (MtrunA17_Chr7g0240991), and \u003cem\u003eMtP21\u003c/em\u003e-like (MtrunA17_Chr8g0386341).\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/ff9168b447d1a8437cf6dc7d.docx"},{"id":57906222,"identity":"05cba033-7a8b-4422-b525-bcdff1fa83fb","added_by":"auto","created_at":"2024-06-07 09:52:44","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":296657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Tables S1-S9. \u003cstrong\u003eTable S1.\u003c/strong\u003e RNA sequence alignment statistics for all samples. \u003cstrong\u003eTable S2.\u003c/strong\u003e Primer pairs used for qPCR validation of RNA-seq data. \u003cstrong\u003eTable S3.\u003c/strong\u003e All DEGs in the resistant and susceptible genotype from 24-72 hpi.\u003cstrong\u003e Table S4. \u003c/strong\u003eNumber of DEGs in each condition. \u003cstrong\u003eTable S5\u003c/strong\u003e. DEGs in susceptible genotype across multiple time points. \u003cstrong\u003eTable S6. \u003c/strong\u003eShared DEGs in the resistant and susceptible genotype.\u003cstrong\u003e Table S7. \u003c/strong\u003eDifferentially expressed genes in QTL regions.\u003cstrong\u003e Table S8. \u003c/strong\u003eDifferentially expressed RLKs in the resistant and susceptible genotype\u003cstrong\u003e. Table S9. \u003c/strong\u003eDifferentially expressed NLRs in the resistant and susceptible genotype.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4426199/v1/c2c6b40674b6496713fb400e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome analysis of resistant and susceptible M. truncatula genotypes in response to the necrotrophic fungus A. medicaginicola ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpring black stem and leaf spot (SBS) disease is a globally distributed disease of \u003cem\u003eMedicago truncatula\u003c/em\u003e and \u003cem\u003eMedicago sativa\u003c/em\u003e (alfalfa) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Notably, SBS disease is one of the most severe foliar disease of alfalfa in Australia, Iran, Europe and Canada [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The causal agent of SBS disease is \u003cem\u003eAscochyta medicaginicola\u003c/em\u003e, previously known as \u003cem\u003ePhoma medicaginis\u003c/em\u003e. With the expansive genomic resources available for \u003cem\u003eM. truncatula\u003c/em\u003e, this interaction presents an opportunity to study the host response to necrotrophic fungal pathogens of legumes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvaluations of SBS disease in \u003cem\u003eMedicago\u003c/em\u003e spp. use \u003cem\u003eA. medicaginicola\u003c/em\u003e inoculum concentrations ranging from 1\u0026ndash;5 x 10\u003csup\u003e6\u003c/sup\u003e conidia per milliliter [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, concentrations as low as 3 x10\u003csup\u003e5\u003c/sup\u003e conidia per milliliter cause severe symptoms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The symptoms of SBS disease include necrotic lesions and chlorosis of the foliar tissue as well as the stems, which results in defoliation of the lower canopy. In alfalfa, yield losses are especially pronounced in the first or second harvest after a wet spring, as \u003cem\u003eA. medicaginicola\u003c/em\u003e requires high relative humidity for infection and disease development. SBS disease is transmitted by wind, insects, rain, and crop debris. To manage SBS disease, growers will plant disease-free seed and partially resistant cultivars, harvest early to minimize yield loss, and manage crop residue by tilling or grazing.\u003c/p\u003e \u003cp\u003eComplete resistance to SBS disease has not been observed, and the majority of cultivars are susceptible. For resistant genotypes of \u003cem\u003eM. sativa\u003c/em\u003e and \u003cem\u003eM. truncatula\u003c/em\u003e, spore germination, penetration, and pycnidia development are delayed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Diseased plant material has higher amounts of the phytoestrogen coumestrol, which can impact the fertility of livestock [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. South Australian Research and Development Institute (SARDI) maintains a large diverse collection of \u003cem\u003eM. truncatula\u003c/em\u003e. Eighty-six of the SARDI \u003cem\u003eM. truncatula\u003c/em\u003e accessions were screened for SBS disease response, and most were found to be susceptible; however, genotype-specific resistance was seen in 16 accessions, including SA27063, also known by the Medicago HapMap identifier HM078. On a 1 to 5 scale increasing in disease severity, HM078 has a mean disease rating of 1.64 against \u003cem\u003eA. medicaginicola\u003c/em\u003e isolate OMT5, whereas the susceptible accession A17 (HM101) has a mean disease rating of 4.15 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSBS-resistant accession SA27063 (HM078) and SBS-susceptible accessions A17 (HM101) and SA3054 were used as parents to generate two populations for quantitative trait locus (QTL) mapping that discovered \u003cem\u003ernpm1\u003c/em\u003e (HM101 \u0026amp; HM078) and \u003cem\u003ernpm2\u003c/em\u003e (SA3054 \u0026amp; HM078), two recessively inherited QTL which account for approximately 30% of the phenotypic variance for resistance to SBS disease of \u003cem\u003eM. truncatula\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, SA27063 (HM078) and SA3054 were also used as resistant and susceptible genotypes in a microarray study of the host transcriptome at 12 hours post inoculation (hpi) with \u003cem\u003eA. medicaginicola\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In that study, Kamphuis et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found upregulation of the phenylpropanoid and octadecanoid pathways associated with defense responses. Another transcriptome study of SBS disease of alfalfa showed that several pathogenesis-related (PR) proteins were significantly upregulated upon infection with \u003cem\u003eA. medicaginicola\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the genome of \u003cem\u003eA. medicaginicola\u003c/em\u003e isolate OMT5 was also studied, and while specific virulence factors have yet to be validated, bioinformatic analysis suggests that \u003cem\u003eA. medicaginicola\u003c/em\u003e utilizes a wide range of cell wall degrading enzymes, effectors, and phytoalexin degrading enzymes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlant defense responses to necrotrophic pathogens are complex and often differ from the host responses to biotrophic pathogens. There are two general arms of the plant immune system. First, an initial detection of pathogen associated molecular patterns (PAMPs) on the cell surface by transmembrane proteins called pattern recognition receptors (PRRs). PRR proteins are described as receptor-like kinases (RLKs) or receptor-like proteins (RLPs) that bind to PAMP ligands such as lipopolysaccharides, β-glucan, or chitin, which trigger signaling cascades that promote PAMP-triggered immunity (PTI). PTI includes callose deposition, lignification, an oxidative burst by reactive oxygen species (ROS), the production of PR proteins such as chitinase, the synthesis of antimicrobial compounds like phytoalexins, and production of plant hormones [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Virulent pathogens possess effectors that dampen the PTI response. Plant disease resistance genes, also known as nucleotide-binding site and leucine-rich repeat (NLR) genes, function in sensor-helper pairs to detect effectors and initiate programmed cell death (PCD) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The second arm of the plant immune system is the detection of these effectors by intracellular NLR proteins [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the gene-for-gene model, NLR-mediated recognition of effector proteins results in effector-triggered immunity (ETI) and PCD, an effective response to constrain the spread of biotrophic pathogens. Specific NLR proteins have been shown to confer susceptibility to toxins of necrotrophic pathogens in the inverse gene-for-gene model also known as effector-triggered susceptibility (ETS) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, NLR proteins have also been found to confer resistance against necrotrophic fungi, such as the Dothideomycete pathogen \u003cem\u003eLeptosphaeria maculans\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Resistance to necrotrophic pathogens has been associated with phytohormones such as salicylic acid (SA), jasmonic acid (JA), abscisic acid (ABA), and ethylene (ET), which regulate stress responses through signaling pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For instance, the accumulation of JA in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e has been associated with resistance to necrotrophic fungus \u003cem\u003eSclerotinia sclerotiorum\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Overall, plant immune responses need to be investigated in regard to specific pathosystems.