Next Article in Journal
“Bligh and Dyer” and Folch Methods for Solid–Liquid–Liquid Extraction of Lipids from Microorganisms. Comprehension of Solvatation Mechanisms and towards Substitution with Alternative Solvents
Previous Article in Journal
Transplantation of Menstrual Blood-Derived Mesenchymal Stem Cells Promotes the Repair of LPS-Induced Acute Lung Injury
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 18
Issue 4
10.3390/ijms18040712
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

De Novo Assembly, Annotation, and Characterization of Root Transcriptomes of Three Caladium Cultivars with a Focus on Necrotrophic Pathogen Resistance/Defense-Related Genes

by
Zhe Cao
and
Zhanao Deng
*
Gulf Coast Research and Education Center, Department of Environmental Horticulture, IFAS, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2017, 18(4), 712; https://doi.org/10.3390/ijms18040712
Submission received: 9 February 2017 / Revised: 21 March 2017 / Accepted: 24 March 2017 / Published: 27 March 2017
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

:
Roots are vital to plant survival and crop yield, yet few efforts have been made to characterize the expressed genes in the roots of non-model plants (root transcriptomes). This study was conducted to sequence, assemble, annotate, and characterize the root transcriptomes of three caladium cultivars (Caladium × hortulanum) using RNA-Seq. The caladium cultivars used in this study have different levels of resistance to Pythium myriotylum, the most damaging necrotrophic pathogen to caladium roots. Forty-six to 61 million clean reads were obtained for each caladium root transcriptome. De novo assembly of the reads resulted in approximately 130,000 unigenes. Based on bioinformatic analysis, 71,825 (52.3%) caladium unigenes were annotated for putative functions, 48,417 (67.4%) and 31,417 (72.7%) were assigned to Gene Ontology (GO) and Clusters of Orthologous Groups (COG), respectively, and 46,406 (64.6%) unigenes were assigned to 128 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A total of 4518 distinct unigenes were observed only in Pythium-resistant “Candidum” roots, of which 98 seemed to be involved in disease resistance and defense responses. In addition, 28,837 simple sequence repeat sites and 44,628 single nucleotide polymorphism sites were identified among the three caladium cultivars. These root transcriptome data will be valuable for further genetic improvement of caladium and related aroids.

Graphical Abstract

1. Introduction

Caladium (Caladium × hortulanum) is an important ornamental aroid which is commonly used in containers, hanging baskets, and landscapes for their variably-shaped and colored leaves. The genus Caladium is native to the tropical regions of South and Central America, including Brazil, Colombia, Ecuador, and Peru. Modern cultivated caladiums are diploids with 2n = 2x = 30 chromosomes [1]. Recent molecular and cytological analyses have shown that cultivated caladiums are most likely to have originated from interspecific hybridizations between Caladium bicolor (Aiton) Vent. and Caladium schomburgkii Schott, which share the same chromosome number (30), similar nuclear DNA contents, and similar DNA fingerprints [1,2,3]. The great majority of commercial caladium plants are forced from tubers. More than 95% of the caladium tubers used in the world are produced in Florida [4].
A major issue affecting commercial caladium tuber and plant production is root rot caused by Pythium myriotylum Drech. Several other necrotrophic fungi including Sclerotium rolfsii Sacc. and Rhizoctonia solani Kuehn could also incite rot on caladium roots. Pythium-infected caladiums exhibit stunted growth, substantial losses of roots, and much-reduced tuber yield [4,5]. For example, Riding and Hartman [6] grew caladium tubers in P. myriotylum-infected substrates and observed that Pythium infection led up to 40% reduction in tuber yield. Caladium growers used to rely on methyl bromide fumigation for control of soil-borne pathogens [5], but methyl bromide has been phased out due to its severe side effect of depleting ozone in the atmosphere, and few alternative fumigants are available now. As a result, growers are facing much more severe losses from Pythium root rot.
Developing and using disease-resistant cultivars is an effective and environmentally-friendly approach to managing diseases in many crop production systems. For many soil-borne diseases caused by necrotrophic fungi/oomycetes that exhibit facultative pathogenicity and broad parasitic spectrums, the most common type of host resistance has been partial resistance [7]. Deng et al. [4,5] screened 42 caladium cultivars for Pythium root rot resistance and identified several commercial cultivars with moderate resistance, of which “Candidum” is particularly valuable, because it is also resistant to another necrotrophic pathogen, Fusarium solani (Mart.) Saa [8]. The continuous distribution of disease resistance level among the caladium cultivars evaluated in the reported studies indicates that the observed Pythium resistance is likely controlled by quantitative trait loci [4,5,9].
To date, our understanding of host resistance to necrotrophic pathogens has mainly come from studies in Arabidopsis, tomato, and rice [10,11,12]. These studies have shown that plant resistance toward necrotrophs is primarily mediated by recognition of pathogen-associated molecular patterns (PAMPs) via pattern recognition receptors (PRRs), signaling networks regulated by ethylene and jasmonic acid, and activation of biosynthesis of antimicrobial metabolites [13]. In Arabidopsis, dozens of genes encoding receptor-like kinases (RLKs) are essential for quantitative resistance to Fursarium oxysporum [14]. In rice, the gene OsACS2, which encodes 1-aminocyclopropane-1-carboxylic acid (ACC) synthase, plays an important role in conferring resistance to Rhizoctonia solani [15]. In tomato, expression of Arabidopsis thionin (an antimicrobial compound) gene Thi2.1 significantly enhanced tomato resistance against Fusarium wilt [16]. Necrotrophic pathogens have caused severe losses to many crops in the world, and a better understanding of the type and expression of disease resistance and defense-related genes in plant roots could greatly facilitate further genetic improvement of crop resistance to necrotrophic pathogens.
Understanding the genetic basis for caladium resistance to necrotrophic pathogens, particularly Pythium, has been a very important objective in caladium genetic studies. Such knowledge would enable plant breeders to select the appropriate breeding parents and breeding crosses, and design effective and efficient selection schemes for the development of new caladium cultivars with enhanced resistance to necrotrophic pathogens. So far, little genomic or transcriptomic data are available for caladiums. The only publicly available DNA data in caladium are 99 simple sequence repeats (SSRs) reported by Gong and Deng [2], and 49 nucleotide sequences and 16 genes in the National Center for Biotechnology Information (NCBI) databases. None of the deposited DNA sequences were involved in disease resistance or defense. To discover putative disease resistance/defense genes in caladium, genomic and/or transcriptomic data are urgently needed.
In recent years, advances in next-generation sequencing (NGS) and assembly algorithms have resulted in a fast and deep transcriptome sequencing technique termed RNA-Seq. Recent development of de novo transcriptome assemblers has made RNA-Seq technology readily available for the study of non-model plants whose genomic sequences are largely unavailable [17]. Data from transcriptome sequencing has greatly facilitated genome-wide gene discovery, identification of genes involved in specific biosynthesis pathways, and development of molecular markers [18]. In recent years, de novo transcriptome sequencing has been reported in numerous non-model plant species for identifying putative genes involved in disease or stress responses. For example, Zhang et al. [19] identified 250 nucleotide-binding site and leucine-rich repeat (NB-LRR) transcripts from shoots, leaves and flower buds in two Camellia species. In Oryza officinalis, He et al. [20] discovered disease resistance genes involved in signaling and plant hormone regulation pathways using leaves, stems and roots. In gerbera, Fu et al. [21] sequenced leaf transcriptomes and found 137 transcripts that were related to the phenylpropanoid biosynthesis pathway that impacts disease resistance. Additionally, transcriptome sequencing data have been used for the development of SSR and single nucleotide polymorphism (SNP) molecular markers in many plant species [22,23].
Herein, the root transcriptomes of three caladium cultivars (“Candidum”, “Gingerland”, and “Miss Muffet”) were sequenced using the paired-end sequencing protocol and the Illumina HiSeq 2000 platform and were assembled de novo. The three cultivars were selected because they are very important breeding parents, and most importantly, they displayed significant differences in Pythium root rot resistance. “Candidum” is moderately resistant to Pythium root rot, while “Gingerland” and “Miss Muffet” are highly susceptible to this disease [4,5,9]. The objectives of this study were to (1) assemble, annotate and characterize the root transcriptomes of these caladium cultivars; (2) identify transcripts that are likely involved in disease resistance/defense pathways; and (3) identify SSR and SNP sites in caladium transcriptomes. This is the first transcriptome study in caladium; the data generated here will be of significant value for development and use of molecular markers in genetic improvement of caladium.

