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. 2023 May 12;13(6):179. doi: 10.1007/s13205-023-03565-4

Transcriptomic analysis towards identification of defence-responsive genes and pathways upon application of Sargassum seaweed extract on tomato plants infected with Macrophomina phaseolina

Tejas C Bosmaia 1, Parinita Agarwal 1, Mohit Dangariya 1, Jackson Khedia 1, Doddabhimappa R Gangapur 1,2,✉, Pradeep K Agarwal 1,2,✉
PMCID: PMC10182239  PMID: 37193326

Abstract

The charcoal-rot caused by Macrophomina phaseolina is one of the major disease in many economically important crop plants including tomato. The molecular responses of the host plant against the M. phaseolina are poorly stated. In the present study, for the first time the molecular insight of tomato-Macrophomina interaction and Sargassum tenerrimum extract (SE) toward managing disease through RNA-seq approach is established. A total of 449 million high-quality reads (HQRs) were obtained and aligned to the tomato genome with an average mapping of 89.12%. The differentially expressed genes (DEGs) regulated across the different treatment pairs were identified. Several DEGs, such as receptor-like kinases (SlRLKs), transcription factors including SlWRKY70, SlGRAS4, SlERF4, SlERF25, pathogenesis related-1 (SlPR1), SlPR2, endochitinase and peroxidase were significantly up-regulated in SE + Macrophomina treated sample as compared to only Macrophomina treated sample. The crosstalk between salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) was a key factor to regulate resistance in tomato during SE + Macrophomina treatment. The KEGG pathway including plant hormone signal transduction, plant-pathogen interaction and mitogen-activated protein kinase (MAPK) signaling pathway were significantly enriched. The RNA-seq data were validated through qPCR using 12 disease-responsive genes and correlated significantly with R2 = 0.73. The present study suggests that SE act as an elicitor molecule and activate the defence-related pathways similar to PAMP-triggered immunity in tomato. The jasmonic acid (JA) mediated signaling pathway was identified as a key factor to induce resistance in tomato against Macrophomina infection. The present study depicts the beneficial effects of SE by regulating molecular mechanism towards defence responses in tomato against Macrophomina infection. The application of SE brings out new prospects to induce disease tolerance in the agricultural crops.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03565-4.

Keywords: Transcriptome, Biotic stress, Macrophomina, Defence response, Sargassum extract, Seaweeds

Introduction

Biotic and abiotic stresses are the major challenge for sustainable crop production. The plant diseases and pests reduce the global food production by 20–40% (FAO 2017). To prevent the plant diseases and achieve the food quantum, the usages of harmful chemicals and pesticides have increased from 0.25 to 0.31 kg ha−1 cropland from 2002 to 2017 (FAO 2019). The reliance on excessive usage of pesticides and chemicals is harmful for the ecosystem and humans (Jjagwe et al. 2020). Plants follow various defence mechanisms by activating its innate immunity system through the systemic acquired resistance (SAR, Durrant and Dong 2004). Plants are adapted for various abiotic and biotic stress conditions by manoeuvring cellular, biochemical and molecular mechanisms. Marine algae provide a valuable and inexpensive source of numerous elicitors inducing SAR. The seaweed extract possess various compounds, which act as bio-elicitors in the plants for the defence against stress conditions. Different polysaccharides present in seaweed extracts have enormous potential for the plant defence (Stadnik and Freitas 2014; Shukla et al. 2019). The effect of several seaweed extracts have been studied on agricultural crops like wheat (Paulert et al. 2010), tomato (Khedia et al. 2020; Sbaihat et al. 2015), capsicum (Ali et al. 2021), cucumber, common bean and grapevine (Jaulneau et al. 2011) etc., that induced disease tolerance against several fungal diseases (Agarwal et al. 2021). Differential regulation of various defence-related genes such as antioxidative, antimicrobial (β-1,3-glucanase and polyphenol oxidase (PPO)), pathogenesis related (PRs), salicylic acid (SA)-related (phenylalanine ammonia-lyase (PAL)) and jasmonic acid (JA)-related synthesis is reported with the application of seaweed extracts from several brown, red and green algae (Agarwal et al. 2016, 2021). Brown seaweed, Sargassum grows abundantly in large geographical range. Sargassum tenerrimum contains phytohormones including IAA, zeatin, GA9 and active compounds like sulphated polysaccharide, terpenoids, sargachromenol, pheophytine, flavonoids and sterols (Prasad et al. 2010; Yende et al. 2014). Despite potential of SE as bio-stimulant very few studies have been reported in context of disease tolerance in plants. The Sargassum wightii extract induced defence-related enzymes in cotton plant against Xanthomonas campestris (Raghavendra et al. 2007). Similarly, S. polycystum extract contributed in regulation of defence enzymes, hormones and phytoalexin scopoletin synthesis during powdery mildew, late blight, and gray mold diseases in tomato (Khompatara et al. 2019). S. fusiforme extract showed reduced Phytophthera and Botrytis cinerea infection on tomatoes (Sbaihat et al. 2015). In our previous report, it was observed that the S. tenerrimum extract (SE) along with Macrophomina confer higher accumulation of SA and lower amount of abscisic acid (ABA) as compared to Macrophomina treatment. The accumulation of reactive oxygen species (ROS) was found less and other defence-related enzymes were high during SE treatment (Khedia et al. 2020). Kappaphycus alvarezii sap application demonstrated the elevations of the plant phytohormones (ABA, indole 3-acetic acid, SA and Zeatin) and also up-regulation of defence-responsive pathogenesis related genes (PR) in tomato during M. phaseolina treatment (Agarwal et al. 2016).

