Functional categorization of de novo transcriptome assembly of Vanilla planifolia Jacks. potentially points to a translational regulation during early stages of infection by Fusarium oxysporum f. sp. vanillae
Upon exposure to unfavorable environmental conditions, plants need to respond quickly to maintain their homeostasis. For instance, physiological, biochemical and transcriptional changes occur during plant-pathogen interaction. In the case of Vanilla planifolia Jacks., a worldwide economically important crop, it is susceptible to Fusarium oxysporum f. sp. vanillae (Fov). This pathogen causes root and stem rot (RSR) in vanilla plants that lead to plant death. To investigate how vanilla plants, respond at the transcriptional level upon infection with Fov, here we employed the RNA-Seq approach to analyze the dynamics of whole-transcriptome changes during two-time frames of the infection.
Analysis of global gene expression profiles upon infection by Fov indicated that the major transcriptional change occurred at 2 days post-inoculation (dpi), in comparison to 10 dpi. Briefly, the RNA-Seq analysis carried out in roots found that 3420 and 839 differentially expressed genes (DEGs) were detected at 2 and 10 dpi, respectively, as compared to the control. In the case of DEGs at 2 dpi, 1563 genes were found to be up-regulated, whereas 1857 genes were down-regulated. Moreover, functional categorization of DEGs at 2 dpi indicated that up-regulated genes are mainly associated to translation, whereas down-regulated genes are involved in cell wall remodeling. Among the translational-related transcripts, ribosomal proteins (RPs) were found increased their expression exclusively at 2 dpi.
The screening of transcriptional changes of V. planifolia Jacks upon infection by Fov provides insights into the plant molecular response, particularly at early stages of infection. The accumulation of translational-related transcripts at early stages of infection potentially points to a transcriptional reprogramming coupled with a translational regulation in vanilla plants upon infection by Fov. Altogether, the results presented here highlight potential molecular players that might be further studied to improve Fov-induced resistance in vanilla plants.
KeywordsTranslational regulation Biological defense Transcriptional reprogramming Biotic stress Ribosomal proteins
Eukaryotic initiation factors
General control non-derepressible
log2 of fold change
High-throughput sequencing of RNA
Large subunit of ribosomal protein
Small subunit of ribosomal protein
Throughout evolution, plants have developed multiple defense strategies to cope with pathogens. The first defense line consists of pre-existing physical and chemical barriers, which restrict their entry . In addition to these constitutive barriers, plants have developed an immune response mechanism that is based on the detection of elicitor compounds derived from pathogens, known as Pathogen-Associated Molecular Patterns (PAMPs) . Such defense response activated by the PAMPs or PAMP-Triggered Immunity (PTI), usually restricts the proliferation of the pathogen [3, 4, 5, 6, 7]. However, some pathogens have circumvented this response by developing effector proteins that interfere or suppress PTI [8, 9, 10]. In this sense, the so-called co-evolutionary ‘arms race’ between plants and pathogens has defined the establishment of the Effector-Triggered Immunity (ETI), a defense line that begins with the recognition of PAMPs by plant pattern recognition receptors (PRRs) . The signals generated by PRRs are transduced through Mitogen-activated Protein Kinases (MAPKs), which in turn activate transcription factors for gene regulation that leads to a proper plant defense response . Among the plant responses, the Hypersensitive Response (HR), the programmed cell death, the expression of proteins related to pathogenesis or the lignification of the cell wall are included [13, 14, 15, 16, 17, 18].