2. Results and Discussion

2.1. Quality Control and De Novo Transcriptome Assembly

A total of 68,000,078, 50,980,474, and 67,583,936 raw reads were obtained for “Candidum”, “Gingerland”, and “Miss Muffet”, respectively (Table 1). The FastQC v0.11.4 analysis of raw sequences showed that the percentage of Q20 (sequencing quality score 20, representing a call accuracy of 99%) reads ranged from 96.84% (“Candidum”) to 96.97% (“Gingerland”) and the percentage of GC contents in these reads varied from 49.20% (“Candidum”) to 49.84% (“Miss Muffet”). After filtering out of adaptors and low-quality reads, 61,439,682 (“Candidum”), 46,148,492 (“Gingerland”), and 61,034,284 (“Miss Muffet”) clean reads were retained. The high Q20 value (>95%) indicated that the sequencing data of the three caladium libraries were of high quality and suitable for conducting assembly.
De novo transcriptome assembly of the clean reads in Trinity yielded 255,215 (“Candidum”), 232,333 (“Gingerland”), and 258,116 (“Miss Muffet”) contigs, respectively (Table 2). The average length and N50 length of these contigs were similar among “Candidum”, “Gingerland”, and “Miss Muffet”: average lengths from 292 bp (“Miss Muffet”) to 299 bp (“Gingerland”), and N50 lengths from 463 bp (“Miss Muffet”) to 492 bp (“Gingerland”). Of these contigs, 66.81% (“Gingerland”) to 67.56% (“Candidum”) were <200 bp, 21.20% (“Candidum”) to 21.45% (“Gingerland”) were from 200 to 500 bp, and 11.19% (“Miss Muffet“) to 11.24% (“Candidum”) were >500 bp (Table S1).
Unigene assemblies of the three caladium cultivars were constructed separately, resulting in a total of 133,737 (46,962 clusters and 86,775 singletons), 122,994 (43,721 clusters and 79,273 singletons), and 135,589 (47,844 clusters and 87,745 singletons) unigenes for “Candidum”, “Gingerland”, and “Miss Muffet”, respectively, with an average length of 729 bp (“Candidum”), 736 (“Gingerland”) and 717 (“Miss Muffet”) bp, and an N50 length of 1525 bp (“Candidum”), 1514 (“Gingerland”), and 1487 bp (“Miss Muffet”) (Table 2).
Merging the unigenes from the three caladium cultivars resulted in a total of 137,354 (62,353 clusters and 75,001 singletons) unigenes for caladium, with an average length of 999 bp and an N50 length of 1755 bp (Table 2). The length distribution of these unigenes was as follows: ≈62.0% unigenes were 100–500 bp, ≈14.0% unigenes were 500–1000 bp, ≈14.5% unigenes were 1000–2000 bp, and ≈8.9% unigenes were >2000 bp (Table S2).
So far, there has been no report of transcriptome assembly in caladium. In Amorphophallus, another aroid species, Zheng et al. [24] sequenced and generated 135,822 unigenes with an average length of 523 bp and an N50 length of 635 bp. Overall, this set of assembled caladium unigenes were longer than those of Amorphophallus.

2.2. Functional Annotation of Assembled Unigenes and Coding Sequence (CDS) Prediction

To annotate assembled unigenes, the NCBI BLAST+ 2.2.28 was used to conduct sequence similarity search (E-value cutoff of ≤10−5) in public databases including the NCBI non-redundant protein (NR) and nucleotide sequence (NT) databases, the Swiss-Prot, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), or Gene Ontology (GO) database (Table 3). Out of the 137,354 assembled sequences, 68,827 (50.1%), 52,233 (38.0%), and 47,907 (34.9%) were successfully annotated in NR, NT and Swiss-Port databases, respectively. Further, 46,406 (33.8%), 31,417 (22.9%), and 48,417 (35.2%) unigenes were then annotated in KEGG, COG, and GO databases, separately. A total of 71,825 unigenes (52.3%) were annotated in at least one of these six databases (Table 3).
Out of the 68,827 unigenes annotated in NR, 43,016 (65.2%) unigenes had homology with the deposited reference sequences at E-value < 1.0 × 10−30 (Figure 1A), 9911 (14.4%) unigenes showed 80% or greater sequence similarity to reference genes, and 26,498 (38.5%) unigenes had 60% to 80% sequence similarity to the reference genes (Figure 1B). The unigenes assembled from caladium root transcriptomes were most homologous to genes of Vitis vinifera (23,401, 34.0%), remotely followed by Amygdalus persia (5024, 7.3%), Ricinus communis (4336, 6.3%), Japanese rice (4060, 5.9%), Populus balsamifera (3785, 5.5%), Fragaria vesca (2615, 3.8%), and Glycine max (2546, 3.7%) (Figure 1C). The relatively high levels of similarity between caladium unigenes and the genes of distantly related V. vinifera were surprising as caladium unigenes were expected to be more homologous to genes of rice or other monocotyledonous plants. Nevertheless, a similar phenomenon was observed in other aroid transcriptome studies. For example, 32.7% of Anthurium unigenes shared highest homologies to V. vinifera genes [25]. The exact causes of this phenomenon remains to be elucidated.

2.2.1. Gene Ontology (GO) Classification

To functionally classify the assembled unigenes, sequences that were annotated in the NR database were then subjected to Gene Ontology (GO) analysis. Results showed that 68,827 caladium unigenes fell into three main categories, including Biological Process, Cellular Component, and Molecular Function (Figure 2 and Table S3). In the Biological Process category, the Cellular Process (28,428 unigenes, 16.96%), Metabolic Process (27,380 unigenes, 16.33%), and Single-Organism Process (18,997 unigenes, 11.33%) were the three primary subcategories. With respect to the Cellular Component category, Cell (35,952 unigenes, 25.38%), Cell Part (35,948 unigenes, 25.37%), and Organelle (29,813 unigenes, 21.04%) were the most enriched subcategories, and in the Molecular Function category, the top three subcategories were Catalytic Activity (24,305 unigenes, 45.39%), Binding (21,486 unigenes, 40.13%), and Transporter Activity (3322 unigenes, 6.20%).
Out of the 68,827 unigenes annotated for GO classifications, 1284 unigenes seem to be root-specific (Table S4). A great majority of them (1284, 98.75%) could be grouped into seven functional categories, including root morphogenesis (62 unigenes, 4.83%), root meristem growth (65 unigenes, 5.07%), root hair elongation (261 unigenes, 20.33%), root epidermal cell differentiation (47 unigenes, 3.67%), root development (535 unigenes, 41.67%), and lateral root formation (85 unigenes, 6.62%) (Figure 3). A small number of the root-specific unigenes (16, 1.25%) could not be assigned to specific functional categories, thus grouped into “Others”. A total of 103 root-specific unigenes are related to disease resistance or plant defense (Table S5).

2.2.2. Clusters of Orthologous Groups (COG) Classification

To predict the putative function of unigenes, all caladium unigenes were compared with the Clusters of Orthologous Groups (COG) database. As shown in Figure 4, the largest number of unigenes had “general function prediction only” (10,905, 13.85%), followed by “transcription” (7448, 9.56%), “translation, ribosomal structure, and biogenesis” (7082, 9.46%), “function unknown” (6245, 7.93%), “replication, recombination, and repair” (6194, 7.87%), “cell cycle control, cell division, chromosome partitioning” (5347, 6.79%), “signal transduction mechanisms” (4910, 6.24%), and other 20 functions, from “posttranslational modification, protein turnover, chaperones” (4722, 5.99%) to “nuclear structure” (19, 0.02%) (Table S6).

2.2.3. Kyoto Encyclopedia of Genes and Genomes (KEGG) Classification

To identify the active biological pathways in caladium roots, the assembled unigenes were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway database. Results showed that 46,406 (64.6%) unigenes could be assigned to 128 KEGG metabolic pathways. The top ten pathways are listed in Table 4. Among the primary KEGG categories, “metabolism pathways” had the highest percentage of caladium unigenes (26.9%), followed by “RNA transport” (11.6%), “biosynthesis of secondary metabolites” (10.3%), “mRNA surveillance pathway” (9.0%), “glycerophospholipid metabolism” (7.8%), and “endocytosis” (7.7%).

2.2.4. CDS Prediction

The protein coding sequence (CDS) and the amino acid sequence of all assembled unigenes were predicted using NCBI BLAST 2.2.28+ and ESTScan (version 3.0.2). Unigenes were firstly aligned in the NR, Swiss-prot, KEGG, and COG databases for CDS prediction. The non-hit unigenes were then subjected to ESTScan (version 3.0.2). In total, the CDS of 68,197 unigenes were successfully predicted in the databases described above. The majority of predicted CDS were in the ranges of 200–500 bp (49.4%), followed by CDS of 600–1000 bp (25.6%), CDS of 1100–2000 bp (18.8%), and CDS of >2000 bp (6.0%) (Figure 5A). The majority of CDS of unigenes (99.2%) predicted by the ESTScan software were less than 500 bp (Figure 5B).

2.2.5. Comparison with Reported Transcriptome Assemblies

The caladium root transcriptome as assembled above consists of 137,354 transcripts and 71,825 unigenes. This number of unigenes largely exceeds the number of gene models in the majority of sequenced plant genomes (20,000 to 40,000) [26]. This phenomenon seems to be not uncommon in transcriptome assemblies and analyses of other aroids [25] and other non-model plants [26]. In Anthurium, a high proportion of short unigenes (69.8%) and a large number of unigenes (111,268) were reported [25]. Several factors might have contributed to this phenomenon. The lack of reference genome sequences in non-model plants makes it difficult to reconstruct full-length gene transcripts from short sequence reads and read alignments. Consequently, multiple unigenes representing the same genes might have failed to be assembled and were treated as independent unigenes, resulting in large numbers of short unigenes and excessive unigene numbers [26]. Alternative splicing of protein-coding genes and expression of transposable elements and pseudogenes will certainly result in larger numbers of transcripts than the number of gene models annotated based on genome sequences. A third potential cause of the large number of unigenes in the current caladium root transcriptome assembly might be the relative large size of the caladium genome (≈4.5 Gb) [1]. When a species possesses a large sized and highly duplicated genome, the number of unigenes tended to be larger [26]. In Amorphophallus bulbifer, an aroid with a genome of ≈9.0 Gb, 119,678 unigenes was reported for its leaf transcriptome. In Allium cepa, a plant with a nuclear genome of 16 Gb, its bulb transcriptome consisted of 115,251 unigenes [27].
Nearly 50% of the caladium unigenes were short (200–500 bp). This was a major and frequent issue in de novo assembly of transcriptomes of non-model plants [28,29,30]. Several factors might have contributed to the presence of large numbers of short unigenes, including under-estimated sequencing depth needs, the use of stringent parameters in the assembly process, lack of reference genomes for read mapping, and highly complex transcriptomes. Deep sequencing using the single molecule real-time sequencing (SMRT) technology may be a very effective approach to extending unigenes to full-length transcripts.
De novo assembly of a soybean root transcriptome resulted in 37,287 contigs and 35,081 unigenes [31]. In Callerya speciosa, a total of 161,926 contigs and 111,706 unigenes were reported for its de novo assembled root transcriptome [32]. A de novo root transcriptome of Louisiana iris consisted of 525,498 transcripts and 313,958 unigenes [33]. These studies, as well as ours, on caladium suggest that the number of assembled contigs and the number of annotated unigenes could vary remarkably among root transcriptomes of different plant species. This variation may indicate a high level of diversity among plant species in root transcriptome (and metabolic) activities.