Tomato (Solanum lycopersicum L.; 2n = 24) is one of the most popular and widely grown vegetables in the world. It belongs to the family Solanaceae and is native of the Peru Ecuador region (Rick 1969). Tomato is the most important vegetable after potato in the world. It is consumed as a fresh vegetable, salad, ketchup, pickles etc. Tomato is considerably rich with phenolic compounds (phenolic acids and flavonoids), carotenoids (lycopene, α, and β carotene), vitamins (ascorbic acid and vitamin A) and glycoalkaloids (tomatine) (Chaudhary et al. 2018). The tomato crop faces major yield loss due to early blight of tomato (~ 45%; Saha and Das 2013), late blight of tomato (~ 30%; Tripathi et al. 2017). Tomato crops also suffer from charcoal rot caused by Macrophomina phaseolina, a soil-borne fungus, which infects the root and lower stem of tomato, resulting in yellowing of foliage, eventually wilting and collapse of the entire plant (Hyder et al. 2018). M. phaseolina produces sclerotia, which serve as primary inoculum and enable it to survive under adverse environmental conditions. Microsclerotia germinate and the hyphae invade the plant through root epidermal cells or wounds. During the initial stages of pathogenesis, the infection is restricted to intercellular spaces of the cortex of the primary roots (Mayek-Pérez et al. 2002). The heavily infected plants die due to the production of phaseolinone, a phytotoxic metabolite of M. phaseolina (Bhattacharya et al. 1994). The mycelium and microsclerotia of the infected plant material provide a means of propagation of the pathogen in the soil. Control of the disease is a challenge, due to the absence of genetic resistance and effective chemical control, alternative or complementary measures are needed such as the use of biological control in an integrated approach. The transcriptomic profiling is an important tool giving rise differential expression of core genes responsible for different stress conditions. The comparative transcriptome analysis of tomato resistant and susceptible varieties against leaf mold disease caused by Cladosporium fulvum fungus identified the cf-16 mediated unique resistance to disease (Zhang et al. 2020). In another transcriptome study, the tomato plant was infected by Fusarium proliferatum, wherein, 184 candidate effectors genes were identified (Gao et al. 2017). Although there are many studies on tomato plant-pathogen interaction using transcriptome, very limited studies are reported in context with disease mitigation via application of bio-control. Coppola et al. (2019) studied transcriptome and metabolome reprogramming in tomato plants by Trichoderma harzianum strain T22 primes and enhances defence responses against aphids. Moreover, the detailed transcriptome analysis of molecular mechanism for disease tolerance using seaweed extract is yet to be studied. In the present study the detailed transcriptome profiling was aimed to analyze the molecular mechanism of disease tolerance by SE against Macrophomina in the tomato.

Materials and methods

Plant growth and treatment

Tomato seeds of variety Junagadh Tomato-3 (JT-3, susceptible to disease caused by M. phaseolina) were obtained from the Junagadh Agricultural University, Junagadh, Gujarat, India and germinated in plastic pots containing autoclaved garden soil and grown under growth chamber conditions (photoperiod 16 h light/8 h dark at a temperature of 25 °C). One-month-old seedlings were transplanted in earthen pots containing red soil, black soil and farmyard manure (2:1:1) and grown in greenhouse. The detailed procedure of preparation of SE was followed as reported in Khedia et al. (2020). For preparation of SE, 5 kg S. tenerrimum collected from the coast of Okha, Gujarat, India followed by shed dry at ambient temperature. The dried material was soaked in 50 L of tap water for 12 h under ambient temperature. Approximately 40 L of clear cold water extract (CWE) was collected using centrifuge or Notch filtration unit. The remaining residue were subjected to hot water extract (HWE) at 120 °C for 120 min in 45 L water. The HWE was obtained using centrifugation at 60–80 °C for 10 min. The residue remaining after HWE were subjected to 45 L 1% w/w of potassium carbonate at 120 °C for 180 min. The aqueous alkali extract (AAE) was collected using centrifugation in hot condition (60–80 °C for 10 min). The extract from HWE and AAE procedure were subjected to heat treatment and reduced to volume to 45 L. A total of 85 L extract was obtained by mixing of CWE, HWE, and AAE. The application of SE was carried out by spraying of 10% SE solution at vegetative stage (54 days after germination, DAG). For Macrophomina treatment, fungal microsclerotia were collected from fungal mat grown on potato dextrose agar for 5 days. The spore suspension with microsclerotia count 300 ± 20 mL−1 was used to inoculate the 54 DAG plants. For Macrophomina and SE combined treatment, and fungal treatment along with foliar application of 10% SE was given to 54 DAG plants. Water was sprayed to another set of plant and used as control. The leaf samples were harvested after 7 days of treatments. The harvested samples were stored in five volumes of RNA later solution (Sigma, USA). The leaf samples submerged in RNA later solution were incubated at 4 °C overnight for allowing solution to thoroughly penetrate the tissue then transferred to − 80 °C for long term storage and further use. A total of two and three independent biological replicates stored in same condition were used for RNA-seq and qPCR analysis, respectively.

RNA isolation, cDNA library preparation, and RNA sequencing

Total RNA from leaves frozen in RNA later solution was extracted using TRIzol like reagent (Pyvovarenko and Lopato 2011). The integrity, quantity and quality of RNAs of each sample were checked by Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA), NanoDrop (Thermo Fisher Scientific Inc.) and 2% agarose gel, respectively. RNA samples with RIN value > 7.0, 260/280 ratio of 2.0–2.1 and 260/230 ratio of 2.0–2.3 were used for the library preparation. Next-generation sequencing library preparations were constructed according to the manufacturer’s protocol (NEBNext® UltraTM RNA library prep kit for illumina®). Four samples with two biological replications were prepared and sequencing of each sample was carried out with the Illumina Hiseq-2500 at Nucleome Informatics Private Limited, Hyderabad, India.

Pre-processing of sequencing data, read alignment, read count

The pre-processing of raw reads was done using Perl script of PRINSEQ-lite v0.20.4 tool by removal of reads with adapter sequences, low-quality bases, short sequences (< 50bases) and reads containing N > 10%. The quality of sequencing data was assessed using FastQC v0.11.9 (www.bioinformatics. babraham.ac.uk/projects/fastqc). The high-quality reads (HQRs) were aligned to the assembly of Tomato genome (SL 3.0) using quasi-alignment mode of Salmon version 0.13.1 (Patro et al. 2017) with 100 bootstraps, GC-bias and transcript length correction to ensure an accurate count of the transcript.