\u003c/p\u003e \u003cp\u003eComparative transcriptome analysis has been shown to be an effective method for identifying differentially expressed genes (DEGs) in response to plant-pathogen interactions. For example, the necrotrophic fungus \u003cem\u003eBotrytis cinerea\u003c/em\u003e was shown to induce the phenylpropanoid pathway and terpenoid biosynthesis in lettuce (\u003cem\u003eLactuca sativa\u003c/em\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In that study, RNA was extracted from leaf tissue 12, 24, and 48 hours post inoculation (hpi) and the authors identified 1, 139, and 4,598 upregulated DEGs, and 0, 12, and 1,935 downregulated DEGs, respectively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. An RNA-seq study of soft rot of potato caused by \u003cem\u003ePectobacterium carotovorum\u003c/em\u003e evaluated at 0, 6, 12, 24, and 72 hpi revealed that DEGs in a tolerant cultivar initiated negative regulation of cell death, while DEGs in a susceptible cultivar contributed to cell wall organization and biosynthesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Finally, a transcriptome analysis of powdery mildew of \u003cem\u003eM. truncatula\u003c/em\u003e at 24 hpi showed the induction of PTI, as well as JA/ET signaling were correlated with resistance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Overall, advances in omics technologies allow for transcriptome profiling to study molecular mechanisms contributing to disease resistance.\u003c/p\u003e \u003cp\u003eIn this study, our objective was to identify candidate genes for SBS disease resistance for future validation in functional studies. We examined the host transcriptome in a resistant (HM078) and susceptible (A17) \u003cem\u003eM. truncatula\u003c/em\u003e genotype at 24, 48, and 72 hpi with \u003cem\u003eA. medicaginicola\u003c/em\u003e. We identified DEGs in the resistant and susceptible cultivar compared to mock-treated samples at each time point and evaluated functionally enriched pathways. However, the number of DEGs was much lower in the resistant genotype. To identify candidate genes for disease resistance we examined the expression of SA and JA pathway genes, genes in QTL regions for disease resistance, RLKs, NLRs, and genes in functionally enriched pathways. We identified specific candidate genes based on five criteria: 1) Among the top ten upregulated genes in the resistant genotype, 2) upregulated DEGs over multiple time points in the resistant genotype, 3) DEGs in the susceptible genotype with higher constitutive expression in the resistant, 4) shared DEGs between resistant and susceptible with variable expression levels, or 5) genes in QTL regions \u003cem\u003ernpm1\u003c/em\u003e and \u003cem\u003ernpm2\u003c/em\u003e with contrasting expression profiles. We identified 19 candidate genes for SBS disease resistance based on our comparative transcriptome analysis, functional annotations, and support from the literature. Overall, this study sheds light on the plant immune response to \u003cem\u003eA. medicaginicola\u003c/em\u003e using contemporary genomic resources, and provides a number of strong candidate genes for SBS disease resistance to be validated in future studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant growth conditions\u003c/h2\u003e \u003cp\u003eGermplasm of \u003cem\u003eM. truncatula\u003c/em\u003e accessions A17 (HM101) and SA27063 (HM078) were obtained from the Medicago HapMap collection. Seed was scarified with 2 mL of concentrated sulfuric acid for 7 minutes, followed by washes with sterile de-ionized (DI) water. Seedlings were grown in autoclaved potting soil (Sun Gro Professional Growing Mix, Sun Gro Horticulture, Agawam, MA, USA) in a growth chamber at 22\u0026ndash;24\u0026deg;C with 16 hours of light per day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInoculation procedure\u003c/h2\u003e \u003cp\u003eFungal cultures were maintained on potato dextrose agar (PDA) and exposed to ambient daylight on the benchtop throughout growth. Inoculum of \u003cem\u003eA. medicaginicola\u003c/em\u003e was prepared from 4-week-old cultures by flooding plates with 5 mL of sterile DI water with 50 ppm Tween\u0026reg;20 surfactant (Sigma-Aldrich, St. Louis, MO) and dislodging conidia into suspension. Conidial suspensions were strained using a Falcon\u0026trade; Cell Strainer with a 40 \u0026micro;m pore (Thermo Fisher Scientific, Waltham, MA, USA) to remove hyphal fragments. Conidial suspensions were quantified using a hemocytometer under 400x magnification and adjusted to 5 x 10\u003csup\u003e5\u003c/sup\u003e conidia/mL. The oldest trifoliate leaf originating from the node of the first secondary branch was marked with a white string tied to the petiole to be designated for inoculation. Approximately 1 mL of inoculum was atomized with a spray bottle at a distance of 15 cm away from the target leaf. Inoculated plants were placed in a humidity chamber at 100% relative humidity in the dark for 72 hours following inoculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMicroscopic evaluation of SBS disease at selected time points\u003c/h2\u003e \u003cp\u003eSpore germination and fungal growth on the leaf surface was observed for each genotype and time point. Cross sections of infected leaves were made to evaluate hyphal penetration. A sliding microtome was used to take 10 \u0026micro;m cross sections to visualize fungal penetration. The infected leaf material was immersed in GFP Polyclonal Antibody, Alexa Fluor\u0026reg; 488 (496/518 nm) (Thermo Fisher Scientific, Waltham, MA, USA) in a phosphate buffered saline solution, which causes hyphae to fluoresce under GFP (482/524 nm) wavelengths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and sequencing\u003c/h2\u003e \u003cp\u003eAt each time point (24, 48, and 72 hpi), three inoculated leaves and three mock-inoculated leaves were sampled from biological replicates of each genotype. A total of 36 inoculated leaves were harvested from SBS-resistant \u003cem\u003eM. truncatula\u003c/em\u003e HM078 (n\u0026thinsp;=\u0026thinsp;18) and SBS-susceptible A17 (n\u0026thinsp;=\u0026thinsp;18) from 36 individual plants. Samples were not pooled between biological replicates. Leaves were harvested in low-light conditions. Tissue was stored at -80\u0026deg;C until RNA extraction. Collected tissue was subjected to RNA extraction using the Qiagen RNeasy Mini Kit for Plants (Qiagen Inc., Valencia, CA, USA). For one sample an entire trifoliate leaf was ground in liquid nitrogen. Then, 75 mg of frozen tissue was sub-sampled, and added to 1 mL Buffer RLT (Qiagen). The rest of the protocol was followed according to the manufacturer\u0026rsquo;s specifications. Illumina RNA sequencing was conducted at the