2.3. Identification of Putative Unigenes Involved in Resistance and Defense against Necrotrophic Pathogens

In response to attacks by necrotrophic pathogens, the innate immune system of plants perceives PAMPs with PRRs and activates PAMP-triggered immunity (PTI) against the pathogens [13]. PTI involves a number of cellular processes, including accumulation of reactive oxygen species and nitric oxide, modulation of hormone level, secretion of antimicrobial compounds, and reinforcing of the cell wall [34]. In this study, we compared unigenes in the root transcriptome of “Candidum” (moderately resistant to P. myriotylum) with those in the root transcriptomes of “Gingerland” and “Miss Muffet” (both highly susceptible to the pathogen) and identified 4518 unigenes that were present only in the root transcriptome of “Candidum” (Figure 6 and Table S7). These unique unigenes were examined for potential involvement in disease resistance and plant defense, in particular, (1) early signal perception and transduction in plant-pathogen interactions; (2) hormone regulation during plant defense responses to necrotrophic pathogens; and (3) secondary metabolites with antimicrobial activities in plant defense (Table 5).

2.3.1. Early Signal Perception and Transduction in Plant-Pathogen Interactions

Recognition of PAMPs through membrane-localized PRRs during plant-pathogen interactions can induce an immune response syndrome [35]. Previous studies have indicated that a series of plant RLKs could be PRRs for plant immune responses [36]. Lack of RLKs could attenuate plant responses to pathogens, resulting in disease susceptibility [37]. All RLKs are transmembrane proteins, each containing a signal sequence, a putative extracellular domain at the amino-terminal, and an intracellular kinase domain at carboxyl-terminal [38]. Previous studies have shown that RLKs participate in many biologically important processes that confer disease resistance [39].
Of the 4518 unigenes found only in “Candidum”, 50 unigenes can be translated into RLKs, including one l-type lection receptor kinase (LecRK), one cysteine-rich receptor-like kinase (CRK2), 12 receptor-like serine/threonine-protein kinases (STKs), 31 proline-rich receptor-like protein kinases (PERKs), and five possible RLKs (Table 5 and Table S8). LecRKs are important immune receptors that bind to the effectors produced by Phytophthora infestans [40]. Previous studies have shown that LecRK genes confer resistance in Arabidopsis to several necrotrophic oomycetes including Phytophthora brassicae and Botrytis cinerea [41,42]. The one LecRK homolog in “Candidum” may play a positive role in its disease resistance. CRKs are one of the largest classes of RLKs in plants [43]. Studies in Arabidopsis have shown that CRKs regulate plant development, stress adaptation, and disease resistance [44]. Yang et al. [45] found that a novel CRK homolog (TaCRK1) is involved in wheat resistance to Rhizocotonia cerealis and the expression levels of TaCRK1 were significantly higher in moderately resistant lines than in highly susceptible lines. In “Candidum” root transcriptome, only one CRK homolog (CRK2) was observed (Table 5). Another important type of plant receptor-like kinases are STKs, which can phosphorylate the hydroxyl group (OH) of serine or threonine residues, resulting in functional alterations of target proteins [46]. The phosphorylated proteins play an important role in disease resistance pathway by triggering a series of downstream activities including protein modulation, protein-protein interaction, transcription factor activation, and secretion of antimicrobial metabolites [47]. It has been shown that two wheat STK homologs, Tsn1 and Stpk-V, are the key genes conferring resistance against biotrophic Blumeria graminis (wheat powdery mildew) and the necrotrophic pathogens Stagonospora nodorum (Stagonospora nodorum blotch) and Pyrenophora tritici-repentis (tan spot) [46,48]. In this study, 12 STK homologs were observed exclusively in the “Candidum” root transcriptome (Table 5). In addition, we found that a majority of RLKs in the “Candidum” (60%) root transcriptome belong to PERKs. In Arabidopsis, PERKs represent a small family of RLKs, all sharing an extracellular domain, a transmembrane domain, and a kinase domain [38]. PERKs in Arabidopsis roots are involved in callose and cellulose deposition; PERK1 mutants have lost their ability to accumulate callose and cellulose in cell walls [49]. Silva and Goring [50] found that PERK1 could response to both mechanical stresses and infection by fungal pathogens and may have a function in the general perception of wounds or recognition of pathogen stimuli. In the “Candidum” root transcriptome, 31 PERK homologs were observed (Table 5), and these PERKs may be good candidates for further gene expression studies. There were five possible RLK-like unigenes (Table 5) that could not be further annotated, suggesting that these RLK-like unigenes may represent unique transcripts in caladium and their functions remain to be studied.
After plant perception of PAMPs, signals from the PRR complexes are transferred to downstream targets through several secondary messengers, including influx of calcium (Ca2+), reactive oxygen species (ROS) burst, and activation of mitogen-activated protein kinases (MAPKs) [51]. The calcium influx is an early step in the signal transduction processes [52]. Ca2+ influx can be sensed by several stimulus-specific Ca2+-binding sensors such as calcium-dependent protein kinases (CPKs) and Calmodulins (CaMs). Previous studies have shown that transgenic potato plants containing StCDPK4 and StCDPK5 (CPK homologs) gain resistance to Phytophthora infestans [53]. A wheat CPK homolog (TaCPK7-D) is involved in determining resistance to necrotrophic pathogen Rhizoctonia cerealis. Here, we identified eight CPKs homologs only in “Candidum“ (Table 5 and Table S9) and suspect that they may be important signal sensors which modulate resistance to necrotrophic pathogens. CaMs and CaM-like (CML) genes could trigger plant defense responses to a broad range of pathogens. In tobacco, a CML gene, NtCaM13, is a key gene for resistance to the necrotrophic pathogens Pythium aphanidermatum and Rhizoctonia solani [54]. Four CaM gene homologs were identified among “Candidum” unigenes (Table 5). ROS are important signals to modulate the expression of defense-related genes to fungal pathogen attacks [55]. The production of ROS involves a number of oxidative enzymes such as the NADPH (nicotinamide adenine dinucleotide phosphate) oxidase, the superoxide dismutase, the cell wall peroxidase, the oxalate oxidase, the lipoxygenase, and the polyamine oxidase [56]. These enzymes work collaboratively or separately to initiate a series of responses including cell wall remodeling, signal transduction, programming cell death, and post-translation regulation to resist pathogens [56]. So far, our understanding of the roles ROS play in plant roots and resistance against necrotrophic pathogens is still rudimentary [56]. In banana, studies have shown that a NADPH oxidase homolog (Rboh) confers resistance against the necrotrophic pathogen Fusarium oxysporum. In Arabidopsis and cotton, F. oxysporum-resistant lines accumulated ROS faster than the susceptible lines [57,58]. In this study, we identified five putative ROS-related unigenes (superoxide dismutase) that were exclusively expressed in “Candidum” (Table 5). MAPK activation is another early signaling activity after plants sense the attack from pathogens [59]. Plant MAPK cascades transfer signals from PRRs to downstream components [60]. In Arabidopsis, MAPK cascades involve several MAPKs such as MEKK1, MKK1/MKK2 (two redundant MAPKKs), MPK4/MPK5 (two redundant MAPKs), and MPK3/MPK6 (two partially redundant MAPKs). Previous studies have shown that MPK3, MPK4, MPK6, MKK1, MKK2, and MEKK1 are involved in innate immune responses against necrotrophic pathogens [61]. Of 4956 unigenes only observed in the “Candidum” root transcriptome, there were eight MAPK homologs including three MPK1, two MPK2, one MPK4, one MPK5, and one MPK14 homolog (Table 5).

2.3.2. Hormone Regulation during Plant-Pathogen Interactions

Increased biosynthesis of hormones is an important plant defense response to invading pathogens [37]. In plants, ethylene (ET) and jasmonic acid (JA) are two important hormones involved in resistance against necrotrophic pathogens. S-adenosylmethionine (S-AdoMet) and 1-aminocyclopropane-1-carboxylic acid (ACC) are two precursors of ethylene [62]. Enzymes catalyzing ET biosynthesis reactions include ACC synthase (ACS) and ACC oxidase (ACO) [62]. Among the annotated unigenes in “Candidum”, both key enzymes participating in ET synthesis were identified (Table 5 and Table S10). Synthesized ethylene can be then perceived and transduced by ethylene receptors to trigger downstream biological responses. There are several types of ethylene receptors in Arabidopsis, such as ethylene receptors (ETRs), ethylene response sensors (ERSs), and ethylene insensitive (EINs) [63]. However, the roles ethylene receptors play in necrotrophic pathogen resistance seem to be different depending on plant species and the types of pathogens. In Arabidopsis, the ein2 (ethylene insensitive) mutant lines displayed increased susceptibility to the necrotrophic pathogen Botrytis cinerea [64], while in soybean, the etr1 and etr2 (ethylene-resistant) mutant lines showed increased resistance against Phytophthora sojae [65]. In this study, we observed 14 ETR homologs that were expressed in the three caladium cultivars. One ETR-like unigene was expressed exclusively in the “Candidum” root transcriptome; this unigene could be a good candidate for future studies.
JA is another important hormone in plant defense against many necrotrophic pathogens [66]. It has been reported that exogenous applications of methyl jasmonate to Arabidopsis enhance plant resistance to root rot disease caused by the necrotrophic pathogen Pythium mastophorum [67]. Cohen et al. [68] found that methyl jasmonate-treated potatoes showed increased resistance to Phytophthora infestans. Studies in tomato have shown that the CORONATINE-INSENSITIVE1 gene (COI1), the acyl-CoA oxidase gene (ACX1A), the suppressor of prosystemin-mediated responses2 (SPR2), and the defenseless1 gene (def1) are involved in JA-induced defense responses against necrotrophic pathogens such as Botrytis cinerea [37]. Tomato plants with either one of these genes mutated could not correctly process JA synthesis, and JA signal perception or transduction, resulting in disrupted immune responses and susceptible plants [37]. We found that 55 unigenes were potentially involved in JA synthesis and expressed in all three caladium cultivars. One ACX1A homolog was expressed only in “Candidum” (Table 5), which suggests that this unigene may be a good candidate for further gene expression studies.