Differential gene expression analysis

The gene expression level was estimated by counting the reads that map to exons, transcripts or genes. The transcript abundance for each sample were summarized using Salmon (Patro et al. 2017) and R package Tximport v3.2.3. The normalized counts from each sample replicates were used to carry out PCA analysis and results are showing correlation between samples replicates (Supplementary Fig. S1). The DEseq2 v3.4 (Anders and Huber 2010) software was used to generate normalized read counts and to detect differentially expressed genes (DEGs). The method is based on testing of DEGs by use of negative binomial generalized linear model and depended on p value estimation and FDR value estimation. The DEGs for each of the sample comparisons were identified based on a p value (< 0.05) and log2 fold change (log2FC), i.e. ≥ 1.5 for up-regulated genes and ≤ – 1.5 for down-regulated genes. Similarly, Padmanabhan et al. (2019) set cut-off of DEGs to 1.5-fold in the study of tomato–tomato spotted wilt tospovirus interaction. The DEGs were classified based on their functional relevance to defence response. The DEGs were grouped into different classes like kinases, pathogenesis-related proteins, growth-related hormones genes, stress-related hormones genes, TFs and anti-oxidative enzymes genes. The different classes of DEGs were presented in a heat map using Multi Experiment Viewer (MeV) v4.9.0, and the number of DEGs among and between the conditions was plotted as a Venn diagram using Vennplex v1.0.0.2 (Cai et al. 2013).

Gene ontology (GO) enrichment and pathway analysis

The identified DEGs were subjected to GO term assessment against gene ontology (GO) database (http://geneontology.org) and GO term were assigned using GOsim tool of bioconductor package (Fröhlich et al. 2007) with default parameters. The assigned GO terms of significant DEGs of all comparisons were visualized using the WEGO v2.0 tool (Ye et al. 2006). The GO enrichment analysis was performed to identify overrepresented biological processes among the DEGs of all comparisons using the Elim–Kolmogorov–Smirnov method with a significance threshold of 0.01 as implemented in R-Bioconductor package TopGO v2.46. DEGs were searched for probable transcription factor against plant transcription factor database v5.0 using transcription factor prediction and enrichment tool (http://plantregmap.gao-lab.org/index.php). The identified DEGs from each sample comparisons were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to map DEGs in different metabolic pathways. The clusteProfiler v1.4.0 (Yu et al. 2012), an R-Bioconductor package was used for the pathway enrichment. Pathways with p value ≤ of 0.01 were only considered as significantly enriched pathway.

Expression analysis of differentially expressed genes using qPCR

qPCR was carried out to analyse the expression of functionally relevant differentially expressed genes. A total of 15 defence-responsive DEGs were selected based on their function to validate the expression pattern revealed by RNA-seq analysis. The primers were designed using Primer blast software. The total RNA was isolated using Trizol-like reagent (Pyvovarenko and Lopato 2011) from the plant samples and treated with DNaseI (MBI Fermentas). Five micrograms of the total RNA was used for first-strand cDNA synthesis according to manufacturer’s protocol using RevertAid cDNA synthesis kit (Thermo Scientific). The qPCR was performed in a 20-µL reaction mixture containing 10 µL of 2X SYBR Green JumpStart™ Taq ReadyMix™ (Sigma), 1 µL of cDNA (1:10 diluted), 1 µL (9.3 ng/ µL) of each primer. The qPCR was performed in 96-well blocks CFX96 Real-Time PCR machine (BioRad, United States) using following cycle: 10 min at 95 °C for 1 cycle, 95 °C for 15 s, 60 °C for 45 s for 40 cycles followed by melt curve analysis. The tomato actin gene (SlACT) was used as an endogenous control to normalize the expression of selected genes. Livak’s method (2– ΔΔCT) was used for data analysis (Livak and Schmittgen 2001). The qPCR data wre analysed by one-way ANOVA with replicates (n = 3). C.D. was calculated to determine the significance of difference across all treatments by Infostat v2020 software at p ≤ 0.05. Mean values significantly different from each other were labelled by different alphabet.

Results

Transcriptome sequencing, mapping and statistical analysis

The RNA-Seq generated a total of 450 million reads from all four samples (with two biological replicates of each) with an average GC content of 44%. The pre-processing of RNA-seq data including removal of low complexity reads, adaptor/primer sequences, rRNA sequences, have yielded 449 million HQRs with a phred value of ≥ 30. Raw data sequencing reads were deposited in the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/sra) under the Bioproject number PRJNA923467. The HQRs were mapped to the tomato genome (SL 3.0) ranging from 83.64 to 93.12% with an average of 89.12% per library (Supplementary Table S1).

Identification of differentially expressed genes regulated during tomato: Macrophomina: SE interaction

The mapping to the SL 3.0 tomato genome revealed a total of 638, 701, 138, 229 and 1222 up-regulated genes, while 1382, 601, 176, 278 and 593 down-regulated genes in Macrophomina (M) vs Control (C); SE vs C; SE + M vs C; SE + M vs SE; and SE + M vs M combinations, respectively (Fig. 1). The DEGs analysis revealed the more number of down-regulated genes in M vs C treatment comparison, while in SE + M vs C more number of up-regulated genes were observed. For better understanding, three-way comparisons of up-regulated, down-regulated and contra-regulated (regulation in treatment pair comparisons is contradict, i.e. in one treatment DEGs are up-regulated and another down-regulated or vice versa). DEGs of different treatment pairs were represented by Venn diagrams (Fig. 1). A total of 455, 504, and 62 DEGs were specifically up-regulated and 960, 216, and 18 DEGs were specifically down-regulated in M, SE, and SE + M treatments, respectively as compared to control. When two treatment pairs were compared, a total of 156 up-regulated, 242 down-regulated, and 8 contra-regulated DEGs were found common in both M vs C and SE vs C treatment comparisons. Similarly, a total of 10 up-regulated, 21 down-regulated and 27 contra-regulated DEGs were found in the M and SE + M treatments as compared to control. When M and SE + M treatments were compared with control, it shared 10 up-regulated, 21 down-regulated and 27 contra-regulated DEGs (Fig. 1a). The second Venn diagram compared M, C and SE treatments with SE + M. A total of 962, 16, and 98 specifically up-regulated and 512, 90, and 203 specifically down-regulated DEGs were found in SE + M vs M, SE + M vs C, and SE + M vs SE treatment pairs, respectively. A total of 80 up-regulated, 57 down-regulated and 11 contra-regulated DEGs were found in the two treatment pairs comparison SE + M vs M and SE + M vs SE. Similarly, 80 up-regulated, 13 down-regulated and 57 contra-regulated DEGs were found in the two treatment pairs comparison SE + M vs M and SE + M vs C (Fig. 1b). However, when we compared SE + M vs C and SE + M vs SE treatment pairs, 8 up-regulated, 7 down-regulated DEGs were found.