University of Minnesota-Twin Cities Genomics Center. TruSeq unique dual-indexed (UDI) stranded mRNA libraries were prepared, combined in a single pool, and sequenced on a single lane of NovaSeq S4 2x150-bp flow cell. Short-read RNA sequence data was uploaded to the Minnesota Supercomputing Institute for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequence read alignment and quantification\u003c/h2\u003e \u003cp\u003eRNA sequence reads derived from mock and inoculated plant tissue samples were processed in a series of steps detailed in the associated code file. First, Cutadadpt v1.18 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to trim Illumina sequencing adapters, retaining RNA sequence reads with Phred-scaled quality scores above 30, and reads longer than 50 bp. FastQC reports of RNA sequence data statistics were summarized with MulitQC v1.14 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The \u003cem\u003eMt5.0\u003c/em\u003e reference genome of \u003cem\u003eM. truncatula\u003c/em\u003e accession A17 was accessed from NCBI under RefSeq identifier GCF_003473485.1. The GFF file was converted to GTF format using the Cufflinks v2.2.1 function \u0026lsquo;gffread\u0026rsquo; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Next, STAR v2.5.3 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used to perform spliced transcript alignments to the \u003cem\u003eMt5.0\u003c/em\u003e genome with the parameter \u0026lsquo;sjdbOverhang 149\u0026rsquo; and the parameter \u0026lsquo;\u0026mdash;sjdbGTFfile\u0026rsquo; set to the \u003cem\u003eMt5.0\u003c/em\u003e GTF file. STAR v2.5.3 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was run in \u0026lsquo;twopassMode Basic\u0026rsquo; and default parameters. RNA sequence mapping statistics were quality checked with MulitQC v1.14 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Samtools v1.9 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was used to merge and filter RNA sequence alignment files to include paired RNA sequence reads with unique alignments. For A17, 94.1% of reads aligned with a mean of 56.4\u0026nbsp;million reads per sample. For HM078, 91.3% of reads aligned with a mean of 55.9\u0026nbsp;million reads per sample (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). HTSeq-count v0.11.0 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used to quantify the number of reads that mapped to each exon. Finally, a feature count matrix was generated showing the number of RNA sequence reads for each tissue sample that mapped uniquely to exons of each gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eqPCR validation of RNA-seq gene expression data\u003c/h2\u003e \u003cp\u003eTo validate the RNA-seq results we performed quantitative RT-PCR (qPCR) on a set of eight genes. RNA was extracted using the Qiagen RNeasy Mini Kit for Plants (Qiagen Inc., Valencia, CA, USA), and synthesis of cDNA was performed using SuperScript IV Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA). qPCR was performed using PerfeCta SYBR Green FastMix (Quanta BioSciences, Beverly Hills, California, USA) following the manufacturer\u0026rsquo;s recommendations. Amplification was performed for Nepenthesin (MtrunA17_Chr1g0187841), \u003cem\u003eMtKCS12\u003c/em\u003e (MtrunA17_Chr2g0327721), \u003cem\u003eMtPP2C\u003c/em\u003e (MtrunA17_Chr3g0105371), \u003cem\u003eMtEDS1L\u003c/em\u003e-like (MtrunA17_Chr3g0118251), \u003cem\u003eMtCYP93C19\u003c/em\u003e (MtrunA17_Chr4g0046331), \u003cem\u003eMtIFR\u003c/em\u003e (MtrunA17_Chr5g0404481), \u003cem\u003eMtLAC7\u003c/em\u003e (MtrunA17_Chr7g0240991), and \u003cem\u003eMtP21\u003c/em\u003e-like (MtrunA17_Chr8g0386341). Primer pairs are detailed in Additional file 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. qPCR was performed in triplicate for one biological replicate from each genotype at each time point, and mean C\u003csub\u003et\u003c/sub\u003e values for each sample were used for calculations. Relative quantification compared to \u003cem\u003eMtACTIN11\u003c/em\u003e (MtrunA17_Chr7g0223901) was calculated using 2^(-ΔΔC\u003csub\u003eT\u003c/sub\u003e) and (2^(-Δδlog\u003csub\u003e2\u003c/sub\u003eCPM)) for qPCR and RNA-seq data, respectively. Pearson\u0026rsquo;s correlation between fold change expression values was performed using R v4.1.2 in R studio v1.4.1717 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and pathway analysis\u003c/h2\u003e \u003cp\u003eA count-based differential expression analysis was performed as previously described [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The EdgeR v3.36.0 pipeline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was used to conduct the differential expression analysis in R v4.1.2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. First, the feature count matrix was filtered to remove genes without any expression across all tissue samples. Then, a DGEList object was created from the filtered feature count matrix and a model matrix object was created to store the experimental design variables for each sample. Next, the EdgeR v3.36.0 function \u0026lsquo;filterByExpr\u0026rsquo; was used to determine which genes had adequate counts for statistical analysis across all samples. The function \u0026lsquo;calcNormFactors\u0026rsquo; was used to determine the scaling factors to normalize counts based on library sizes, and the function \u0026lsquo;estimateDisp\u0026rsquo; was used to evaluate common and tagwise dispersions. Then, the \u0026lsquo;glmQLFit\u0026rsquo; function was used to fit the negative binomial generalized linear model (GLM) for each gene. Quasi-likelihood F-tests were performed for specified group comparisons using the \u0026lsquo;glmQLFTest\u0026rsquo; function to calculate statistical differences in expression. For evaluating DEGs an absolute log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;1 and an adjusted p-value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as cutoffs. A lower than standard log\u003csub\u003e2\u003c/sub\u003eFC cutoff was chosen due to relatively low numbers of DEGs in the resistant genotype. Statistical comparisons were made for each accession within each time point between mock and inoculated samples. DEGs were evaluated for enriched pathways using g:Profiler [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Representative GO (Gene Ontology) terms were evaluated by Biological Processes (BP), Molecular Function (MF), Cellular Component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Significantly enriched terms were retained if the adjusted p-value was below 0.05, or -log\u003csub\u003e10\u003c/sub\u003e(adjusted p-value)\u0026thinsp;\u0026gt;\u0026thinsp;1.3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eMicroscopic evaluation reveals invasive hyphae penetrating leaf tissue samples\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo establish optimal harvest time points to capture the host-pathogen interaction, infected leaf cross sections of resistant (HM078) and susceptible (A17) \u003cem\u003eM. truncatula\u003c/em\u003e were examined at 24, 48, and 72 hpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Invasive hyphae penetrating epidermal cells in cross sections of leaf samples were observed at each time point, except for the resistant genotype at 24 hpi. Notably, fewer invasive hyphae were observed on the resistant genotype in all samples along with reduced fungal growth on the leaf surface (Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Overall, we concluded that the selected harvest time points were sufficient to capture the host response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis showed the largest differences in expression occurred at 72 hpi\u003c/h2\u003e \u003cp\u003eMock and inoculated \u003cem\u003eM. truncatula\u003c/em\u003e leaf tissue of an SBS-resistant and SBS-susceptible genotype was collected