2.3.3. Secondary Metabolites with Antimicrobial Activities

Plants can secrete secondary metabolites around their rhizosphere to inhibit or repel soil-borne pathogens. In general, phenolics and terpenoids are two primary classes of root-secreted antimicrobial metabolites [69]. Common root-secreted phenolic compounds include phenylpropanoids and flavonoids [70]. Many phenylpropanoids from a broad range of plant species have shown antimicrobial activities [71]. Phenylpropanoids in chickpea roots affect resistance against the necrotrophic pathogen Fusarium oxysporum; resistant chickpea varieties produce significantly more phenylpropanoids than susceptible ones [72]. Lanoue et al. [71] found that phenylpropanoids were constitutively secreted by the barley root system and inhibited Fusarium graminearum spore germination. In “Candidum“, we found two unigenes that are likely involved in phenylpropanoid synthesis. Flavonoids are often one of the largest groups of phenolics in root exudates [69]. Studies in maize have indicated that high levels of flavonoids are associated with an increased defensive state in its roots [73]. We identified four unigenes in “Candidum” that are likely involved in flavonoid synthesis (Table 5 and Table S11).
Terpenoids are another large group of compounds in root exudates that have antimicrobial activities [74]. Some volatile terpenoids emitted from roots could act as a direct barrier against pathogens [69]. In Arabidopsis, Steeghs et al. [75] found a below-ground volatile compound (monoterpene 1,8-cineole) which had direct effects on Pseudomonas syringae. Kapulnik et al. [76] observed that strigolactones released from roots inhibited the growth of several soil-borne pathogens, including Fusarium oxysporum, Fusarium solani, and Macrophomina phaseolina. We identified four unigenes in the “Candidum” root transcriptome that are likely involved in the terpenoid synthesis pathway. Derivatives of tryptophan are another important antimicrobial root exudates. Typical examples of tryptophan derivatives include camalexin, which is produced by Arabidopsis and exhibits antimicrobial activities to a wide range of soil-borne pathogens including Pythium sylvaticum, Rhizoctonia solani, and Sclerotinia sclerotiorum [77,78]. In the “Candidum” root transcriptome, we found nine tryptophan synthesis-related unigenes (Table 5 and Table S11), which may be involved in resistance against soil-borne necrotrophic pathogens. One of these unigenes is root-specific.

2.4. Simple Sequence Repeat (SSR) and Single Nucleotide Polymorphism (SNP) Discovery

Transcriptome sequencing data have become a very important resource for discovering nucleotide sequence polymorphisms and developing molecular makers. SSRs and SNPs are two very important types of nucleotide sequence polymorphisms, and each has its own characteristics: SSRs are multi-allelic and hyper-variable, while SNPs are the most abundant type [79]. Molecular markers based on SSRs and SNPs have been widely used in plants for a wide range of research and practical purposes, including estimation of genetic diversity, determination of phylogenetic relationship, development of genetic linkage maps, identification of quantitative trait loci (QTL), association studies, marker-assisted breeding, and cultivar identification and protection [80]. In many uses, SSR and SNP-based molecular markers can complement each other.
To reveal SSRs in caladium transcriptomes, all assembled unigenes were subjected to the MISA software for screening putative SSR motifs. As a result, 28,837 SSR repeats were discovered, including 2594 mono-, 15,504 di-, 9583 tri-, 443 quad-, 346 penta-, and 367 hexa-nucleotides (Table 6). The most abundant type of SSR motifs was AG/CT (43.2%), followed by A/T (7.8%), AGG/CTT (7.5%), and AC/GT (6.1%) (Table 6). The 8988 unigenes that contain these SSRs were subjected to the Primer 3 software for primer designing, which resulted in 44,922 pairs of primers for use in caladium (Table S12).
To detect SNPs, the unigenes of “Candidum”, “Gingerland”, and “Miss Muffet” were merged to generate a set of reference sequences. SNP sites were called for “Candidum”, “Gingerland”, and “Miss Muffet” by mapping their clean reads to the set of reference sequences. As a result, 281,728, 244,685, and 270,217 SNP sites were identified for “Candidum”, “Gingerland”, and “Miss Muffet”, respectively (Table S13). The frequency of SNPs in these unigenes ranged from 1 per 487 bp in “Candidum” to 1 per 560 bp in “Gingerland”. The most abundant SNPs were of the transition-type (about 60.1%), cytosine to thymine (C ↔ T) or adenine to guanine (A ↔ G), followed by the transversion-type (A ↔ C, A ↔ T, C ↔ G, or G ↔ T) (39.9%) (Table 7 and Table S13).
Molecular markers have become a highly valuable tool in genetic improvement of plant resistance to various pathogens [81]. The main bottleneck that constrains breeding efforts to improve Pythium root rot resistance in caladium has been the low efficiency in phenotyping large breeding populations and the difficulty in obtaining reliable data for resistance to Pythium root rot. The availability of molecular DNA markers closely linked to or associated with caladium root rot resistance will empower breeders to implement highly desired marker-assisted selection in caladium. The putative disease resistance/defense genes and the large numbers of SNPs and SSRs identified in caladium root transcriptomes will serve as a very important genomic resource for developing molecular markers, linkage map construction, association studies, identification and validation of caladium QTL linked to Pythium root rot resistance. Such a marker-assisted selection strategy has been successfully implemented in many agronomic and horticultural plant species and has resulted in enhanced resistance to a wide spectrum of pathogens [82]. It is expected that application of this strategy will speed up the development and deployment of new Pythium-resistant caladium cultivars.

3. Material and Methods

3.1. Plant Material

Tubers (approximately 6 to 9 cm in diameter) of caladium cultivars “Candidum”, “Gingerland”, and “Miss Muffet” were planted individually in plastic containers (15 cm diameter) filled with coarse vermiculite (Palmetto Vermiculite Company, Woodruff, SC, USA) and irrigated with an overhead misting system (once a day). Plants sprouted from the tubers were grown in a greenhouse at the University of Florida’s Gulf Coast Research and Education Center, Wimauma, FL, USA. Temperatures inside the greenhouse were maintained between 21 °C (night) and 30 °C (day). Five grams of a controlled-release fertilizer (Osmocote®; The Scotts Miracle-Gro Company, Marysville, OH, USA) was applied to the top of the potting mix in each container to provide nutrients for the plants. Two months after the tubers were planted, roots were collected from four plants per cultivar, combined into a 50-mL centrifuge tube, and frozen with liquid nitrogen. The frozen root tissues were stored at –86 °C in a deep freezer (NuAire Corporate, Plymouth, MN, USA) until use.

3.2. RNA Extraction and Quality Control

Total RNA was isolated from the frozen root tissues using the pBiozol Total RNA Extraction Reagent (BioFlux, Hangzhou, China) following the manufacturer’s instructions. RNA concentration, RNA integrity number (RIN), and the 28S/18S ratio were determined on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) at the BGI (Beijing Genomics Institute, Shenzhen, China).

3.3. Illumina cDNA Library Preparation and Transcriptome Sequencing

Three cDNA libraries were prepared at BGI using the TruSeqTM RNA Sample Prep Kit v2 (Illumina, Inc., San Diego, CA, USA). Approximately 200 ng of total RNA for each sample were purified with oligo-dT (25) beads. The purified poly(A)-containing mRNAs were fragmented into small pieces of 120–200 bp using the Elute, Prime, and Fragment Mix reagents from Illumina TruSeq™ RNA Sample Prep Kit v2 at 94 °C for 8 min. First-strand cDNA was synthesized using the First Strand Master Mix and SuperScript II from Invitrogen (Carlsbad, CA, USA) and the following PCR program: 25 °C for 10 min, 42 °C for 50 min, and 70 °C for 15 min. Second-strand cDNA was synthesized using the Second Strand Master Mix from Invitrogen at 16 °C for 1 h. End repair was performed using the End Repair Mix from Invitrogen at 30 °C for 30 min and the Ampure XP beads (Beckman Coulter, Brea, CA, USA). The resulting cDNA fragments were adenylated using the A-Tailing Mix from Illumina at 37 °C for 30 min. Subsequently, the cDNA fragments were ligated to the RNA Index Adapter at 30 °C for 10 min using the Ligation Mix from Illumina and then purified with the Ampure XP beads (Beckman Coulter). Purified cDNA fragments were enriched with 10 cycles of PCR: 98 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s. The resulting PCR products were purified with Ampure XP beads (Beckman Coulter). The final libraries were quantitated using an Agilent 2100 Bioanalyzer (Agilent Technologies). Prepared cDNA libraries were amplified on a cBotTM system (Illumina) to generate clusters on the flow cell using the TruSeq PE Cluster Kit V4-cBot-HS (Illumina) and then subjected to the 125 bp paired-end sequencing on a HiSeq 2000 system (TruSeq SBS KIT-HS V4, Illumina) at BGI.