Fig. 1.

Fig. 1

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Venn diagram representing the number of DEGs across different treatment pairs A M, SE, SE + M vs C and B SE + M vs C, M, SE as obtained by mapping with tomato genome. M Macrophomina, SE Sargassum extract, C control. The number in bold, red color and underlined indicated the up-regulated, contra-regulated and down-regulated DEGs

Gene ontology, pathway and transcription factor enrichment analysis

Total number of DEGs, number of DEGs assigned to GO term, number of GO terms assigned and enriched are presented in supplementary table S2. The GO terms is categorized into three main categories including biological process (2742), cellular component (2120), and molecular function (3327), respectively (Fig. 2). The majority of GO terms are found to be involved in various metabolic processes, including regulation of metabolic process (GO:0019222), cellular component of organization (GO:0071840), signaling, response to stimulus (GO:0050896), cellular response to stimulus (GO:0051716), and response to biotic stimulus (GO:0009607). The biotic stress-related GO terms including response to chitin (GO:0010200), defence response to other organism (GO:0098542), defence response (GO:0006952), response to fungus (GO:0009620), response to endogenous stimulus (GO:0009719) were highly enriched in the treatment pairs SE + M vs C, SE + M vs M and SE + M vs SE compared to the M vs C and SE vs C. A total of 22 DEGs were assigned to GO term defence response (GO:0006952) in SE + M vs M treatment comparison and majority of DEGs (14) were up-regulated. Similarly, 15 DEGs were assigned to term response to chitin (GO:0010200) and all were up-regulated in SE + M vs M treatment comparison. The GO term including water homeostasis (GO:0030104), organic hydroxy compound transport (GO:0015850), regulation of cell size (GO:0008361), secondary metabolite biosynthetic process (GO:0044550), and cell wall biogenesis (GO:0042546) were significantly higher enriched in M vs C comparison. However, in the case of SE vs C treatment pair, plant hormones and growth-related GO terms including regulation of organ growth (GO:0046620), response to auxin (GO:0009733), organ growth (GO:0035265), auxin transport (GO:0060918), and hormone transport (GO:0009914) were highly enriched. The top five enriched terms in biological process obtained from DEGs of different treatment comparison are represented using the bubble plot (Fig. 3). In the comparison of SE vs C, the top five GOs enriched of DEG revealed the genes of hormones and growth promoting parameters. The comparison of GOs enriched of DEGs of SE + M vs C, SE + M vs M and SE + M vs SE revealed the majority of the GO terms of stress-responsive pathways (Fig. 3). The results indicate that application of SE on control plants enhanced the growth while in the presence of fungal pathogen (M), induced strong defence response. The DEGs encoding transcription factors were identified in different treatment pairs. The treatment pairs M vs C, SE vs C and SE + M vs M showed maximum TF DEGs (306), followed by SE + M vs SE (305) and SE + M vs C (294). The treatment pair SE + M vs C comprised of the highest number of enriched TFs 55 followed by 41 in M vs C; SE + M vs M and 31 in SE vs C; SE + M vs SE (Supplementary Table S2). The KEGG pathway enrichment analysis of up-regulated genes underlying pathway revealed enrichment of plant hormone signal transduction (sly04075), plant–pathogen interaction (sly04626), mitogen activated protein kinase (MAPK) signaling pathway–plant (sly04016) in SE + M vs M treatment pair. Down-regulated genes underlying in the pathways including porphyrin and chlorophyll metabolism (sly00860), photosynthesis-antenna proteins (sly00196) were significantly enriched (Table 1). However, in the treatment pair M vs C, genes underlying the pathways including plant hormone signal transduction (sly04075) and MAPK signaling pathway–plant (sly04016) were significantly enriched (Table 1).

Fig. 2.

Fig. 2

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Identified gene ontology terms of DEGs of treatment pairs pairs M vs C; SE + M vs C; SE + M vs M; SE + M vs SE and SE vs C. These GO terms are classified into three categories (cellular component, molecular function, and biological processes). C control, M Macrophomina, SE Sargassum extract

Fig. 3.

Fig. 3

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Top five Gene ontology term enrichment analysis of DEGs involved in different treatments comparison

Table 1.