at 24, 48, and 72 hpi for RNA sequencing. A total of 5.7\u0026nbsp;billion paired-end reads, with a mean library depth of 61.9\u0026nbsp;million reads per sample (Q\u0026thinsp;\u0026gt;\u0026thinsp;30), were generated. After mapping reads to the \u003cem\u003eMt5.0\u003c/em\u003e reference genome, 25,084 genes had sufficient expression for statistical analysis across all time points. Across all samples, log\u003csub\u003e2\u003c/sub\u003e counts per million (CPM) values ranged from \u0026minus;\u0026thinsp;4.87 to 15.54, with a mean of 1.95 log\u003csub\u003e2\u003c/sub\u003eCPM. Principal component analysis (PCA) of all samples revealed that genotype was the primary separating factor, followed by hpi (Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). For both genotypes, PCA showed significant separation between mock and inoculated at 72 hpi. For the resistant genotype at 24 hpi, mock and inoculated samples overlap and show little variation, which may indicate transcription shifts due to pathogen inoculation were delayed (Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). For the resistant genotype at 48 hpi, there was variability in mock and inoculated samples. For the susceptible genotype at 48 hpi, a single mock-inoculated sample appears to be an outlier, as it overlaps with the inoculated replicates (Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis shows less response in the resistant genotype\u003c/h2\u003e \u003cp\u003eDifferential gene expression from mock and inoculated, resistant and susceptible leaf tissue from three time points was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Relevant statistics for the differential expression results at each time point are summarized for both host genotypes (Additional file 2: Table S3). Gene names and functional annotations are included when available. The resistant genotype HM078 had a total of 192 DEGs, which increased over time with 5, 27, and 150 DEGs at 24, 48, and 72 hpi, respectively (Additional file 1: Figure S3) (Additional file 2: Table S4). The susceptible genotype A17 had a total of 2,908 DEGs, with 393, 17, and 2,498 DEGs at 24, 48, and 72 hpi, respectively. The number of DEGs detected fits with observations made in the PCA. For instance, the high variability between biological replicates at 48 hpi likely contributed to low numbers of DEGs detected at this time point.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUnique DEGs in the resistant genotype highlight potential genetic factors involved in disease resistance\u003c/h2\u003e \u003cp\u003eThe majority of the top ten most upregulated DEGs in the resistant genotype in response to pathogen infection were not differential expressed in the susceptible genotype. These included \u003cem\u003eMtPBP1\u003c/em\u003e, \u003cem\u003eMtPrx28\u003c/em\u003e, a MATH domain-containing protein, and a \u003cem\u003eMtRPP13\u003c/em\u003e-like coiled-coil plant disease resistance protein (Table\u0026nbsp;1). Both calcium-binding protein \u003cem\u003eMtPBP1\u003c/em\u003e and peroxidase \u003cem\u003eMtPrx28\u003c/em\u003e are involved in ROS signaling and defense responses [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. MATH domain-containing proteins have been shown to regulate NLR turnover in \u003cem\u003eA. thaliana\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The top ten most upregulated DEGs in the resistant genotype occurred at 72 hpi, while the top ten downregulated DEGs occurred at all time points. Notably, \u003cem\u003eMtPBP1\u003c/em\u003e had a 600-fold increase in expression between mock and inoculated samples at 72 hpi. Overall, the identified genes are potentially strong candidates for SBS disease resistance.\u003c/p\u003e \u003cp\u003eUpregulated DEGs in the resistant genotype across multiple time points were identified, and the majority were not differentially expressed in the susceptible genotype (Table\u0026nbsp;2). These included the JA biosynthesis gene linoleate 9S-lipoxygenase, \u003cem\u003eMtLOX1-5\u003c/em\u003e, were upregulated across all three time points [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A member of the 3-ketoacyl-CoA synthase family, \u003cem\u003eMtKCS12\u003c/em\u003e, as well as a receptor-like kinase of the RLK-Pelle-DLSV family, were upregulated at 48 and 72 hpi. Genes in the 3-ketoacyl-CoA synthase family perform biosynthesis of very long chain fatty acids (VLCFA) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Transmembrane RLKs can act to recognize apoplastic pathogen effectors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. No DEGs were shown to be downregulated in HM078 across multiple time points, although several were downregulated at 48 hpi and later upregulated at 72 hpi (Table\u0026nbsp;2). Overall, DEGs unique to the resistant genotype that were among the top ten most upregulated or upregulated across multiple time points in response to pathogen infection highlight potential genetic factors involved in disease resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUnique DEGs in the susceptible genotype provide insight into the compatible host response\u003c/h2\u003e \u003cp\u003eWhen evaluating candidate genes for disease resistance from transcriptome data, it is beneficial to compare expression levels between contrasting host genotypes. Furthermore, analyzing DEGs in the susceptible genotype can provide insight into the compatible plant immune response to a necrotrophic fungus. In the susceptible genotype, the top ten most upregulated DEGs included \u003cem\u003eMtMYB\u003c/em\u003e and \u003cem\u003eMtCHS-1A\u003c/em\u003e (Table\u0026nbsp;3). The MYB transcription factor family is involved in regulating a variety of stress responses, while chalcone synthases (\u003cem\u003eCHS\u003c/em\u003e) are a vital component of flavonoid biosynthesis. The most downregulated DEG in the susceptible genotype was a major facilitator superfamily (MFS) transporter, which transport a variety of substrates across membranes (Table\u0026nbsp;3). Only three DEGs were upregulated across all time points in the susceptible genotype; cytochrome P450 monooxygenase \u003cem\u003eMtCYP76X2\u003c/em\u003e, chalcone synthase \u003cem\u003eMtCHS-1A\u003c/em\u003e, and alcohol dehydrogenase \u003cem\u003eMtADH6.\u003c/em\u003e Cytochrome P450s conduct NADPH or O\u003csub\u003e2\u003c/sub\u003e dependent hydroxylation, while alcohol dehydrogenase oxidizes ethanol. An additional 67 DEGs were upregulated across multiple time points (Additional file 2: Table S5). Overall, the majority of DEGs in the susceptible genotype were not shared by the resistant genotype, and reveal a drastically different host response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eShared DEGs by both host genotypes include a variety of transcription factors\u003c/h2\u003e \u003cp\u003eA total of 65 genes were differentially expressed in both the resistant and susceptible genotypes (Additional file 2: Table S6). The top upregulated DEG in both genotypes was RNA-binding protein ARP1. A variety of transcription factor gene families were among the shared DEGs, which included C2H2, AS2-LOB, Calmodulin-binding, Homeobox-WOX, WD40, WRKY, C2C2-Dof, and AP2-EREBP. Ten TFs were differentially expressed in both genotypes; seven were upregulated and two were downregulated. A WD40-type strictosidine synthase-like 10 transcription factor, \u003cem\u003eMtSTR10-\u003c/em\u003elike, was downregulated in the susceptible genotype while upregulated in the resistant genotype. Strictosidine synthases have been implicated in terpenoid biosynthesis of phytoalexins [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The resistant genotype showed higher upregulation for six of the seven upregulated transcription factors. For example, the C2H2 transcription factor \u003cem\u003eMtZAT11\u003c/em\u003e had a 36-fold increase in the resistant