Vanilla planifolia Jacks. is one of the most economically relevant orchids. It is produced extensively in several countries and is the main natural source of one of the most widely used flavoring agents in the world, vanillin [19, 20]. Its cultivation has spread throughout the world, with Madagascar and Indonesia as the leaders of annual production (35.5 and 34.5%, respectively), followed by China (13.7%) and Papua New Guinea (4.1%) [21, 22, 23, 24, 25]. Although Mexico is the center of domestication and diversification of this crop, vanillin production is positioned in the fifth place, contributing to only 4.0% of world production . Importantly, vanilla plants are susceptible to parasites and pathogens. The most lethal pathogen that afflicts vanilla is Fov, a pathogenic form of the genus Fusarium that specifically infects this plant species [22, 25, 26]. This pathogen causes RSR, as well as the colonization of vascular tissues that finally leads to plant death. Several studies indicate that V. planifolia has a high susceptibility and incidence of Fov [25, 27, 28]. For instance, infection of vanilla plants by this pathogen is capable of destroying 65% of the plantation [22, 25, 26]. The lack of genetic variability of V. planifolia is another factor that worsens the scenario [26, 29, 30]. Thus, given the economic importance of V. planifolia, is mandatory to do an effort to elucidate the overall plant response upon infection by this pathogen, likewise, has been done in other crops [31, 32, 33]. Moreover, since inferences from mRNA expression data are valuable as it reflects changes with a biological meaning, we looked into the transcriptome of V. planifolia roots exposed to Fov, to figure out the responsive mechanisms at early (2 days after inoculation, 2dpi) and later (10 days after inoculation, 10 dpi) stages of infection. Gene expression profiles indicated that major transcriptional changes occur at 2 dpi. Accordingly, vanilla plants accumulate transcripts associated to several processes, but mostly translational regulation-related transcripts. Thus, this study provides the identification of molecular players in plant-pathogen interaction between V. planifolia and F. oxysporum f. sp. vanillae, particularly a transcriptional reprogramming coupled with a translational regulation. Our study is aimed to understand the response of vanilla plants, which could help to fight the most damaging disease of vanilla caused by Fov.
Assembly of the transcriptome of V. planifolia roots exposed to Fov
Analysis of gene expression and functional categorization of DEGs at 2 and 10 dpi
Gene Ontology categories significantly enriched in DEGs at 2 dpi in the infection caused by Fusarium in vanilla
cellular macromolecule biosynthetic process
macromolecule biosynthetic process
cellular biosynthetic process
protein metabolic process
cellular protein metabolic process
primary metabolic process
macromolecule metabolic process
cellular macromolecule metabolic process
cellular metabolic process
ribonucleoprotein complex biogenesis
cellular component biogenesis
structural constituent of ribosome
structural molecule activity
translation factor activity, nucleic acid binding
translation elongation factor activity
intracellular non-membrane-bounded organelle
cytosolic large ribosomal subunit
large ribosomal subunit
intracellular organelle part
cytosolic small ribosomal subunit
small ribosomal subunit
intracellular organelle lumen
cell wall modification
plant-type cell wall organization
cellular carbohydrate metabolic process
carbohydrate metabolic process
polysaccharide metabolic process
plant-type cell wall modification
transferase activity, transferring glycosyl groups
transferase activity, transferring hexosyl groups
external encapsulating structure
anchored to membrane
Functional association networks of DEGs at 2 dpi
On the other hand, for down-regulated genes at 2 dpi, 256 were recognized by String out of 294 submitted genes (Fig. 5b). Also, a central network (49 nodes) was obtained with genes involved mainly in cell cycle, DNA replication and cell wall organization (Fig. 5b) (Additional file 12: Table S7). In this case, genes encoding proteins such as Cyclin A1;1 (CycA1;1), Cyclin-dependent kinase B2 (CdkB2), Minichromosome maintenance 3 (Mcm3), Origin recognition complex subunit 3 (Orc3), Cellulose synthase-like protein D5 (Csld5), Cellulose synthase A catalytic subunit 8 (CesA8), Cellulose synthase A catalytic subunit 7 (CesA7), among others, clearly formed a central network (Fig. 5b) (Additional file 12: Table S7). Notably, these networks were exclusively for DEGs at 2 dpi, since DEGs corresponding to 10 dpi did not show a clear interaction (Additional file 11: Figure S5). In resume, the generation and visualization of relationships among DEGs at 2 dpi show significantly more interactions than expected. In the case of up-regulated genes, they are mainly associated to ribosome biogenesis and translation as well as in development, whereas down-regulated genes are involved in cell cycle, DNA replication and cell wall organization.
Differential gene expression of ribosome-related proteins at 2 dpi
As supported by several studies around the world, Fov is the principal species that causes RSR in vanilla plants [25, 39, 40]. Although the generation and use of resistant varieties are the best mean to restrict Fov, scarce information about the plant-pathogen interaction, as well as limited genetic resources, have impeded to eradicate or limit the devastation that cause Fov in vanilla production. Under such scenario, the understanding of mechanistic responses of vanilla plants upon infection by Fov is scarce and necessary. Therefore, the primary goal of this work was to elucidate the early and late mechanistic responses of vanilla plants induced by Fov through investigating whole transcriptional changes in root tissues (Fig. 1). The RNA-Seq technique was employed to detect the DEGs during two frame times of infection by this root-infecting fungal pathogen, namely at early (2 dpi) and late (10 dpi) stages. The RNA-Seq analysis carried out in roots revealed that 4480 and 881 genes were differentially modulated by Fov at 2 and 10 dpi, respectively, as compared to the control (Fig. 2). This result indicated that the major transcriptional change occurs at early stages of infection, encouraging further analysis for these DEGs (Fig. 3). After functional classification of DEGs at 2 and 10 dpi, it was further confirmed that only DEGs at 2 dpi contained enriched functional categories (Fig. 4). For instance, enrichment analyses revealed the involvement of DEGs at 2 dpi to ribosome biogenesis and translation for up-regulated genes, whereas down-regulated genes were mainly associated to cell wall biogenesis (Table 1).