Pathway enrichment analysis of DEGs present in different treatments

ID Description BgRatio p value P.adjust Gene count Up/down regulation of pathway
M vs C
 sly04075 Plant hormone signal transduction 209/5381 9.80E−10 8.43E−08 15 Up
 sly04016 MAPK signaling pathway—plant 130/5381 2.72E−05 0.00117 8 Up
SE + M vs C
 sly04141 Protein processing in endoplasmic reticulum 144/5381 0.003722 0.078668 3 Up
SE vs C
 sly00100 Steroid biosynthesis 38/5381 0.000128 0.012392 8 Up
 sly00970 Aminoacyl-tRNA biosynthesis 87/5381 0.00084 0.040738 11 Up
 sly03010 Ribosome 365/5381 6.19E−18 6.12E−16 72 Down
 sly00520 Amino sugar and nucleotide sugar metabolism 136/5381 3.13E−05 0.00155 23 Down
 sly04145 Phagosome 90/5381 8.36E−05 0.002757 17 Down
 sly04075 Plant hormone signal transduction 299/5381 0.000806 0.019959 35 Down
 sly00190 Oxidative phosphorylation 155/5381 0.001537 0.030436 21 Down
SE + M vs SE
 sly03010 Ribosome 365/5381 6.54E−06 0.000549 30 Up
 sly00520 Amino sugar and nucleotide sugar metabolism 136/5381 0.000808 0.033952 13 Up
SE + M vs M
 sly00520 Amino sugar and nucleotide sugar metabolism 136/5381 4.65E−07 4.65E−05 27 Up
 sly04075 Plant hormone signal transduction 299/5381 0.000362 0.012483 37 Up
 sly04626 Plant-pathogen interaction 225/5381 0.000374 0.012483 30 Up
 sly00270 Cysteine and methionine metabolism 118/5381 0.001213 0.025743 18 Up
 sly04016 MAPK signaling pathway—plant 189/5381 0.001287 0.025743 25 Up
 sly04141 Protein processing in endoplasmic reticulum 229/5381 0.002262 0.033772 28 Up
 sly00010 Glycolysis/gluconeogenesis 135/5381 0.002364 0.033772 19 Up
 sly00860 Porphyrin and chlorophyll metabolism 49/5381 0.000725 0.046944 7 Down
 sly00196 Photosynthesis—antenna proteins 26/5381 0.001079 0.046944 5 Down
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Pathways with p value ≤ 0.01

Differential expression of candidate genes across different comparisons

The expression patterns of DEGs were analysed and several disease-responsive genes were identified based on their GO allotment in SE + M vs M treatment pairs. This treatment pair is considered to be an important, which indicated the expression of disease-responsive genes in combination of SE + M vs M. The heatmaps of stress-responsive DEGs including kinases, pathogenesis-related proteins, growth-related hormones genes, stress-related hormones genes, TFs and anti-oxidative enzymes genes were generated (Fig. 4, Supplementary Table S3). The necrotrophic fungal pathogen was recognized by the different receptor proteins involved in MAPK signaling cascades. The pathway analysis shows the enrichment of MAPK signaling pathway in both SE + M vs C and M vs C treatment comparison. However, the gene sets under MAPK signaling pathway are different in both comparison, i.e. Solyc01g100640.3, Solyc11g006115.1, Solyc04g014650.3, Solyc02g070910.2, Solyc03g007050.3 are up-regulated 3.8, 3.7, 1.99, 1.84 and 1.80 fold, respectively, in M vs C while down-regulate in SE + M vs M. Although gene encode for clade VI lectin receptor kinase, serine/threonine-protein kinase, non-specific serine/threonine protein kinase, and mitogen-activated protein kinase were significantly up-regulated in SE + M vs M, as compared to M vs C.

Fig. 4.

Fig. 4

Fig. 4

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Heat map generated using treatment responsive DEGs divided into different A kinases, B transcription factors, C growth related hormones genes, D stress related hormones genes, E pathogenesis related proteins, F anti-oxidative enzymes genes. M Macrophomina, SE Sargassum extract, C control

The differentially expressed TFs including WRKY family proteins, GRAS4 (derived from Gibberellin Insensitive (GAI), Gal Repressor (RGA) and Scarecrow (SCR)) TF, ethylene response factor 5 (SlERF5) and SlERF25 were significantly up-regulated in SE + M vs M and SE + M vs C treatment pairs, while down-regulated in M vs C treatment pair. In the case of pathogenesis-related (PR) proteins, Acidic 26 kDa endochitinase (SlCHN), Pathogenesis-related genes transcriptional activator (SlPTI6, SlPTI5), SlPR2, SlPR1, pectinesterase inhibitor protein were significantly up-regulated in SE + M vs M and SE + M vs C treatment pairs while down-regulated in M vs C treatment pair. Interestingly the expression of linoleate 9S-lipoxygenaseA (SlLOX) was highly up-regulated in SE + M vs M and M vs C treatment pairs. The application of SE induced the expression of genes related to endogenous growth hormones as the extract contained various growth hormones, bioactive molecules and elicitors. In the present study, several growth hormone-responsive genes including auxin- responsive proteins, auxin efflux component proteins, abscisic acid and environmental stress-inducible protein (SlTAS14) and abscisic acid 8'-hydroxylase (SlCYP707A2) were significantly up-regulated in SE + M vs M while down-regulated in M vs C treatment pairs (Fig. 4, Supplementary Table S3). The defence-related hormone-responsive genes (SA; JA; Ethylene, ET and Brassinosteroids, Br) were also differentially expressed in the present study. JA is a key hormone being regulated in the necrotrophic fungal interaction. All genes responsible for the biosynthesis of JA from α-Linolenic acid including phospholipase, lipoxigenase, allen oxide synthase/cyclase and 4-Coumarate-CoA ligases were significantly regulated across different treatments. The application of SE induced up-regulation of these key genes, moreover higher expression of JA induced downstream genes were also recorded in SE + M treated samples compared to M and C samples. The regulation of JA biosynthesis pathways and its responsive downstream genes were presented in the Fig. 5. In addition, two SA-responsive genes were also significantly up-regulated in SE + M vs M treatment pair as compared to other treatment pairs. The JA and ET both are synergistically regulated during necrotrophic infection. In the present study, ET biosynthesis and responsive genes including 1-aminocyclopropane-1-carboxylate oxidase 1, SlERF1, gene involve in ET-activated signaling pathway were up-regulated in the combination of SE + M treatment while down-regulated by M treatment only. Similarly, the genes responsible for Br biosynthesis and its downstream pathways were significantly up-regulated in SE + M vs M treatment pair while down-regulated in M vs C treatment pair. The mechanistic overview of role of SE in macrophomina–tomato interaction is represented in the Fig. 6.

Fig. 5.

Fig. 5

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Heat map representation of Jasmonic acid biosynthesis pathway and regulation of its downstream genes in response to SE application against Macrophomina infection in tomato

Fig. 6.