genotype, but only a 6-fold increase in the susceptible genotype at 72 hpi. C2H2 transcription factors have been shown to be involved in regulation of hormone pathways in response to biotic stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Finally, for the two downregulated TFs, the resistant genotype showed less downregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis provides insight into contrasting host responses\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of DEGs revealed that the resistant and susceptible genotypes activate distinct pathways. We examined upregulated and downregulated DEGs across all time points for each genotype, and reported significantly enriched terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the resistant genotype, the most significant cellular components included \u0026lsquo;cell wall\u0026rsquo;, \u0026lsquo;external encapsulating structure\u0026rsquo;, and \u0026lsquo;extracellular region\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most significant biological processes were \u0026lsquo;cell wall organization\u0026rsquo; and \u0026lsquo;external encapsulating structure or organization\u0026rsquo;. The most significant molecular function was \u0026lsquo;calcium ion binding\u0026rsquo;. Overall, due to fewer DEGs, the functional enrichment analysis in the resistant genotype was limited. For instance, KEGG enrichment analysis resulted in one significantly term, \u0026lsquo;Plant-pathogen interaction\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the susceptible genotype, the most significant cellular components were \u0026lsquo;cell periphery\u0026rsquo; and \u0026rsquo;plasma membrane\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The most significant biological processes were \u0026lsquo;secondary metabolite biosynthetic process\u0026rsquo; and \u0026lsquo;flavonoid biosynthetic process\u0026rsquo;. The most significant molecular function was \u0026lsquo;oxidoreductase activity\u0026rsquo;. KEGG enrichment analysis showed engagement in \u0026lsquo;Biosynthesis of secondary metabolites\u0026rsquo;, \u0026lsquo;Metabolic pathways\u0026rsquo;, and \u0026lsquo;Flavonoid biosynthesis\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRegulation of hormone pathways in response to\u003c/b\u003e \u003cb\u003eA. medicaginicola\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe SA and JA pathways are crucial for plant immune responses. Previous studies have shown that in response to \u003cem\u003eA. medicaginicola\u003c/em\u003e, defense mechanisms relying on these pathways are activated. In the compatible interaction with the susceptible genotype, there is a greater induction in the SA pathways, whereas the resistant genotype shows a rapid induction of the JA pathway [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Across all three time points, the resistant genotype upregulated the JA biosynthesis gene \u003cem\u003eMtLOX1-5\u003c/em\u003e (Table\u0026nbsp;4). \u003cem\u003eMtLOX1-5\u003c/em\u003e was highlighted earlier for being one of a few DEGs upregulated in the resistant genotype over multiple time points. At 72 hpi, the susceptible genotype upregulated SA biosynthesis and signaling genes, such as numerous PR proteins, as well as genes in the JA pathway. Interestingly, isoflavone reductase (\u003cem\u003eIFR\u003c/em\u003e) and phenylalanine ammonia lyase (\u003cem\u003ePAL\u003c/em\u003e) were upregulated in the susceptible genotype, while the resistant genotype had higher constitutive expression in mock-inoculated samples (Additional file 1: Figure S4). These genes are known to participate in isoflavonoid biosynthesis of anti-fungal phytoalexins [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eContrasting expression profiles in QTL provide candidate genes for disease resistance\u003c/h2\u003e \u003cp\u003eQTL regions \u003cem\u003ernpm1\u003c/em\u003e and \u003cem\u003ernpm2\u003c/em\u003e described by Kamphuis et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] for SBS disease resistance were examined for differentially expressed genes. We examined the expression of 130 genes within a 1 Mbp region across \u003cem\u003ernpm1\u003c/em\u003e, and 69 genes across approximately 440 kbp in the fine-mapped \u003cem\u003ernpm2\u003c/em\u003e region [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Overall, differential expression of genes in these regions was only identified in the susceptible genotype (Additional file 2: Table S7). While no differential expression was observed across the QTL in the resistant genotype, there were genes with contrasting expression profiles between the resistant and susceptible genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In \u003cem\u003ernpm1\u003c/em\u003e, these include a Toll/Interleukin1 receptor-nucleotide binding site-leucine-rich repeat (TIR-NBS-LRR) disease resistance protein (MtrunA17_Chr4g0008981), which is constitutively expressed in HM078 at much higher levels than observed in A17. A Blast2GO annotation shows this gene has high similarity (78.79%) to the disease resistance protein RPS6 isoform X1. Conversely, TIR-NBS-LRRs (MtrunA17_Chr4g0009001, MtrunA17_Chr4g0009011) were expressed in A17 but had little to no expression in HM078. Finally, in \u003cem\u003ernpm2\u003c/em\u003e, a \u003cem\u003ePAM16\u003c/em\u003e-like (MtrunA17_Chr4g0064871) gene was identified as having a contrasting expression profile between the resistant and susceptible genotype. Overall, genes with contrasting expression in the QTL regions for disease resistance may point to structural variation or transcriptional repression, and warrant further investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePlant immune system receptors feature notable candidate genes for disease resistance\u003c/h2\u003e \u003cp\u003eWe examined RLKs among the DEGs of both genotypes (Additional file 2: Table S8) (Additional file 1: Figure S5). For the resistant genotype, these included nine RLKs from five classes (Table\u0026nbsp;5). Of these, an RLK-Pelle-LRR was the most upregulated, and an RLK-Pelle-DLSV was upregulated over time. Neither were differentially expressed in the susceptible genotype. In the susceptible genotype, there were 166 differentially expressed RLK genes, and only three shared between both genotypes (Additional file 2: Table S8).\u003c/p\u003e \u003cp\u003eAmong the DEGs, we evaluated plant disease resistance genes (Additional file 1: Figure S6). In the resistant genotype, these included a TIR-NBS-LRR and three coiled-coil NLRs (Table\u0026nbsp;6). Notably, a CC-NBS plant disease resistance gene had the highest upregulation, with approximately a 75-fold increase in expression. This coiled-coil NLR gene has homology to \u003cem\u003eRPP13\u003c/em\u003e-like protein 1 in \u003cem\u003ePisum sativum\u003c/em\u003e (amino acid identity\u0026thinsp;=\u0026thinsp;73.35%), which lacks an LRR domain. A total of 64 plant disease resistance proteins were differentially expressed in the susceptible genotype, and none were shared between both genotypes (Additional file 2: Table S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSelection of candidate genes for SBS disease resistance\u003c/h2\u003e \u003cp\u003eAfter evaluating differential expression in a resistant and susceptible genotype of \u003cem\u003eM. truncatula\u003c/em\u003e in response to \u003cem\u003eA. medicaginicola\u003c/em\u003e, candidate genes for SBS disease resistance were selected (Table\u0026nbsp;7). Genes were chosen based on the following criteria: among the top ten upregulated genes in the resistant genotype, upregulation across multiple time points in the resistant genotype, differentially expressed in the susceptible genotype with higher constitutive expression in the resistant genotype, shared DEGs between both genotypes with different expression levels, genes that had contrasting expression profiles in QTL regions, uniquely upregulated RLKs or NLRs in the resistant genotype, and upregulated genes in functionally enriched pathways. Finally, candidate genes that have not been linked to plant disease resistance in the literature were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eValidation of transcriptome sequencing data with qPCR\u003c/h2\u003e \u003cp\u003eTo validate our transcriptome sequencing results we performed qPCR on cDNA synthesized from the RNA samples collected throughout our experiment. We selected eight genes for validation across all time points for both genotypes. Relative quantification of target genes was performed compared to \u003cem\u003eMtACTIN11\u003c/em\u003e, and RNA-seq fold change had a significant positive correlation (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;0.98) with qPCR fold change for each gene tested (Additional file 1: Figure S7).