Most biological processes, from cell differentiation to organ development, as well as the adaptation to the environment, relies on transcriptional adjustments. Even that gene expression regulation is solidly established, it is clear that regulation beyond this level also plays a pivotal role in modulating key biological processes. Among the enriched functional categories for DEGs at 2 dpi, translation was the most prominent among up-regulated genes (Table 1) (Additional file 10: Figure S4), suggesting that this biological process is significantly impacted upon infection by Fov. Moreover, the formation a single network involving all these RPs supports a putative function in the early stages of infection by Fov (Fig. 5a). On the other hand, the finding that down-regulated genes are mostly involved in cell wall modifications (Table 1), is in agreement with the known susceptibility of V. planifolia plants to Fov. In this regard, since the plant cell wall acts as an important barrier against pathogen penetration by activating cell wall strengthening-related genes , the down-regulation of these genes reflects the facilitation of pathogen entry and then the negative impact on processes such as cell division and DNA replication of plant cells (Fig. 5b).
Being the basic infrastructure for protein translation, ribosomal proteins (RPs) have been known primarily for their housekeeping functions . However, in the recent years, emerging functions of RPs have been described, including regulation of gene expression through translational mechanisms [43, 44]. One hint for this is, for example, that even there are at least 230 genes encoding RPs in the Arabidopsis genome, a single member of each family of RPs has been found as part of the subunits of ribosomes, suggesting that expression of the additional RPs are subjected to different cues, including environmental conditions [45, 46]. Among the up-regulated RPs found in this work, RPL13 has been related to the tolerance of potato to Verticullum dahliae . Similarly, RPL10, RPS12/S23, and PRPL19e [48, 49], as well as the expression of RPS6, RPL19, RPL7, and RPS2 , have been associated to plant response against bacteria and virus; respectively. Also, RPS10 and RPS10p/S20e have been found to be up-regulated by Phytophthora sojae in Glycine max . Finally, RPL12 and RPL19 also have been shown to participate in the resistance against P. syringae in Nicotiana benthamiana and A. thaliana, respectively [49, 52]. On the other hand, abiotic stress has also been found to induce transcription of RPs. For example, transcript levels of RPS15a (and its variants A, C, D and F) increased significantly in response to heat stress in Arabidopsis . This was also the case for RPS14, RPL13, and RPL30 in Arabidopsis, in which their expression levels augment under the treatment with benzylaminopurine . In addition, RPL10 and RPL10C were induced when Arabidopsis plants were treated with UV, like those results obtained in maize plants [55, 56]. Finally, regarding low temperature conditions, increase of RPS6, RPS13 and RPL37 have been observed in Glycine max and Brassica napus [57, 58]. Besides the association of these RPs to biotic or abiotic stresses, functional characterization of them has allowed to elucidate their role in plants. In that sense, mutation of RPL10 causes lethality of the female gametophyte in Arabidopsis . Also, the mutation of RPS13A results in a reduction of cell division, retardation of flowering, and delayed growth of shoots and leaves . Similar phenotypes of growth retardation and fertility reduction have been reported in the RPL23aA mutant . In summary, until now, the central role of RPs in development as their global participation in response to abiotic stress in iron and phosphate deficit conditions has been assessed. Here, we report for the first time the RPs global participation in response to biotic stress in a translational manner.
Under such scenario, upon infection by Fov, root cells of vanilla plants likely change the expression of RPs, resulting in alterations of ribosomes composition, as reported against abiotic stress  and, therefore, in modulation of translation for certain transcripts as a response of fungal invasion (Fig. 7). This is particularly relevant since this is the first time that RPs are associated to Fov-derived response in vanilla plants. Moreover, it has been indicated that the typical chromosomal number of V. planifolia is 2n = 32 and more recently, cytogenetic studies conducted in the Mansa morphotype, reported an intra-individual variation in the number of chromosomes in the apical cells of the root, which may vary from 2n = 20 to 2n = 32 or more. Likewise, the existence of a “progressively partial endoreplication” in V. planifolia has been reported, however, this process does not occur in all tissues and some studies have reported that less than half of the genome of V. planifolia is being replicating effectively in each cycle. However, we consider that the methodology used in this study minimizes the effect of this phenomenon, so we propose the role of the translational regulation in the early plant response in the interaction with pathogen .