Fig. 6

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A schematic representation depicting the stress tolerance mechanism of SE towards induction of SAR in tomato against Macrophomina infection

Gene expression analysis by quantitative qPCR

A total of 15 genes were selected for the validation purpose based on functional annotation of defence-related DEGs. SlACT gene was used as an endogenous control for normalization of the relative expression of other target genes. Of these 15 primers, 12 amplified a single amplicon and three primers could not amplify any product (Supplementary Table S4). The expression analysis of 12 different defence-related DEGs was observed in different treatment pairs (Fig. 7). The expression of SlXET, SlERF5, SlPR3 genes was found more than 16, 11, eight fold, respectively in SE + M treatment compared to M, while SlERF25, SlWRKY70, SlJAZ2, lectin-domain containing receptor kinase (SlLRK) genes showed the expression of around 2.81–5.24-fold. Interestingly, pathogenesis-related genes transcriptional activator SlPTI5 expressed 15.02-fold in SE + M treatment compared to M, while lower expression (1.39 fold-change) of SlPTI4 was reported in SE + M treatment compared to M. A comparison of gene expression level between RNA-seq data and qPCR was also carried out and a positive correlation (R2 = 0.73) were recorded between both datasets (Fig. 8). These genes are indeed involved in the resistance to pathogenesis in tomato plants against Macrophomina.

Fig. 7.

Fig. 7

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Gene expression analysis through qPCR of different treatment pairs. SlERF25 ethylene-responsive factor25, SlWRKY70 WRKY transcription factor70, SlJAZ2 jasmonate ZIM-domain protein 2, Sl4CL 4-coumarate–CoA ligase-like 9, SlXET xyloglucan endo-transglycosylase, SlPTI5 pathogenesis related genes transcriptional activator PTI5, SlLRK lectin-domain containing receptor kinase VI.3-like, SlFRK fructokinase, SlMPK4 mitogen-activated protein kinase 4, SlPTI4 pathogenesis related genes transcriptional activator PTI4, SlERF5 ethylene-responsive transcription factor 5, SlPR3 pathogenesis related protein-3, M Macrophomina, SE Sargassum extract, C control. Values represent mean relative expression (n = 3) ± SE at p = 0.05. Means followed by the same lower case letters within a column are not significantly different

Fig. 8.

Fig. 8

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Correlation between differentially expressed genes (DEGs) from RNA-seq and qPCR data of SE + M vs M treatment pair. M Macrophomina, SE Sargassum extract