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed the transcriptomes of both resistant and susceptible \u003cem\u003eM. truncatula\u003c/em\u003e accessions in response to inoculation with the necrotrophic pathogen \u003cem\u003eA. medicaginicola\u003c/em\u003e at three time points. We observed an approximate 24-hour delay in fungal penetration of the resistant genotype, possibly due to physical barriers or antimicrobial compounds [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Invasive hyphae were more difficult to identify at earlier time points on both genotypes, likely resulting in less dramatic transcriptional shifts in whole-leaf samples and overlap between mock and inoculated samples observed on PCA plots at 24 and 48 hpi. Variability between individual plants and uneven inoculation application may have contributed to dispersal between biological replicates, particularly at 48 hpi, reducing statistical power and limiting our ability to detect differential expression. PCA analysis showed the largest degree of separation between mock and inoculated samples at 72 hpi, which corresponded to the highest numbers of DEGs for both genotypes. Similar studies have also noted large differences in DEGs between host genotypes in response to fungal pathogens, likely due to the number of invaded cells being sampled, highlighting a limitation of RNA-seq experiments [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eFunctional enrichment in the resistant genotype suggest antifungal mechanisms\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of DEGs revealed differences in the host response of resistant and susceptible genotypes to \u003cem\u003eA. medicaginicola\u003c/em\u003e. In the resistant genotype, significantly enriched terms related to the extracellular region, cell wall, and calcium ion binding, which were all unique to the resistant genotype. Upregulated DEGs in extracellular region included peroxidase 28 (\u003cem\u003ePrx28\u003c/em\u003e) and ribonuclease T2 (\u003cem\u003eRNASET2\u003c/em\u003e). \u003cem\u003ePrx28\u003c/em\u003e regulates redox signaling pathways for defense responses including cell wall thickening and PCD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eRNASET2\u003c/em\u003e is thought to inhibit pathogen colonization at infection sites [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Upregulated DEGs in the cell wall included xyloglucan/xyloglucosyl transferase (\u003cem\u003eXET\u003c/em\u003e), β-galactosidase (\u003cem\u003eBGAL\u003c/em\u003e), and ascorbate oxidase (\u003cem\u003eAAO\u003c/em\u003e). \u003cem\u003eXET\u003c/em\u003e cross-links xyloglucans to strengthen the cell wall [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. \u003cem\u003eBGAL\u003c/em\u003e hydrolyze β-galactosides to modify the cell wall [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. \u003cem\u003eAAO\u003c/em\u003e produces ROS that mediate defense signaling [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In the resistant genotype, the most upregulated DEG overall was PINOID-BINDING PROTEIN 1 (PBP1), which similar to calmodulin (\u003cem\u003eCML\u003c/em\u003e), contains domains that bind calcium ions [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Calcium ion elevations activate MAPK signaling cascades, the oxidative burst, and the hypersensitive response [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Resistant \u003cem\u003eM. truncatula\u003c/em\u003e genotype HM078 has been observed to have a hypersensitive-like response after inoculation with \u003cem\u003eA. medicaginicola\u003c/em\u003e that could be attributed to an oxidative burst [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Overall, the function of genes in these pathways shed light on potential antifungal mechanisms in the resistant genotype.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlant hormone pathways engaged during the host response to\u003c/b\u003e \u003cb\u003eA. medicaginicola\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePlant hormones, such as SA and JA, are crucial signaling molecules to regulate defense responses [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. JA is synthesized from fatty acids in the octadecanoid pathway by enzymes including OPDA reductase, lipoxygenase, allene oxide synthase, and lipase [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eJA signaling mediates defense responses against necrotrophic fungi, resulting in lignin formation, synthesis of PR proteins, flavonoids, terpenoids, and phytoalexins [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In the resistant genotype, \u003cem\u003eMtLOX1-5\u003c/em\u003e, was upregulated from 24\u0026ndash;72 hpi, while the susceptible genotype, upregulated JA biosynthesis genes at 72 hpi. On the other hand, SA is synthesized from phenylalanine in the phenylpropanoid pathway by a series of enzymes resulting in isoflavonoid phytoalexins, lignin, benzoic acid, phenylpropenes, and coumarins [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Chalcone synthase (\u003cem\u003eCHS\u003c/em\u003e), isoflavone reductase (\u003cem\u003eIFR\u003c/em\u003e), 4-coumarate-CoA ligase (\u003cem\u003e4CL\u003c/em\u003e), and phenylalanine ammonia lyase (\u003cem\u003ePAL\u003c/em\u003e) mediate flavonoid biosynthesis. In the susceptible genotype, phenylpropanoid pathway genes were enriched, including \u003cem\u003eMtCHS-1A\u003c/em\u003e, \u003cem\u003eMtCHS-1B\u003c/em\u003e, \u003cem\u003eMtIFR\u003c/em\u003e, \u003cem\u003eMtPAL\u003c/em\u003e, and \u003cem\u003eMt4CL-2\u003c/em\u003e. Kamphuis et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found an induction of SA in resistant and susceptible genotypes, but found the resistant genotype HM078 contained constitutively higher levels of isoflavonoids. This was supported by our finding that \u003cem\u003eMtPAL\u003c/em\u003e and \u003cem\u003eMtIFR\u003c/em\u003e were upregulated at 72 hpi in the susceptible genotype, however, the resistant genotype had higher constitutive expression. Overall, both SA and JA likely play crucial roles as signaling molecules and the host response to \u003cem\u003eA. medicaginicola\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes in QTL identified based on contrasting expression profiles\u003c/h2\u003e \u003cp\u003eQTL \u003cem\u003ernpm1\u003c/em\u003e and \u003cem\u003ernpm2\u003c/em\u003e were examined for their role in SBS disease resistance, focusing on gene expression patterns. DEGs were only detected at 72 hpi in the susceptible genotype. These QTL are known to be inherited recessively, which may support the inverse gene-for-gene model [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This paradigm is illustrated by the LOV1 gene in oat that confers sensitivity to the fungal toxin victorin, while also providing resistance to the crown rust fungus [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Genes expressed in the susceptible genotype and not expressed