3.4. Raw Sequencing Data Trimming and De Novo Transcriptome Assembly

Raw sequence reads were cleaned using the filter_fq software (an internal version of BGI) to remove adaptors, reads with more than 5% unknown (N) nucleotides, and low quality reads which had more than 20% of the nucleotides scored ≤10. De novo assembly of clean reads was performed using the short reads assembling software, Trinity (version 20130225) [83], and its default parameters, with the following changes: min contig length = 100, reinforcement distance = 85, min glue = 3, group pair distance = 250, and min kmer cov = 3. The decrease of min contig length from 200 to 100 was to allow more sequences to be clustered by the TGI Clustering Tool (TGICL) (version 2.1) [84] and to be available for further analyses. Changes in reinforcement distance (from 75 to 85), min glue (from 2 to 3), and min ker cov (from 1 to 3) were made to increase the quality of resulting assemblies.
The assembled sequences (contigs) were clustered into unigenes using TGICL and the default settings, with the following changes: I (minimum overlap length) = 40, c (base quality cutoff of clipping) = 10, and v (maximum length of unmatched overhangs) = 20. TGICL-clustered contigs (>70% of sequence identity) were re-named as CLxxxx.Contigxx (x represents numeric numbers), as in CL9792.Contig11 and CL9792.Contig12. Non-clustered sequences (singleton) were labelled as Unigenexxx (as in Unigene107).

3.5. Unigenes Annotation and Coding DNA Sequence (CDS) Prediction

The assembled unigenes were annotated against the NR (release 2013_04) and NT (released 2013_04) databases, the Swiss-Prot database, the KEGG (release 2013_03) database, and the COG (release 2009_03) database using NCBI BLAST 2.2.28+ with an E-value < 10−5 (version 2.2.26). The software BLAST2GO (version 2.5.0) was used to retrieve GO annotations for unigenes annotated with the NR database. Subsequently, all unigenes annotated by GO were subjected to the software WEGO for functional classification. The software Path_finder (an internal version of BGI) was used to perform KEGG pathway annotation against the KEGG database. Unigenes failing to hit any in these databases were then subjected to the software ESTScan (version 3.0.2) for CDS prediction.

3.6. Detection of SSR and SNP Sites

MicroSatellite (MISA, http://pgrc.ipk-gatersleben.de/misa/) was used to identify SSR motifs in assembled unigenes with the following standards: mono repeats—12 units, dimer repeats—6 units, trimer repeats—5 units, tetramer repeats—5 units, pentamer repeats—5 units, and hexamer repeats—4 units. The software Primer3 (http://primer3.sourceforge.net/releases.php) was used to design primers for amplifying identified SSR sites in PCR products. To identify putative SNP sites, unigenes from all three caladium cultivars were combined to create a set of reference sequences. The SOAPsnp software (version 1.03) was employed to align unigenes of each cultivar against the combined set of reference sequences to call SNPs.

Supplementary Materials

Supplementary materials can be found at www.mdpi.com/1422-0067/18/4/712/s1.

Acknowledgments

This study was supported by the USDA (United States Department of Agriculture) hatch project FLA-GCC-005507. Authors thank BGI staff for technical assistance in sequencing, assembling and annotating caladium transcriptomes described in this paper.

Author Contributions

Zhe Cao performed the experiments, analyzed the data, and drafted the manuscript. Zhanao Deng conceived the study, reviewed data and results, and revised the manuscript. All authors read and consented to the final version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

CDSCoding DNA sequence
COGClusters of Orthologous Groups
GOGene Ontology
KEGGKyoto Encyclopedia of Genes and Genomes
NCBINational Center for Biotechnology Information
NRNon-redundant protein database (NCBI)
NTNucleotide sequence database (NCBI)
PAMPPathogen-associated molecular pattern
PTIPAMP-triggered immunity
SNPSingle nucleotide polymorphism
SSRSimple sequence repeat
USDAUnited States Department of Agriculture