Discussion

The charcoal rot caused by the M. phaseolina results in economic yield loss to tomato. Our previous study revealed that the foliar application of SE serve as bio-stimulant and also provides resistance to tomato plants against Macrophomina via regulating phytohormones and ROS homeostasis (Khedia et al. 2020). The present study was undertaken to decipher the involvement and regulation of key defence pathways and genes towards resistance mechanism induced by SE in tomato against Macrophomina. The RNA-seq approach was employed to analyze the molecular response of tomato during SE, M, and SE + M treatments. Analysis of the DEGs for all five treatment pairs, revealed higher number of up-regulated genes with SE vs C and SE + M vs M treatment pairs; Similarly, 635 and 456 genes were up-regulated and down-regulated, respectively, in the RNA-seq data of tomato on application of Ascophyllum nodosum extract (Ali et al. 2022). The number of DEGs largely depend on the cutoff level used for selection of DEGs, i.e. p value, FDR and fold-change expression. The higher number of up-regulated genes indicate the elicitor activity and growth-promoting activity of SE on tomato. The GO term analysis classified DEGs to metabolic process, component of organization, signaling, response to chitin and response to biotic stimulus. The GO enrichment analysis indicating response to chitin (GO:0010200), defence response (GO:0006952), and response to fungus (GO:0009620) were significantly enriched in SE + M treated samples. The application of SE on control plant displayed growth related GO terms enrichment. The hormones present in the SE might play role in growth related GO enrichment along with SE induced endogenous phytohormones in tomato plant (Khedia et al. 2020). Chitin is a polysaccharide present in the cell wall of fungi and acts as a microbial-associated molecular pattern (MAMP) perceived by the plant to induce basal defence (Pusztahelyi 2018). The genes involved in chitin metabolic process and the receptor like kinases (RLKs) were enriched as a core defence response in tomato fruit against different fungal pathogens (Silva et al. 2020). The GO term for cell wall biosynthesis and secondary metabolites was enriched in M treated plants. The cell wall thickening and lignification gets induced after recognition of pathogen-associated molecular pattern (PAMP) in plants as part of the defence response (Jones and Dangl 2006). These GO terms were also enriched in the transcriptome study of Macrophomina–sesame interaction (Yan et al. 2021). The GO term auxin transport, hormones transport, and response to auxin were enriched in SE treated sample. This is in support of our previous observation, where the application of SE enhanced the biosynthesis and metabolism of the phytohormones in tomato plants (Khedia et al. 2020). The TF prediction analysis of DEGs revealed the enrichment of WRKY, bLHL, MYB TF family proteins in M vs C and SE + M vs C treatment comparisons, while SE vs C and SE + M vs SE treatment comparisons showed enriched TF including MYB, C2-C2 dof and trihelix TF family proteins. The WRKY TF family are responsive to pathogen, elicitor and stress-related hormones like SA, JA/ET in plant (Chen et al. 2019). Similarly, MYB, NAC and WRKY domain TFs were found up-regulated in maize on application of Kappaphycus alvarezii seaweed extract (Trivedi et al. 2021). The KEGG pathways including plant–pathogen interaction, MAPK signaling, and plant hormone signal transduction were significantly enriched. A total of 30, 25 and 37 DEGs were up-regulated underlying the plant–pathogen interaction, MAPK signaling and plant hormone signal transduction pathways, respectively. Plant pathogen interaction pathway mainly involved the genes encoded for Receptor-like protein SlEIX1 (EIX receptor 1), calcium-dependent protein kinases, SlMAPK, SlPTI5, SlPTI6. Similarly, the transcriptome study on tomato ripe fruit revealed the enrichment of plant–pathogen interaction (sly04626) and MAPK signaling (sly4016), secondary metabolite biosynthesis pathways (sly00900, sly00941, sly00945) against three fungal pathogens (Silva et al. 2020). Defence responses have been categorized into PAMP triggered immunity (PTI) or Effector triggered immunity (ETI). The PTI is the first line of defence during the necrotrophic fungal infection. The various PAMPs are recognized by different kinases known as pattern recognition receptors (PRRs). In the present study, different types of calcium-dependent protein kinases, Mitogen-activated protein kinase, serine/threonine-protein kinase, thymidine kinase, clade XVI lectin receptor, leucine-rich repeat receptor-like kinase, and auxin-regulated dual-specificity cytosolic kinase demonstrated altered expression in different treatment pairs as mentioned in the heatmaps. The magnitude of expression of these genes was higher in SE + M treatment compared to M suggesting the role of SE in the enhancement of the defence response in tomato against Macrophomina. The virus-induced gene silencing (VIGS)-based knockdown of tomato MAPKs demonstrated its role in positive regulation of defence response against necrotrophic fungi B. cinerea (Li et al. 2014). The PTI induced by PAMP is relatively weak compared to ETI but it has broad-spectrum immune response against the necrotrophic fungal pathogen (Wang et al. 2014). The PTI and/or ETI activate stress-related phytohormones during pathogenesis (Yuan et al. 2021). SA, JA, and ET are key regulators of biotic stresses and provide a vital role in defence response (Bigeard et al. 2015). SA-mediated defence response can be operated against biotrophic and hemi-biotrophic pathogens while JA and ET-mediated defence responses against insect herbivores and necrotrophic pathogens (Pieterse et al. 2012; Bari and Jones 2009). In the present study, JA biosynthesis and JA-responsive downstream pathways were enriched significantly. The different JA biosynthesis genes including phospholipase, lipoxygenase, allene oxide synthase/cyclase, 4-coumarate-CoA ligase, methyle transferase, and JA-responsive genes including ZIM domain protein, late embryogenesis abundant protein, SlERF25, defensin were significantly up-regulated in SM + M vs M treatment pairs. The 1-aminocyclopropane-1-carboxylate oxidase 1 gene encoding for the enzyme responsible for the biosynthesis of ethylene was up-regulated in SE + M vs M while down-regulated in M vs C treatment pairs. A similar trend was also observed in gene encoded for SlERF1 and genes involved in the ethylene-activated signaling pathway. The present result is in accordance with the finding elucidated earlier that the ethylene/jasmonic acid-signaling lack of function mutant showed enhanced susceptibility in Arabidopsis against Macrophomina (Schroeder et al. 2019). The ethylene/jasmonic acid-signaling pathways play a key role in the development of resistance against Macrophomina in tomato plants. The elicitor molecules present in SE were significantly enhancing the expression of these genes as compared to control and only M treated plants. Two genes encoding SA responsive proteins (proline rich protein (SlRSI1) and Calmodulin binding protein-like (SlCBP)) were also found to be up-regulated in SE + M vs M treatment pair while down-regulated or less expressed in other treatment pairs. A potential role of SA in host defence against Macrophomina was observed in the SA signaling deficit knockout mutant of Arabidopsis (Schroeder et al. 2019). The role of brassinosteroids (Br) hormone is less elucidated in plant-pathogen interaction. Application of low concentration of Br improved resistance to viral and fungal pathogen in cucumber, tomato, and tobacco plant (Wang et al. 2015; Bajguz and Hayat 2009). Similarly, in the present study, Br biosynthetic process and homeostasis responsive genes (Ent-kaurenoic acid oxidase 2 (SlKAO2), Cytochrome P450 734A1-like (SlCYPs)) were contra regulated in SE + M vs M and M vs C treatment pairs. Apart from the stress responsive hormones (SA, JA/ET), growth promoting hormone like auxin also play important role in regulating plant defence, either alone or together with the primary defence hormones (Kazan and Manners 2009). The presence of IAA in SE (Prasad et al. 2010), might be facilitating the up-regulation of different genes like auxin-responsive proteins, auxin-regulated protein, and auxin efflux carrier component in SE + M vs M treatment while down-regulated in M vs C treatment. The Arabidopsis knockout mutant of auxin signaling pathways exhibited the defects in ubiquitination pathway and showed increased susceptibility to the necrotrophic fungi Plectosphaerella cucumerina and B. cinerea (Llorente et al. 2008). The activation of PTI and hormones signal transduction pathways directly target and regulate the expression of several TFs. In the present study, SlWRKY3, SlWRKY70, SlWRKY33, SlGATA, SlGRAS4, SlERF4, SlERF25, and SlMYB TFs were differentially expressed across different treatment pairs. The WRKY TFs family induced and mediated by MAPK cascade has an important role in the activation of defence response (Gao et al. 2020). Out of 20 WRKY TFs differentially expressed in the transcriptome study of sesame-Macrophomina interaction, 6 WRKY TFs were induced in resistant genotype and positively regulate resistance response (Yan et al. 2021). Similarly, GRAS, MYB, and ERF TFs are also involved in plant resistance against the pathogen (Cui et al. 2018; Liu et al. 2017; Yan et al. 2021). PR proteins accumulate to protect damage from pathogen. The SE can able to regulate defence related hormones JA/ET and/ or SA and as cross talk between them lead to accumulation of PR proteins, as well as increase in defence enzyme activities. The PR proteins including SlLOX, SlPR1, SlPR2, acidic26 kDa endochitinase (SlPR3), and chitin-binding and catabolic process genes were significantly up-regulated in SE + M vs M treatment pair while down-regulated in M vs C. It has been reported that PR proteins are regulated during the induced systemic resistance (ISR) and systemic acquired resistance (SAR) which counter the fungal infection. Both SAR and ISR are important mechanisms for plant defense against pathogens. However, SAR is a response to an ongoing pathogen attack, while ISR is a preventive measure to protect the plant from future attacks. During a pathogenic condition, SAR is triggered by the recognition of the pathogen by the plant's immune system called PAMP triggered immunity, which activate SAR by generating long distance signaling molecules SA and JA (Dempsey and Klessig 2012). SAR leads to the broad spectrum, long lasting and systemic (un-inoculated portion of plant) resistance and making it more difficult for the pathogen to enter the plant cells (Dempsey and Klessig 2012). On the other hand, Seaweed extracts contain plant bioactive molecules that can induce resistance in plants. The seaweed based elicitors molecules act in defence response against pathogens via pathogen-associated molecular patterns (PAMPs) triggered immunity (Shukla et al. 2016; 2019). In the present study SE vs C treatment comparison showed the up-regulation of growth related genes and not much defence signaling. Furthermore, SE + M vs M and SE + M vs C showed the activation of strong defence response in term of MAPK signaling, plant pathogen interaction pathway and downstream regulation of ROS scavenger activity. The foliar application of SE to tomato is not associated with direct defense response. However, a strong defense response can get activated rapidly upon subsequent infection of pathogen compared to non-sprayed control plant. Such enhance defence response is referred as primed defence response (De Kesel et al. 2021). In the present study, expression of SlPR3, SlERF5 and SlERF25 increased significantly in SE + M treatment compared to M and SE. The ERF5 is involved in JA mediated defence regulation, the overexpression of ERF5 in Arabidopsis showed positive regulation of downstream genes related to JA mediated defence pathway (Moffat et al. 2012). The present finding is also in agreement with the studies by Ramkissoon et al. (2017) and Ali et al. (2016) about the elicitation of JA/ET defence response by brown seaweed extracts. Subramanian et al. (2011) observed that application of A. nodosum extract induced jasmonic acid dependent systemic resistance in Arabidopsis against Pseudomonas and Sclerotium. However, down-regulation of SA pathway marker gene (SlPR1a) in SE vs C and M vs C treatment comparisons might indicated no direct role of SE and M in SA mediated systemic resistance in tomato plants. The up-regulation of SlPR1 in SE + M vs C and SE + M vs M treatment comparisons might indicated plants indicate stronger SA mediated defence response upon SE treatment following infection of Macrophomina infection. The expression of chitinase increased in both resistant and susceptible genotypes of groundnut upon infection with necrotrophic fungi Sclerotia rolfsii. However, expression in the resistant genotype was much higher as compared to the susceptible genotype (Bosamia et al. 2020). In the present study, the expression of chitinase and gene involved in chitin-binding and catabolic process were significantly up-regulated in the SE treated plants indicating the role of SE mediated elevation of defence response. Plant produce several antioxidative enzymes to reduce the damage caused by ROS. The antioxidative enzymes genes such as L-ascorbate peroxidase 5, catalase, different iso-enzymes of peroxidase, ascorbate peroxidase-2 (SlAPX2) were significantly up-regulated in SE + M vs M treatment pair while down-regulated in M vs C. The peroxidase activity in sesame was higher in resistant genotype as compared to susceptible genotype and may contribute towards resistance against Macrophomina (Yan et al. 2021).