in the resistant genotype are of particular interest. In \u003cem\u003ernpm1\u003c/em\u003e, TIR-NBS-NLR genes MtrunA17_Chr4g0009001 and MtrunA17_Chr4g0009011 were found to be expressed only in the susceptible genotype, supporting this concept. In \u003cem\u003ernpm2\u003c/em\u003e, which does not contain NLRs, a gene orthologous to \u003cem\u003eAtPAM16\u003c/em\u003e showed no expression in the resistant genotype and high expression in the susceptible genotype. \u003cem\u003ePAM16\u003c/em\u003e is known to play a role in plant immunity, as shown in \u003cem\u003eA. thaliana\u003c/em\u003e mutants lacking this gene, which exhibit enhanced disease resistance [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Backcrossing studies with an \u003cem\u003eAtPam16\u003c/em\u003e knockout mutant (\u003cem\u003emuse5-1\u003c/em\u003e) indicated that the recessive inheritance of the resistant phenotype aligns with the recessive inheritance pattern observed for \u003cem\u003ernpm2\u003c/em\u003e. Overall, genes with differing expression patterns between the resistant and susceptible genotypes in these QTL regions are candidate genes for further study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRPP13\u003c/b\u003e \u003cb\u003e-like plant disease protein likely involved in incompatible host response\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA promising plant disease resistance gene, potentially involved in the incompatible host response was identified. This coiled-coil class NLR gene was uniquely upregulated in the resistant genotype. Notably, this disease resistant protein is similar to \u003cem\u003eRPP13\u003c/em\u003e-like protein 1, which confers broad spectrum resistance to biotrophic pathogens \u003cem\u003eMelampsora lini\u003c/em\u003e (flax rust), as well as \u003cem\u003eHyaloperonospora arabidopsidis\u003c/em\u003e and \u003cem\u003ePeronospora parasitica\u003c/em\u003e (downy mildew) in \u003cem\u003eA. thaliana\u003c/em\u003e [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Coiled-coil class NLRs have also been shown to play important roles in plant immunity to necrotrophic pathogens, for example, overexpression of \u003cem\u003eGbCNL130\u003c/em\u003e confers resistance to \u003cem\u003eVerticillium dahliae\u003c/em\u003e in cotton [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Pathogen ligand binding to these proteins results in downstream defense responses. For instance, the activation of HOPZ-ACTIVATED RESISTANCE 1 (ZAR1) causes a calcium ion influx, ROS production, and cell death conferring resistance to \u003cem\u003ePseudomonas syringae\u003c/em\u003e in \u003cem\u003eA. thaliana\u003c/em\u003e [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Future directions will include investigating the specific role of this coiled-coil class NLR.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eExamining host-pathogen interactions between \u003cem\u003eM. truncatula\u003c/em\u003e and the necrotrophic fungal pathogen \u003cem\u003eA. medicaginicola\u003c/em\u003e has the potential to illuminate molecular factors that could be used to enhance disease resistance to Ascochyta blights in legumes. We performed a transcriptome analysis for a resistant (HM078) and susceptible (A17) \u003cem\u003eM. truncatula\u003c/em\u003e genotype infected with \u003cem\u003eA. medicaginicola\u003c/em\u003e to evaluate the host response and identify candidate genes for disease resistance. We examined DEGs, functionally enriched pathways, hormone pathways, RLKs, NLRs, and QTL regions for SBS disease resistance. We identified a number of candidate genes for disease resistance with support from the literature. After functional validation of candidate genes, future studies will explore engineering SBS disease resistance in the economically important forage crop alfalfa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSBS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpring black stem\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSARDI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSouth Australian Research and Development Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQTL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative trait locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ehpi\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHours post inoculation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePathogenesis-related\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ernpm1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResistance to the necrotroph \u003cem\u003ePhoma medicaginis\u003c/em\u003e one\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ernpm2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResistance to the necrotroph \u003cem\u003ePhoma medicaginis\u003c/em\u003e two\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgrammed cell death\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePAMPs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePathogen associated molecular patterns\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePattern recognition receptors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRLK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor-like kinases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRLP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor-like proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePTI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePAMP-triggered immunity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNucleotide-binding site and leucine-rich repeat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eETI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffector-triggered immunity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eETS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeffector-triggered susceptibility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSalicylic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eJA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJasmonic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eABA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbscisic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eET\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDEGs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eqPCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative RT-PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDe-ionized\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePDA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePotato dextrose agar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Processes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular Component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKEGG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCPM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCounts per million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVLCFA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery long chain fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChalcone synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMFS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor facilitator superfamily\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIFR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIsoflavone reductase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePAL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenylalanine ammonia lyase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTIR-NBS-LRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eToll/Interleukin1 receptor-nucleotide binding site-leucine-rich repeat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePrx28\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeroxidase 28\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRNASET2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonuclease T2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eXET\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eXyloglucan/xyloglucosyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBGAL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eβ-galactosidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAAO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAscorbate oxidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePBP1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePINOID-BINDING PROTEIN 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCML\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalmodulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003e4CL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4-coumarate-CoA ligase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLOV1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLOCUS ORCHESTRATING VICTORIN EFFECTS1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePAM16\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePresequence translocase-associated motor 16\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eZAR1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHOPZ-ACTIVATED RESISTANCE 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequence data has been deposited in the NCBI database under BioProject PRJNA975868. SRA numbers SRR24775309, SRR24775310, SRR24775311, SRR24775312, SRR24775313, SRR24775314, SRR24775315, SRR24775316, SRR24775317, SRR24775318, SRR24775319, SRR24775320, SRR24775321, SRR24775322, SRR24775326, SRR24775327, SRR24775328, SRR24775329, SRR24775330, SRR24775331, SRR24775332, SRR24775333, SRR24775334, SRR24775338, SRR24775339, SRR24775341, SRR24775342, SRR24775343, SRR24775344, SRR24775345, SRR24775349, SRR24775350, SRR24793325, SRR24793326, SRR24793327, SRR24793328 contain the RNA-seq reads used throughout this study. The code run throughout this study (RNA-seq_associated_code.html) and the RNA-seq feature count data (feature_count_matrix.txt) is available on GitHub (https://github.com/shaun-curtin/RNA-seq-analysis-of-Spring-Black-Stem-Disease-SBS-). Germplasm of \u003cem\u003eM. truncatula\u0026nbsp;\u003c/em\u003eused in this study can be requested at (https://medicago.legumeinfo.org/tools/germplasm/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by USDA-ARS project 5062-21000-035-000D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.B. conducted the analysis and wrote the original manuscript with input from S.J.C. J.B. and S.J.C conceived the study, planned experiments, and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the U.S. Department of Agriculture, Agricultural Research Service. Mention of any trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U. S. Department of Agriculture. USDA is an equal opportunity provider and employer, and all agency services are available without discrimination. We would like to thank the Centre for Crop and Disease Management, Curtin University (Bentley WA, Australia) for providing \u003cem\u003eA. medicaginicola\u003c/em\u003e isolate OMT5. The authors acknowledge the Minnesota Supercomputing Institute at the University of Minnesota for providing resources that contributed to the research results reported within this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang H, Hwang SF, Chang KF, Gossen BD, Turnbull GD, Howard RJ. Assessing resistance to spring black stem and leaf spot of alfalfa caused by \u003cem\u003ePhoma\u003c/em\u003e spp. Can J Plant Sci. 2004;84:311\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eEllwood SR, Kamphuis LG, Oliver RP. Identification of Sources of Resistance to Phoma medicaginis Isolates in Medicago truncatula SARDI Core Collection Accessions, and Multigene Differentiation of Isolates. Phytopathology. 2006;96:1330\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eNaseri B, Marefat AR. Seasonal dynamics and prevalence of alfalfa fungal pathogens in Zanjan province, Iran. 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Cell. 2021;184:3528-3541.e12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"RNA-seq, transcriptome, Medicago truncatula, Ascochyta medicaginicola, necrotroph, host response","lastPublishedDoi":"10.21203/rs.3.rs-4426199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4426199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAscochyta blights cause yield losses in all major legume crops. Spring black stem (SBS) and leaf spot disease is a major foliar disease of \u003cem\u003eMedicago truncatula\u003c/em\u003e and \u003cem\u003eM. sativa\u003c/em\u003e (alfalfa) caused by the necrotrophic fungus \u003cem\u003eAscochyta medicaginicola\u003c/em\u003e. This present study sought to identify candidate genes for SBS disease resistance for future functional validation. We employed RNA-seq to profile the transcriptomes of a resistant (HM078) and susceptible (A17) genotype of \u003cem\u003eM. truncatula\u003c/em\u003e at 24, 48, and 72 hours post inoculation. Preliminary microscopic examination showed reduced pathogen growth on the resistant genotype. In total, 192 and 2,908 differentially expressed genes (DEGs) were observed in the resistant and susceptible genotype, respectively. Functional enrichment analysis revealed the susceptible genotype engaged in processes in the cell periphery and plasma membrane, as well as flavonoid biosynthesis whereas the resistant genotype utilized calcium ion binding, cell wall modifications, and external encapsulating structures. Candidate genes for disease resistance were selected based on criteria, among the top ten upregulated genes in the resistant genotype, upregulated over time in the resistant genotype, hormone pathway genes, plant disease resistance genes, receptor-like kinases, contrasting expression profiles in QTL for disease resistance, and upregulated genes in enriched pathways. Overall, 19 candidate genes for SBS disease resistance were identified with support from the literature. These genes will be sources for future targeted mutagenesis and candidate gene validation potentially helping to improve disease resistance to this devastating foliar pathogen.\u003c/p\u003e","manuscriptTitle":"Transcriptome analysis of resistant and susceptible M. truncatula genotypes in response to the necrotrophic fungus A. medicaginicola ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 09:52:39","doi":"10.21203/rs.3.rs-4426199/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-08T07:07:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-07T21:53:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1282285618759689069651934559284483332","date":"2024-06-27T14:17:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-07T18:57:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130106562398969764135672060688751695065","date":"2024-05-28T20:31:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-27T09:15:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-27T08:53:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-27T08:51:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T08:51:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-05-15T15:30:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3afea0dd-99aa-466b-ad32-73d86aa84150","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:06:19+00:00","versionOfRecord":{"articleIdentity":"rs-4426199","link":"https://doi.org/10.1186/s12870-024-05444-3","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-07-29 15:58:04","publishedOnDateReadable":"July 29th, 2024"},"versionCreatedAt":"2024-06-07 09:52:39","video":"","vorDoi":"10.1186/s12870-024-05444-3","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05444-3","workflowStages":[]},"version":"v1","identity":"rs-4426199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4426199","identity":"rs-4426199","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}