References

  1. Cao, Z.; Mclaughlin, M.; Deng, Z. Interspecific genome size and chromosome number variation shed new light on species classification and evolution in caladium. J. Am. Soc. Hortic. Sci. 2014, 139, 449–459. [Google Scholar]
  2. Gong, L.; Deng, Z. Development and characterization of microsatellite markers for caladiums (Caladium Vent.). Plant Breed. 2011, 130, 591–595. [Google Scholar] [CrossRef]
  3. Deng, Z.; Goktepe, F.; Harbaugh, B.K.; Hu, J.G. Assessment of genetic diversity and relationships among caladium cultivars and species using molecular markers. J. Am. Soc. Hortic. Sci. 2007, 132, 219–229. [Google Scholar]
  4. Deng, Z.; Harbaugh, B.K.; Kelly, R.O.; Seijo, T.; McGovern, R.J. Pythium root rot resistance in caladium cultivars. HortScience 2005, 40, 549–552. [Google Scholar]
  5. Deng, Z.; Harbaugh, B.K.; Kelly, R.O.; Seijo, T.; McGovern, R.J. Screening for resistance to pythium root rot among twenty-three caladium cultivars. HortTechnology 2005, 15, 631–634. [Google Scholar]
  6. Ridings, W.H.; Hartman, R.D. Pathogenicity of Pythium myriotylum and other species of Pythium to caladium derived from shoot-tip culture. Phytopathology 1976, 66, 704–709. [Google Scholar] [CrossRef]
  7. Bruehl, G.W. Nonspecific genetic resistance to soil-borne fungi. Phytopathology 1983, 73, 948–951. [Google Scholar] [CrossRef]
  8. Goktepe, F.; Seijo, T.; Deng, Z.; Harbaugh, B.K.; Peres, N.A. Toward breeding for resistance to fusarium tuber rot in caladium: Inoculation technique and sources of resistance. HortScience 2007, 42, 1135–1139. [Google Scholar]
  9. Deng, Z.; Harbaugh, B.K.; Kelly, R.; Seijo, T.; McGovern, R.J. Evaluation of caladium cultivars for resistance to pythium root rot. HortScience 2004, 39, 772–773. [Google Scholar]
  10. Fawke, S.; Doumane, M.; Schornack, S. Oomycete interactions with plants: Infection strategies and resistance principles. Microbiol. Mol. Biol. Rev. 2015, 79, 263–280. [Google Scholar] [CrossRef] [PubMed]
  11. Sela-Buurlage, M.B.; Budai-Hadrian, O.; Pan, Q.; Carmel-Goren, L.; Vunsch, R.; Zamir, D.; Fluhr, R. Genome-wide dissection of Fusarium resistance in tomato reveals multiple complex loci. Mol. Genet. Genom. 2001, 265, 1104–1111. [Google Scholar]
  12. Komatsu, S.; Yang, G.; Hayashi, N.; Kaku, H.; Umemura, K.; Iwasaki, Y. Alterations by a defect in a rice G protein α subunit in probenazole and pathogen-induced responses. Plant Cell Environ. 2004, 27, 947–957. [Google Scholar] [CrossRef]
  13. Wang, X.L.; Jiang, N.; Liu, J.L.; Liu, W.D.; Wang, G.L. The role of effectors and host immunity in plant-necrotrophic fungal interactions. Virulence 2014, 5, 722–732. [Google Scholar] [CrossRef] [PubMed]
  14. Diener, A.C.; Ausubel, F.M. Resistance to Fusarium oxysporum 1, a dominant Arabidopsis disease-resistance gene, is not race specific. Genetics 2005, 171, 305–321. [Google Scholar] [CrossRef] [PubMed]
  15. De Vleesschauwer, D.; Xu, J.; Hofte, M. Making sense of hormone-mediated defense networking: From rice to Arabidopsis. Front. Plant Sci. 2014, 5, 611. [Google Scholar] [CrossRef] [PubMed]
  16. Chan, Y.L.; Prasad, V.; Sanjaya; Chen, K.H.; Liu, P.C.; Chan, M.T.; Chiu-Ping, C. Transgenic tomato plants expressing an Arabidopsis thionin (thi2.1) driven by fruit-inactive promoter battle against phytopathogenic attack. Planta 2005, 221, 386–393. [Google Scholar] [CrossRef] [PubMed]
  17. Ward, J.A.; Ponnala, L.; Weber, C.A. Strategies for transcriptome analysis in non-model plants. Am. J. Bot. 2012, 99, 267–276. [Google Scholar] [CrossRef] [PubMed]
  18. Xiao, M.; Zhang, Y.; Chen, X.; Lee, E.J.; Barber, C.J.S.; Chakrabarty, R.; Desgagne-Penix, I.; Haslam, T.M.; Kim, Y.B.; Liu, E.W.; et al. Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J. Biotechnol. 2013, 166, 122–134. [Google Scholar] [CrossRef] [PubMed]
  19. Zhang, H.B.; Xia, E.H.; Huang, H.; Jiang, J.J.; Liu, B.Y.; Gao, L.Z. De novo transcriptome assembly of the wild relative of tea tree (Camellia taliensis) and comparative analysis with tea transcriptome identified putative genes associated with tea quality and stress response. BMC Genom. 2015, 16, 298. [Google Scholar] [CrossRef] [PubMed]
  20. He, B.; Gu, Y.H.; Tao, X.; Cheng, X.J.; Wei, C.H.; Fu, J.; Cheng, Z.Q.; Zhang, Y.Z. De novo transcriptome sequencing of Oryza officinalis Wall ex Watt to identify disease-resistance genes. Int. J. Mol. Sci. 2015, 16, 29482–29495. [Google Scholar] [CrossRef] [PubMed]
  21. Fu, Y.Q.; Esselink, G.D.; Visser, R.G.F.; van Tuyl, J.M.; Arens, P. Transcriptome analysis of Gerbera hybrida including in silico confirmation of defense genes found. Front. Plant Sci. 2016, 7, 247. [Google Scholar] [CrossRef] [PubMed]
  22. Kim, J.E.; Oh, S.K.; Lee, J.H.; Lee, B.M.; Jo, S.H. Genome-wide SNP calling using next generation sequencing data in tomato. Mol. Cells 2014, 37, 36–42. [Google Scholar] [CrossRef] [PubMed]
  23. Guo, Y.F.; Wiegert-Rininger, K.E.; Vallejo, V.A.; Barry, C.S.; Warner, R.M. Transcriptome-enabled marker discovery and mapping of plastochron-related genes in Petunia spp. BMC Genom. 2015, 16, 726. [Google Scholar] [CrossRef] [PubMed]
  24. Zheng, X.F.; Pan, C.; Diao, Y.; You, Y.N.; Yang, C.Z.; Hu, Z.L. Development of microsatellite markers by transcriptome sequencing in two species of Amorphophallus (Araceae). BMC Genom. 2013, 14, 490. [Google Scholar] [CrossRef] [PubMed]
  25. Li, Z.; Wang, J.; Zhang, X.; Xu, L. Comparative transcriptome analysis of Anthurium “Albama” and its anthocyanin-loss mutant. PLoS ONE 2015, 10, e0119027. [Google Scholar] [CrossRef] [PubMed]
  26. Schliesky, S.; Gowik, U.; Weber, A.P.; Brautigam, A. RNA-Seq assembly—Are we there yet? Front. Plant Sci. 2012, 3, 220. [Google Scholar] [CrossRef] [PubMed]
  27. Rajkumar, H.; Ramagoni, R.K.; Anchoju, V.C.; Vankudavath, R.N.; Syed, A.U. De novo transcriptome analysis of Allium cepa L. (onion) bulb to identify allergens and epitopes. PLoS ONE 2015, 10, e0135387. [Google Scholar] [CrossRef] [PubMed]
  28. Cherukupalli, N.; Divate, M.; Mittapelli, S.R.; Khareedu, V.R.; Vudem, D.R. De novo assembly of leaf transcriptome in the medicinal plant Andrographis paniculata. Front. Plant Sci. 2016, 7, 1203. [Google Scholar] [CrossRef] [PubMed]
  29. Ma, X.; Wang, P.; Zhou, S.; Sun, Y.; Liu, N.; Li, X.; Hou, Y. De novo transcriptome sequencing and comprehensive analysis of the drought-responsive genes in the desert plant Cynanchum komarovii. BMC Genom. 2015, 16, 753. [Google Scholar] [CrossRef] [PubMed]
  30. Wang, Y.; Li, X.; Zhou, W.; Li, T.; Tian, C. De novo assembly and transcriptome characterization of spruce dwarf mistletoe Arceuthobium sichuanense uncovers gene expression profiling associated with plant development. BMC Genom. 2016, 17, 771. [Google Scholar] [CrossRef] [PubMed]
  31. Zhukov, V.A.; Zhernakov, A.I.; Kulaeva, O.A.; Ershov, N.I.; Borisov, A.Y.; Tikhonovich, I.A. De novo assembly of the pea (Pisum sativum L.) nodule transcriptome. Int. J. Genom. 2015, 2015, 695947. [Google Scholar]
  32. Xu, L.; Wang, J.; Lei, M.; Li, L.; Fu, Y.; Wang, Z.; Ao, M.; Li, Z. Transcriptome analysis of storage roots and fibrous roots of the traditional medicinal herb Callerya speciosa (Champ.) schot. PLoS ONE 2016, 11, e0160338. [Google Scholar] [CrossRef] [PubMed]
  33. Tian, S.; Gu, C.; Liu, L.; Zhu, X.; Zhao, Y.; Huang, S. Transcriptome profiling of Louisiana iris root and identification of genes involved in lead-stress response. Int. J. Mol. Sci. 2015, 16, 28087–28097. [Google Scholar] [CrossRef] [PubMed]
  34. Dodds, P.N.; Rathjen, J.P. Plant immunity: Towards an integrated view of plant-pathogen interactions. Nat. Rev. Genet. 2010, 11, 539–548. [Google Scholar] [CrossRef] [PubMed]
  35. Boller, T.; Felix, G. A renaissance of elicitors: Perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors. Annu. Rev. Plant Biol. 2009, 60, 379–406. [Google Scholar] [CrossRef] [PubMed]
  36. Morris, E.R.; Walker, J.C. Receptor-like protein kinases: The keys to response. Curr. Opin. Plant Biol. 2003, 6, 339–342. [Google Scholar] [CrossRef]
  37. Mengiste, T. Plant immunity to necrotrophs. Annu. Rev. Phytopathol. 2012, 50, 267–294. [Google Scholar] [CrossRef] [PubMed]
  38. Shiu, S.H.; Karlowski, W.M.; Pan, R.S.; Tzeng, Y.H.; Mayer, K.F.X.; Li, W.H. Comparative analysis of the receptor-like kinase family in Arabidopsis and rice. Plant Cell 2004, 16, 1220–1234. [Google Scholar] [CrossRef] [PubMed]
  39. Gish, L.A.; Clark, S.E. The RLK/Pelle family of kinases. Plant J. 2011, 66, 117–127. [Google Scholar] [CrossRef] [PubMed]
  40. Bouwmeester, K.; Govers, F. Arabidopsis l-type lectin receptor kinases: Phylogeny, classification, and expression profiles. J. Exp. Bot. 2009, 60, 4383–4396. [Google Scholar] [CrossRef] [PubMed]
  41. Bouwmeester, K.; de Sain, M.; Weide, R.; Gouget, A.; Klamer, S.; Canut, H.; Govers, F. The lectin receptor kinase LecRK-I.9 is a novel Phytophthora resistance component and a potential host target for a RXLR effector. PLoS Pathog. 2011, 7, e1001327. [Google Scholar] [CrossRef] [PubMed]
  42. Wang, Y.; Cordewener, J.H.G.; America, A.H.P.; Shan, W.X.; Bouwmeester, K.; Govers, F. Arabidopsis lectin receptor kinases LecRK-IX.1 and LecRK-IX.2 are functional analogs in regulating Phytophthora resistance and plant cell death. Mol. Plant Microbe Int. 2015, 28, 1032–1048. [Google Scholar] [CrossRef] [PubMed]
  43. Chen, K.G.; Du, L.Q.; Chen, Z.X. Sensitization of defense responses and activation of programmed cell death by a pathogen-induced receptor-like protein kinase in Arabidopsis. Plant Mol. Biol. 2003, 53, 61–74. [Google Scholar] [CrossRef] [PubMed]