Conclusion

The present study provides insight into the molecular regulation of SE mediated induced systemic resistance against Macrophomina infection in tomato. The application of SE proved beneficial for the control of charcoal rot in tomato plants by modulating the functions of different key genes, regulating the signaling pathways involved in the disease tolerance. The SE showed the noteworthy effect on the growth and induced disease resistance. The SE vs C comparison showed the enrichment of GO terms related to growth of the tomato plants while SE + M vs C and SE + M vs M treatment comparison revealed GO terms related to defence response along with growth. The application of SE along with the Macrophomina vs only Macrophomina infection revealed higher number of stress-related DEGs of different pathways like; kinases, transcription factors and stress-related hormones. These pathways reflect that SE indeed help plant during stress condition. These pathways could involve unique disease tolerance mechanisms in tomato. The present study evidenced the molecular basis of controlling the disease manifestation in tomato crops using SE. For larger prospective, the application of SE should be tested at the field level to validate and support current finding for the commercial application of SE against disease manifestation in the tomato.

Supplementary Information

Below is the link to the electronic supplementary material.

13205_2023_3565_MOESM1_ESM.jpg (386.2KB, jpg)

Supplementary file1Supplementary figure S1: PCA analysis of expression data to visualize variation across samples and replicates (JPG 386 KB)

Supplementary file2 (DOCX 13 KB) (12.9KB, docx)
Supplementary file3 (DOCX 13 KB) (12.6KB, docx)
Supplementary file4 (DOCX 48 KB) (47.6KB, docx)
Supplementary file5 (DOCX 15 KB) (15.3KB, docx)

Acknowledgements

CSIR-CSMCRI communication no. is 108/2021.The authors are thankful to the CSIR, New Delhi for providing the funds under Seaweed Mission Mode project (HCP0024).

Author contributions

JK & MD executed the experiment. TCB, PA, DRG and PKA were involved in data analysis and writing manuscript, PKA conceptualized and coordinated the experiments. All the authors have read and approved the manuscript.

Funding

This work was supported by Seaweed Mission mode project (HCP0024), CSIR, New Delhi.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Human and/or animal rights

The present research is not involved human and/or animal participation.

Contributor Information

Parinita Agarwal, Email: pagarwal@csmcri.res.in.

Doddabhimappa R. Gangapur, Email: drgangapur@csmcri.res.in

Pradeep K. Agarwal, Email: pagarwal@csmcri.res.in

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13205_2023_3565_MOESM1_ESM.jpg (386.2KB, jpg)

Supplementary file1Supplementary figure S1: PCA analysis of expression data to visualize variation across samples and replicates (JPG 386 KB)

Supplementary file2 (DOCX 13 KB) (12.9KB, docx)
Supplementary file3 (DOCX 13 KB) (12.6KB, docx)
Supplementary file4 (DOCX 48 KB) (47.6KB, docx)
Supplementary file5 (DOCX 15 KB) (15.3KB, docx)

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

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