The screening of transcriptional changes of V. planifolia upon infection by F. oxysporum f. sp. vanillae shows that the major change occurs at early stages of infection, according to the analysis of DEGs at 2 dpi that shows, among other biological processes, the transcription of RPs increases specifically at this moment. Moreover, given the changes of these RPs are involved in plant developmental programs, as well as in response to biotic and abiotic stress conditions, their differential expression point to a biological role during infection. Therefore, is proposed that in response to Fov infection, root cells of vanilla plants activate a transcriptional reprogramming coupled with a translational regulation. The results presented here highlight key processes and potential molecular players that might be further studied to develop vanilla breeding programs, help to fight the most damaging disease of this crop.
From plants of V. planifolia Jacks. (Mansa morphotype) growing on a farm located in the Totonacapan region (Veracruz, Mexico), samples were collected and propagated under greenhouse conditions. Vigorous and pathogen-free plants were used in the present study at the developmental age of 12 weeks for infectivity assays. Such plants exhibited leaf morphology characteristic of V. planifolia. Sixty plants were distributed in twelve groups of five each one, for an experimental design intended for four treatments (two times conditions and two control) and three biological replicates by treatment (Fig. 1). The time conditions were 2 dpi (2 days post-inoculate) and 10 dpi (10 days post-inoculate) and the controls were plants non-treated with Fov.
The in vitro fungal infection of V. planifolia plants was carried out with the JAGH3 strain of Fov. This strain of Fov was isolated from V. planifolia (Mansa morphotype) with evident RSR , its pathogenic capacity was proven, so it has been used for further studies [25, 40, 78]. Briefly, cuttings of V. planifolia were subjected to darkness for ten days. The absence of light exposition allowed the generation of new roots. A mechanical incision was made in each root under aseptic conditions. Then, roots were exposed to an aqueous solution of spores with a concentration of 1 × 106 CFU of Fov (JAGH3 strain). The inoculation was carried out directly on the substrate where cuttings were established. Cuttings belonging to the control group were treated similarly, exposing them to an aqueous solution free of spores. For a single biological experiment, control and treatment experiments consisted of 30 plants of the same age, established on substrate and maintained under greenhouse conditions with a 12-h photoperiod (shaded). Two biological experiments were carried out covering two frames of time of Fov infection, namely 2 and 10 days post-inoculation (dpi) (Fig. 1). For each of the treatments and their respective controls, five tissue samples were collected in each case, pooled and processed immediately for RNA extraction. In total, twelve pools were obtained, covering two biological experiments for each treatment time (2 and 10 dpi).
The pathogenicity of the JAGH3 strain on vanilla plants was evaluated following the protocol specified by Koyyappurath et al., (2015) ; which is based on the observation and recording of infection symptoms, on the alternate days after inoculation. According to the above, the symptoms were observed and recorded on alternate days in aerial parts, which include aerial roots, in addition to the stem and leaves; The observation period was from day 1 to day 9, after inoculation. The presence of the characteristic symptoms of the infection was monitored using a rating scale of 0–4 as follows: 0 = no symptoms; 1 = the leaves lost their brightness; 2 = local browning visible on the stem; 3 = lodging of plants, brown areas and mycelium visible in the aerial parts; and 4 = totally rotten or dead plant.
Total RNA extraction
For the total RNA extraction from the roots of vanilla plants, a protocol was standardized based on a previous report . Briefly, 200 mg of root tissue were homogenized with the Trizol reagent and then treated with Phenol:Chloroform:Isoamyl Alcohol (25:24:1), followed by vortexing and centrifugation. The upper aqueous phase was transferred into silica columns included in the SV Total RNA Isolation System extraction kit from Promega. The integrity of the obtained RNA was determined by electrophoresis in 2% agarose gel, stained with ethidium bromide (EtBr 0.5 μg ml− 1) under denaturing conditions. The concentration of total RNA samples was verified using a NanoDrop spectrophotometer, as well as its RNA Integrity Number (RIN) values were obtained with an Agilent 2100 Bioanalyzer system (Agilent Technologies). RNA samples with RIN values > 6 were used for cDNA synthesis and subsequent sequencing.
Generation and sequencing of cDNA libraries
The generation and sequencing of the cDNA libraries was carried out in the University Unit of Massive Sequencing and Bioinformatics of the Institute of Biotechnology of the National Autonomous University of Mexico (UUSMB IBT-UNAM). In total, the construction of 12 cDNA libraries was carried out. Afterwards, the sequencing of the cDNA libraries was performed using the Nextseq 500 illumina platform, generating paired-end reads of 76 bp. In total, 204 million 517 thousand 080 reads were obtained.
De novo transcriptome assembly and annotation
Quality of reads obtained from the high-throughput sequencing was carried out using the FastQC software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads above 32 nt, without the presence of adapters were considered for further analysis. First, to filter and discard reads corresponding to the plant pathogen used in the infectivity assays, alignment of reads was performed with the Smalt software (version 0.7.6) using the reference genome of F. oxysporum f. sp. lycopersici strain Fol4287. Then, de novo transcriptome assembly corresponding to V. planifolia reads was made using the Trinity software (version 2.4). For assessing the quality of the obtained transcriptome assembly, metrics like total number of contigs, longest contig length, mean and median contig length, and N50 were calculated using TransRate, followed by an analysis with BUSCO to explore completeness according to conserved ortholog content. The analysis with the BUSCO software was carried out using the Liliopsida odb10* database, following the software default parameters . Subsequently, the annotation of the transcriptions was made with the Trinotate software. The search for the open reading frames in the transcriptions was made with the TransDecoder software. Transcripts and amino acid sequences were aligned against the UniProt database using Blastn and Blastx. Moreover, the presence of PFAM domains in the protein sequences predicted from the transcripts was tested with the HMMER software. Finally, the annotation of the transcripts was done using Blast2go , as well as the databases of Gene Ontology (GO), KEGG, COG.
Differential expression analysis and functional categorization
For assessing differentially expressed genes (DEGs) of the assembled transcripts, a method based on mapping the reads against the assembled transcriptome was done. Such mapping of reads was done with Bowtie2, as part of the Trinity pipeline, followed by an analysis with RSEM. The results obtained by RSEM were submitted to IDEAMEX , a website intended for differential expression analysis using several approaches. Specifically, IDEAMEX analysis is based on DESeq , DESeq2 , NOISeq  and EdgeR  methods. For selection of DEGs, the following parameters were used: padj <= 0.04, FDR < = 0.04 and prob.> = 0.96, and a logFC> = 2. Heatmaps for DEGs were done in R using the ggplot2 package . From the functional annotation of the assembled transcripts obtained by Blast2GO, visualization of the transcriptome regarding expression patterns was performed with Mapman V2 software . For functional categorization, DEGs were submitted to the agriGO v2.0 software , selecting the singular enrichment analysis (SEA). The enrichment analysis was carried out according to the following parameters in the AgriGo v2.0 Software, Statistical test method Fisher; Multi test adjustment method: Yekutieli (FDR under dependency); and 0.05 of significance level. The enrichment analysis was also carried out with PANTHER classification system software (v.14.0). under the following specifications, statistical test method Fisher; Multi test adjustment method: Bonferroni; and 0.05 of significance level. Finally, gene networks among DEGs were obtained with the STRING software .
MTSC gratefully acknowledges to the Consejo Nacional de Ciencia y Tecnología (CONACYT) for the scholarship provided (259580) and to the Tecnológico Nacional de México for financial support of this work. Also, thanks to the Biologist Lolvin Delaurens-Santacruz for his help during the experiments, as well as Dr. Alejandro Blanco, Dr. Gastón Contreras Jiménez and Dr. Matías Baranzelli for their valuable help, generous support and encouragement during the preparation this manuscript. Finally, we appreciate the technical assistance during RNA extractions to Edder Darío Aguilar-Méndez, Daniel Abisaí Jerez-Prieto, Dulce Natali Gómez-Hernández and Cecilio Mauricio-Ramos.
MTSC, JAG, LGIA and MLR conceived and designed the experiments. MTSC and EEEH performed the experiments and collected samples. VJJ, LVA, MTSC, EEEH and JGJ performed primary data analysis and carried out bioinformatics analysis. MTSC, JGJ and MLR conceived and organized the manuscript structure. All authors contributed during the manuscript preparation and approved the final manuscript.
This work was funded by Tecnológico Nacional de México (project number 5911.16-P Transcriptome of Vanilla planifolia Jacks., exposed to infection caused by Fusarium oxysporum f. sp. vanillae). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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The authors declare that they have no competing interests.
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