  44. Bourdais, G.; Burdiak, P.; Gauthier, A.; Nitsch, L.; Salojarvi, J.; Rayapuram, C.; Idanheimo, N.; Hunter, K.; Kimura, S.; Merilo, E.; et al. Large-scale phenomics identifies primary and fine-tuning roles for CRKs in responses related to oxidative stress. PLoS Genet. 2015, 11, 7. [Google Scholar] [CrossRef] [PubMed]
  45. Yang, K.; Rong, W.; Qi, L.; Li, J.; Wei, X.; Zhang, Z. Isolation and characterization of a novel wheat cysteine-rich receptor-like kinase gene induced by Rhizoctonia cerealis. Sci. Rep. UK 2013, 3, 3021. [Google Scholar] [CrossRef] [PubMed]
  46. Cao, A.H.; Xing, L.P.; Wang, X.Y.; Yang, X.M.; Wang, W.; Sun, Y.L.; Qian, C.; Ni, J.L.; Chen, Y.P.; Liu, D.J.; et al. Serine/threonine kinase gene Stpk-V, a key member of powdery mildew resistance gene Pm21, confers powdery mildew resistance in wheat. Proc. Natl. Acad. Sci. USA 2011, 108, 7727–7732. [Google Scholar] [CrossRef] [PubMed]
  47. Sessa, G.; Martin, G.B. Signal recognition and transduction mediated by the tomato Pto kinase: A paradigm of innate immunity in plants. Microbes Infect. 2000, 2, 1591–1597. [Google Scholar] [CrossRef]
  48. Faris, J.D.; Zhang, Z.C.; Lu, H.J.; Lu, S.W.; Reddy, L.; Cloutier, S.; Fellers, J.P.; Meinhardt, S.W.; Rasmussen, J.B.; Xu, S.S.; et al. A unique wheat disease resistance-like gene governs effector-triggered susceptibility to necrotrophic pathogens. Proc. Natl. Acad. Sci. USA 2010, 107, 13544–13549. [Google Scholar] [CrossRef] [PubMed]
  49. Haffani, Y.Z.; Silva-Gagliardi, N.F.; Sewter, S.K.; Grace Aldea, M.; Zhao, Z.; Nakhamchik, A.; Cameron, R.K.; Goring, D.R. Altered expression of PERK receptor kinases in Arabidopsis leads to changes in growth and floral organ formation. Plant Signal. Behav. 2006, 1, 251–260. [Google Scholar] [CrossRef] [PubMed]
  50. Silva, N.F.; Goring, D.R. The proline-rich, extensin-like receptor kinase-1 (PERK1) gene is rapidly induced by wounding. Plant Mol. Biol. 2002, 50, 667–685. [Google Scholar] [CrossRef] [PubMed]
  51. Malinovsky, F.G.; Fangel, J.U.; Willats, W.G.T. The role of the cell wall in plant immunity. Front. Plant Sci. 2014, 5, 178. [Google Scholar] [CrossRef] [PubMed]
  52. Lecourieux, D.; Raneva, R.; Pugin, A. Calcium in plant defence-signalling pathways. New Phytol. 2006, 171, 249–269. [Google Scholar] [CrossRef] [PubMed]
  53. Kobayashi, M.; Yoshioka, M.; Asai, S.; Nomura, H.; Kuchimura, K.; Mori, H.; Doke, N.; Yoshioka, H. StCDPK5 confers resistance to late blight pathogen but increases susceptibility to early blight pathogen in potato via reactive oxygen species burst. New Phytol. 2012, 196, 223–237. [Google Scholar] [CrossRef] [PubMed]
  54. Takabatake, R.; Karita, E.; Seo, S.; Mitsuhara, I.; Kuchitsu, K.; Ohashi, Y. Pathogen-induced calmodulin isoforms in basal resistance against bacterial and fungal pathogens in tobacco. Plant Cell Physiol. 2007, 48, 414–423. [Google Scholar] [CrossRef] [PubMed]
  55. Levine, A.; Tenhaken, R.; Dixon, R.; Lamb, C. H2O2 from the oxidative burst orchestrates the plant hypersensitive disease resistance response. Cell 1994, 79, 583–593. [Google Scholar] [CrossRef]
  56. Lehmann, S.; Serrano, M.; L’Haridon, F.; Tjamos, S.E.; Metraux, J.P. Reactive oxygen species and plant resistance to fungal pathogens. Phytochemistry 2015, 112, 54–62. [Google Scholar] [CrossRef] [PubMed]
  57. Gupta, S.; Bhar, A.; Chatterjee, M.; Das, S. Fusarium oxysporum f.sp ciceri race 1 induced redox state alterations are coupled to downstream defense signaling in root tissues of chickpea (Cicer arietinum L.). PLoS ONE 2013, 8. [Google Scholar] [CrossRef] [PubMed]
  58. Kumar, V.; Parkhi, V.; Kenerley, C.M.; Rathore, K.S. Defense-related gene expression and enzyme activities in transgenic cotton plants expressing an endochitinase gene from Trichoderma virens in response to interaction with Rhizoctonia solani. Planta 2009, 230, 277–291. [Google Scholar] [CrossRef] [PubMed]
  59. Ichimura, K.; Shinozaki, K.; Tena, G.; Sheen, J.; Henry, Y.; Champion, A.; Kreis, M.; Zhang, S.Q.; Hirt, H.; Wilson, C.; et al. Mitogen-activated protein kinase cascades in plants: A new nomenclature. Trends Plant Sci. 2002, 7, 301–308. [Google Scholar]
  60. Chisholm, S.T.; Coaker, G.; Day, B.; Staskawicz, B.J. Host-microbe interactions: Shaping the evolution of the plant immune response. Cell 2006, 124, 803–814. [Google Scholar] [CrossRef] [PubMed]
  61. Kong, Q.; Qu, N.; Gao, M.; Zhang, Z.; Ding, X.; Yang, F.; Li, Y.; Dong, O.X.; Chen, S.; Li, X.; et al. The MEKK1-MKK1/MKK2-MPK4 kinase cascade negatively regulates immunity mediated by a mitogen-activated protein kinase kinase kinase in Arabidopsis. Plant Cell 2012, 24, 2225–2236. [Google Scholar] [CrossRef] [PubMed]
  62. Wang, K.L.C.; Li, H.; Ecker, J.R. Ethylene biosynthesis and signaling networks. Plant Cell 2002, 14, S131–S151. [Google Scholar] [PubMed]
  63. Cho, Y.H.; Yoo, S.D. Novel connections and gaps in ethylene signaling from the ER membrane to the nucleus. Front. Plant Sci. 2015, 5, 733. [Google Scholar] [CrossRef] [PubMed]
  64. Thomma, B.P.H.J.; Eggermont, K.; Tierens, K.F.M.J.; Broekaert, W.F. Requirement of functional ethylene-insensitive 2 gene for efficient resistance of Arabidopsis to infection by Botrytis cinerea. Plant Physiol. 1999, 121, 1093–1101. [Google Scholar] [CrossRef] [PubMed]
  65. Hoffman, T.; Schmidt, J.S.; Zheng, X.Y.; Bent, A.F. Isolation of ethylene-insensitive soybean mutants that are altered in pathogen susceptibility and gene-for-gene disease resistance. Plant Physiol. 1999, 119, 935–949. [Google Scholar] [CrossRef] [PubMed]
  66. AbuQamar, S.; Chai, M.F.; Luo, H.L.; Song, F.M.; Mengiste, T. Tomato protein kinase 1b mediates signaling of plant responses to necrotrophic fungi and insect herbivory. Plant Cell 2008, 20, 1964–1983. [Google Scholar] [CrossRef] [PubMed]
  67. Vijayan, P.; Shockey, J.; Levesque, C.A.; Cook, R.J.; Browse, J. A role for jasmonate in pathogen defense of Arabidopsis. Proc. Natl. Acad. Sci. USA 1998, 95, 7209–7214. [Google Scholar] [CrossRef] [PubMed]
  68. Cohen, Y.; Gisi, U.; Niderman, T. Local and systemic protection against Phytophthora infestans induced in potato and tomato plants by jasmonic acid and jasmonic methyl-ester. Phytopathology 1993, 83, 1054–1062. [Google Scholar] [CrossRef]
  69. Baetz, U.; Martinoia, E. Root exudates: The hidden part of plant defense. Trends Plant Sci. 2014, 19, 90–98. [Google Scholar] [CrossRef] [PubMed]
  70. Harborne, J.B.; Williams, C.A. Advances in flavonoid research since 1992. Phytochemistry 2000, 55, 481–504. [Google Scholar] [CrossRef]
  71. Lanoue, A.; Burlat, V.; Henkes, G.J.; Koch, I.; Schurr, U.; Rose, U.S.R. De novo biosynthesis of defense root exudates in response to Fusarium attack in barley. New Phytol. 2010, 185, 577–588. [Google Scholar] [CrossRef] [PubMed]
  72. Arfaoui, A.; El Hadrami, A.; Mabrouk, Y.; Sifi, B.; Boudabous, A.; El Hadrami, I.; Daayf, F.; Cherif, M. Treatment of chickpea with Rhizobium isolates enhances the expression of phenylpropanoid defense-related genes in response to infection by Fusarium oxysporum F. sp. ciceris. Plant Physiol. Biochem. 2007, 45, 470–479. [Google Scholar] [CrossRef] [PubMed]
  73. Balmer, D.; de Papajewski, D.V.; Planchamp, C.; Glauser, G.; Mauch-Mani, B. Induced resistance in maize is based on organ-specific defence responses. Plant J. 2013, 74, 213–225. [Google Scholar] [CrossRef] [PubMed]
  74. Vaughan, M.M.; Wang, Q.; Webster, F.X.; Kiemle, D.; Hong, Y.J.; Tantillo, D.J.; Coates, R.M.; Wray, A.T.; Askew, W.; O’Donnell, C.; et al. Formation of the unusual semivolatile diterpene rhizathalene by the Arabidopsis class I terpene synthase TPS08 in the root stele is involved in defense against belowground herbivory. Plant Cell 2013, 25, 1108–1125. [Google Scholar] [CrossRef] [PubMed]
  75. Steeghs, M.; Bais, H.P.; de Gouw, J.; Goldan, P.; Kuster, W.; Northway, M.; Fall, R.; Vivanco, J.M. Proton-transfer-reaction mass spectrometry as a new tool for real time analysis of root-secreted volatile organic compounds in Arabidopsis. Plant Physiol. 2004, 135, 47–58. [Google Scholar] [CrossRef] [PubMed]
  76. Kapulnik, Y.; Delaux, P.M.; Resnick, N.; Mayzlish-Gati, E.; Wininger, S.; Bhattacharya, C.; Sejalon-Delmas, N.; Combier, J.P.; Becard, G.; Belausov, E.; et al. Strigolactones affect lateral root formation and root-hair elongation in Arabidopsis. Planta 2011, 233, 209–216. [Google Scholar] [CrossRef] [PubMed]
  77. Stotz, H.U.; Sawada, Y.; Shimada, Y.; Hirai, M.Y.; Sasaki, E.; Krischke, M.; Brown, P.D.; Saito, K.; Kamiya, Y. Role of camalexin, indole glucosinolates, and side chain modification of glucosinolate-derived isothiocyanates in defense of Arabidopsis against Sclerotinia sclerotiorum. Plant J. 2011, 67, 81–93. [Google Scholar] [CrossRef] [PubMed]
  78. Millet, Y.A.; Danna, C.H.; Clay, N.K.; Songnuan, W.; Simon, M.D.; Werck-Reichhart, D.; Ausubel, F.M. Innate immune responses activated in Arabidopsis roots by microbe-associated molecular patterns. Plant Cell 2010, 22, 973–990. [Google Scholar] [CrossRef] [PubMed]
  79. Ching, A.; Caldwell, K.S.; Jung, M.; Dolan, M.; Smith, O.S.; Tingey, S.; Morgante, M.; Rafalski, A.J. SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet. 2002, 3, 19. [Google Scholar] [CrossRef]
  80. Agarwal, M.; Shrivastava, N.; Padh, H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 2008, 27, 617–631. [Google Scholar] [CrossRef] [PubMed]
  81. Vleeshouwers, V.; Oliver, R. Effectors as tools in disease resistance breeding against biotrophic, hemibiotrophic, and necrotrophic plant pathogens. Mol. Plant Microbe Interact. 2014, 27, 196–206. [Google Scholar] [CrossRef] [PubMed]
  82. Ashkani, S.; Rafii, M.Y.; Shabanimofrad, M.; Miah, G.; Sahebi, M.; Azizi, P.; Tanweer, F.A.; Akhtar, M.S.; Nasehi, A. Molecular breeding strategy and challenges towards improvement of blast disease resistance in rice crop. Front. Plant Sci. 2015, 6, 886. [Google Scholar] [CrossRef] [PubMed]
  83. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [PubMed]
  84. Pertea, G.; Huang, X.; Liang, F.; Antonescu, V.; Sultana, R.; Karamycheva, S.; Lee, Y.; White, J.; Cheung, F.; Parvizi, B.; et al. TIGR gene indices clustering tools (TGICL): A software system for fast clustering of large est datasets. Bioinformatics 2003, 19, 651–652. [Google Scholar] [CrossRef] [PubMed]
Ijms 18 00712 g001 550
Figure 1. Characterization of caladium unigenes via similarity searches against the NR database. (A) E-value distribution of BLAST hits for the assembled unigenes; (B) similarity distribution of BLAST hits for the assembled unigenes; and (C) species distribution of the top BLAST hits for the assembled unigenes.
Figure 1. Characterization of caladium unigenes via similarity searches against the NR database. (A) E-value distribution of BLAST hits for the assembled unigenes; (B) similarity distribution of BLAST hits for the assembled unigenes; and (C) species distribution of the top BLAST hits for the assembled unigenes.
Ijms 18 00712 g001
Ijms 18 00712 g002 550
Figure 2. Gene ontology (GO) classification of annotated caladium unigenes. Results were grouped into three main categories: Biological Process, Cellular Component, and Molecular Function. The right y-axis showed the number of unigenes corresponding to each subcategory, and the left y-axis showed the percentage of unigenes involved in each specific subcategory.
Figure 2. Gene ontology (GO) classification of annotated caladium unigenes. Results were grouped into three main categories: Biological Process, Cellular Component, and Molecular Function. The right y-axis showed the number of unigenes corresponding to each subcategory, and the left y-axis showed the percentage of unigenes involved in each specific subcategory.
Ijms 18 00712 g002
Ijms 18 00712 g003 550
Figure 3. Root-specific unigenes identified in the three caladium root transcriptomes. The y-axis on the right indicates the functional annotation categories, and the x-axis on the bottom shows the number of unigenes falling into each functional annotation category.
Figure 3. Root-specific unigenes identified in the three caladium root transcriptomes. The y-axis on the right indicates the functional annotation categories, and the x-axis on the bottom shows the number of unigenes falling into each functional annotation category.
Ijms 18 00712 g003
Ijms 18 00712 g004 550
Figure 4. Clusters of Orthologous Groups (COG) classification of assembled caladium unigenes. The COG function classification of these unigenes fell into 26 categories (x-axis). The y-axis showed the number of caladium unigenes corresponding to each category.
Figure 4. Clusters of Orthologous Groups (COG) classification of assembled caladium unigenes. The COG function classification of these unigenes fell into 26 categories (x-axis). The y-axis showed the number of caladium unigenes corresponding to each category.
Ijms 18 00712 g004
Ijms 18 00712 g005 550
Figure 5. Length distribution of CDS predicted in the NR, Swiss-prot, KEGG and COG databases (A), or by the ESTscan (B).
Figure 5. Length distribution of CDS predicted in the NR, Swiss-prot, KEGG and COG databases (A), or by the ESTscan (B).
Ijms 18 00712 g005
Ijms 18 00712 g006 550
Figure 6. Venn diagram showing the number of specific and common unigenes in three caladium cultivars, “Candidum”, “Gingerland”, and “Miss Muffet”.
Figure 6. Venn diagram showing the number of specific and common unigenes in three caladium cultivars, “Candidum”, “Gingerland”, and “Miss Muffet”.
Ijms 18 00712 g006
Table
Table 1. Summary of the HiSeq sequencing data of three caladium root transcriptomes.
Table 1. Summary of the HiSeq sequencing data of three caladium root transcriptomes.
CultivarsRaw ReadsClean ReadsTotal Clean Nucleotides (nt)Q20 Percentage (%)N Percentage (%)GC Percentage (%)
“Candidum”68,000,07861,439,6826,143,968,20096.840.0149.20
“Gingerland”50,980,47446,148,4924,614,849,20096.970.0149.47
“Miss Muffet”67,583,93661,034,2846,103,428,40096.920.0149.84
Table
Table 2. Summary of de novo assembly of caladium root transcriptomes.
Table 2. Summary of de novo assembly of caladium root transcriptomes.
SampleTotal NumberTotal Length (nt)Mean Length (nt)N50 Length (nt)Total Consensus SequencesDistinct ClustersDistinct Singletons
Contig“Candidum”255,21574,985,719294471---
“Gingerland”232,33369,427,118299492---
“Miss Muffet”258,11675,467,248292463---
Unigene“Candidum”133,73797,471,0527291525133,73746,96286,775
“Gingerland”122,99490,548,7007361514122,99443,72179,273
“Miss Muffet”135,58997,184,0277171487135,58947,84487,745
All137,354137,253,9569991755137,35462,35375,001
Table
Table 3. Summary of caladium unigenes annotated in different databases.
Table 3. Summary of caladium unigenes annotated in different databases.
DatabasesNumber of Caladium Unigenes with Hits in DatabasesPercentage of All Unigenes (%)
NR68,82750.11
NT52,23338.03
Swiss-Prot47,90734.88
KEGG46,40633.79
COG31,41722.87
GO48,41735.25
All71,82552.29
Table
Table 4. The top 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the assembled caladium unigenes.
Table 4. The top 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the assembled caladium unigenes.
RankPathwayCount (46,406)Pathway IDFunction
1Metabolic pathways12,474ko01100Metabolism
2RNA transport5390ko03013Genetic Information Processing
3Biosynthesis of secondary metabolites4780ko01110Metabolism
4mRNA surveillance pathway4198ko03015Genetic Information Processing
5Glycerophospholipid metabolism3613ko00564Metabolism
6Endocytosis3563ko04144Cellular Processes
7Ether lipid metabolism3169ko00565Metabolism
8Plant-pathogen interaction2415ko04626Organismal Systems
9Spliceosome2024ko03040Genetic Information Processing
10RNA degradation1823ko03018Genetic Information Processing
Table
Table 5. Summary of 98 putative disease resistance-related unigenes observed only in Pythium-resistant “Candidum”.
Table 5. Summary of 98 putative disease resistance-related unigenes observed only in Pythium-resistant “Candidum”.
Functional PathwayPutative AnnotationNumber of UnigenesRoot-Specific
Signal perceptionl-type lection receptor kinase (LecRK)1No
Cysteine-rich receptor-like kinase (CRK)1No
Receptor-like serine/threonine-protein kinases (STKs)12No
Proline-rich receptor-like protein kinases (PERKs)31No
Receptor-like protein kinases (RLKs)5No
Signal transductionCalcium-dependent protein kinases (CPKs)8No
Calmodulins (CaM) or calmodulins-like (CaM-like)4No
Superoxide dismutase5No
Mitogen-activated protein kinases (MAPKs)8No
Hormone regulationEthylene receptors1No
S-adenosylmethionine (S-AdoMet)1No
1-aminocyclopropane-1-carboxylic acid (ACC)1No
Acyl-CoA oxidase (ACX1A)1No
Antimicrobial compoundsPhenylpropanoid biosythetic process2No
Flavonoid1No
Flavonoid 31No
Flavonoid 6-hydroxylase1No
Flavonoid 3-monooxygenase1No
Terpene synthase4No
Tryptophan biosynthetic process1No
Tryptophan 5-monooxygenase5No
Tryptophan catabolic process1Yes
Tryptophan 5-monooxygenase1No
Tryptophan biosynthetic process1No
Table
Table 6. Summary of simple sequence repeats (SSRs) identified in caladium root transcriptome sequences.
Table 6. Summary of simple sequence repeats (SSRs) identified in caladium root transcriptome sequences.
Number of RepeatsMono-Nucleotide RepeatsDi-Nucleotide RepeatsTri-Nucleotide RepeatsQuad-Nucleotide RepeatPenta-Nucleotide RepeatsHexa-Nucleotide Repeats
40000293362
5005183356534
60348928578701
7026591431000
802879102000
9035017000
10023971000
1105532000
121086180000
1354340000
1433640000
1517800000
1610900000
176600000
185200000
193700000
204100000
215400000
223800000
235300000
24100000
SubTotal259415,5049583443346367
Table
Table 7. Summary of single nucleotide polymorphism (SNP) types detected in caladium root transcriptomes.
Table 7. Summary of single nucleotide polymorphism (SNP) types detected in caladium root transcriptomes.
SNP Type“Candidum”“Gingerland”“Miss Muffet”
Transition169,687147,579162,919
A (adenine) ↔ G (guanine)85,66674,53882,157
C (cytosine) ↔ T (thymine)84,02173,04180,762
Transversion112,04197,106107,298
A (adenine) ↔ C (cytosine)27,27423,70525,952
A (adenine) ↔ T (thymine)23,24619,71721,984
C (cytosine) ↔ G (guanine)34,44630,37233,440
G (guanine) ↔ T (thymine)27,07523,31225,922
Total281,728244,685270,217

Share and Cite

MDPI and ACS Style

Cao, Z.; Deng, Z. De Novo Assembly, Annotation, and Characterization of Root Transcriptomes of Three Caladium Cultivars with a Focus on Necrotrophic Pathogen Resistance/Defense-Related Genes. Int. J. Mol. Sci. 2017, 18, 712. https://doi.org/10.3390/ijms18040712

AMA Style

Cao Z, Deng Z. De Novo Assembly, Annotation, and Characterization of Root Transcriptomes of Three Caladium Cultivars with a Focus on Necrotrophic Pathogen Resistance/Defense-Related Genes. International Journal of Molecular Sciences. 2017; 18(4):712. https://doi.org/10.3390/ijms18040712

Chicago/Turabian Style

Cao, Zhe, and Zhanao Deng. 2017. "De Novo Assembly, Annotation, and Characterization of Root Transcriptomes of Three Caladium Cultivars with a Focus on Necrotrophic Pathogen Resistance/Defense-Related Genes" International Journal of Molecular Sciences 18, no. 4: 712. https://doi.org/10.3390/ijms18040712

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop