Sequencing and characterization of leaf transcriptomes of six diploid Nicotiana species
© Long et al. 2016
Received: 9 November 2015
Accepted: 5 April 2016
Published: 18 April 2016
Nicotiana belongs to the Solanaceae family that includes important crops such as tomato, potato, eggplant, and pepper. Nicotiana species are of worldwide economic importance and are important model plants for scientific research. Here we present the comparative analysis of the transcriptomes of six wild diploid Nicotiana species. Wild relatives provide an excellent study system for the analysis of the genetic basis for various traits, especially disease resistance.
Whole transcriptome sequencing (RNA-seq) was performed for leaves of six diploid Nicotiana species, i.e. Nicotiana glauca, Nicotiana noctiflora, Nicotiana cordifolia, Nicotiana knightiana, Nicotiana setchellii and Nicotiana tomentosiformis. For each species, 9.0–22.3 Gb high-quality clean data were generated, and 67,073–182,046 transcripts were assembled with lengths greater than 100 bp. Over 90 % of the ORFs in each species had significant similarity with proteins in the NCBI non-redundant protein sequence (NR) database. A total of 2491 homologs were identified and used to construct a phylogenetic tree from the respective transcriptomes in Nicotiana. Bioinformatic analysis identified resistance gene analogs, major transcription factor families, and alkaloid transporter genes linked to plant defense.
This is the first report on the leaf transcriptomes of six wild Nicotiana species by Illumina paired-end sequencing and de novo assembly without a reference genome. These sequence resources hopefully will provide an opportunity for identifying genes involved in plant defense and several important quality traits in wild Nicotiana and will accelerate functional genomic studies and genetic improvement efforts of Nicotiana or other important Solanaceae crops in the future.
KeywordsNicotiana Transcriptome De novo assembly Phylogenetic relationship Nicotiana setchellii Nicotiana cordifolia Nicotiana knightiana Nicotiana tomentosiformis Nicotiana noctiflora Nicotiana glauca
The genus Nicotiana is a member of the Solanaceae or nightshade family, which includes many economically important crop plants such as tomato, potato, eggplant, and pepper. According to Goodspeed  and Goodspeed & Thompson , Nicotiana was initially divided into three subgenera and 14 sections. Recently, this genus was reclassified into 13 sections based on morphological, cytological, and DNA sequence data [3, 4]. Nicotiana includes over 75 naturally occurring species, almost half of which are allopolyploid . The genus Nicotiana contains species of scientific and economic importance, with different evolutionary histories resulting to highly complex genomes . Of all species, only Nicotiana tabacum (common tobacco) and Nicotiana rustica are cultivated worldwide, whereas the others are wild species. Moreover, Nicotiana benthamiana is used extensively as a model to study plant-pathogen interactions. Several other species, such as Nicotiana alata and Nicotiana sylvestris, are grown as ornamentals. In N. tabacum breeding programs, wild Nicotiana species are valuable sources for identifying genes involved in disease and pest resistance, important quality traits, and phytochemicals, which are not present in cultivated varieties .
Plants are constantly under the attack of bacteria, fungi, viruses, nematodes and insect pests. Some of them have successfully invaded crop plants, causing diseases and reducing crop quality and yield. To protect against pathogens, plants have evolved various defense mechanisms. Plant disease resistance (R) genes play a key role in defending plants from a range of pathogens. For instance, N genes from tobacco confer resistance to tobacco mosaic virus (TMV) . In recent years, a set of 112 known and 104,310 putative R genes fighting against 122 different pathogens have been identified in 233 plant species . Most of the characterized R genes share a few highly conserved domains, including nucleotide binding site (NBS), leucine-rich repeat (LRR), Toll/Interleukin-1 receptor (TIR) and coiled-coil (CC) domains [9–11]. These conservative domains provide convenient and reliable means for rapidly identifying and cloning R genes or resistance gene analogs (RGAs).
Identification of Nicotiana R genes and RGAs cannot only help elucidate the molecular mechanisms of host-pathogen interaction, but also benefit breeding programs for disease resistance in Nicotiana and other important Solanaceae crops. Transcriptomic sequences can be useful substitutes for gene discovery in species without sequenced genomes. In the past, a large RGA pool has been mined from transcriptomic sequences and expressed sequence tags (ESTs) of coffee , Phaseolus vulgaris , Curcuma longa  and Cocos nucifera . Wild Nicotiana species are known to resist a variety of pathogens. For example, N. glauca has attractive potentials to resist black root rot (BRR), potato virus Y (PVY), tobacco etch virus (TEV), anthracnose (An), powdery mildew (PM), rattle virus (RV) and tobacco streak virus (TS) [16–18]. Nicotiana noctiflora is resistant to PM and PVY. Nicotiana cordifolia shows resistance to TS. Nicotiana knightiana manifests high resistance to An, PM, root knot nematodes (RK), PVY and TEV. Nicotiana setchellii shows resistance to RV and TEV. Nicotiana tomentosiformis is resistant to cyst nematodes (CN), RK, RV and TEV [16, 17]. These observations suggest that wild Nicotiana species are excellent depositories of R genes and RGAs, but relevant analyses of these genes have been lacking.
In Nicotiana species, alkaloids (e.g. nicotine) are believed to function as a chemical defense mechanism against pathogens and herbivores. Nicotine and related pyridine alkaloids are synthesized in the tobacco root and then translocated to the aerial parts of the plant [19, 20]. Thus the translocation of nicotine from the root to the leaves is very important in tobacco defenses.
Comparative studies of closely related species can advance our understanding of the genetic architecture of adaptive traits. So far, such studies have been very limited for several crops including tobacco. This is mainly due to the lack of genomic resources hampering the development of genetic markers for investigating species divergence, adaptation and demographic processes in natural populations.
Summary of the six wild Nicotiana species investigated in this study
Resistance to diseases
BRR, An, PM, RV, TEV, TS, PVY
An, PM, RK, TEV, PVY
CN, RK, RV, TEV
Results and discussion
Assembly of RNA-seq reads and evaluation
The Illumina paired-end sequencing yielded 100 bp paired-end independent reads from each insert of cDNA. After stringent quality assessment and data filtering, reads with Q20 bases (those with a base quality greater than 20) were selected as high quality reads for further analysis. In this study, 9.0–22.3 Gb of clean data were generated for each sample (Additional file 1). Due to the lack of reference genome information, Trinity was used for de novo assembly of the six wild Nicotiana species . We ultimately obtained 182,046, 146,188, 134,519, 67,073, 102,935 and 117,640 transcripts with length >100 bp for N. glauca, N. noctiflora, N. cordifolia, N. knightiana, N. setchellii and N. tomentosiformis, respectively (Additional file 1). Subsequently, open reading frames (ORFs) were predicted and the transcripts were translated into peptides culled at a minimum length of 100 amino acids. Only ORFs longer than 300 bp were considered to be possible protein-encoding transcripts and 33,995–79,449 ORFs were obtained through this process for the studied species (see Additional file 2). Although the ORFs of the six wild Nicotiana species varied within a large range, from 33,995 to 79,449, after removing redundancy due to alternative splicing isoforms, the ORFs ranged from 22,168 to 29,356 (N. glauca 22,934, N. noctiflora 26,788, N. cordifolia 29,356, N. knightiana 22,168, N. setchellii 26,579 and N. tomentosiformis 24,213).
In the absence of a reference genome, evaluating the quality of the de novo assembled transcriptomes becomes a tedious job. To resolve it, we marked N. tomentosiformis as a reference. A total of 53,753 reported peptide sequences (ftp://solgenomics.net/genomes/Nicotiana_tomentosiformis/annotation/, Accessed 27th Apr 2015) were blasted  against our predicted ORFs of N. tomentosiformis using BLASTp with a cut-off e-value of 10−5. A total of 50,390 (93.74 %) N. tomentosiformis proteins had a BLAST hit in our ORFs and 32,761 (60.95 %) proteins showed ≥90 % identity with more than 50 % matched length of the corresponding proteins, which suggests our assembly should be largely complete. Moreover, ORFs were compared to the core eukaryote gene (CEG) set of 248 proteins from six reference species  to assess the quality of each transcriptome. The CEGs were well-represented in the assembled transcriptomes of the N. glauca, N. noctiflora, N. cordifolia, N. knightiana, N. setchellii, N. tomentosiformis, with significant matches (alignment length ≥50 % CEG length and e-value <10−5) to 87.10, 92.34, 91.94, 89.92, 90.73 and 91.53 % of the CEGs, respectively. This indicated that the quality and completeness of our transcriptome assemblies were high enough for subsequent analyses. These transcriptome sequences may greatly enrich the Nicotiana sequence database, and will be useful in trait-related gene mining, such as the identification of plant defense genes.
Transcriptome annotation and expression analysis
Summary of functional annotation of predicted ORFs
Distribution of ORF expressions in six wild Nicotiana species
31,857 (48.69 %)
28,985 (45.34 %)
33,698 (42.41 %)
6245 (18.37 %)
16,346 (30.89 %)
18,670 (35.15 %)
11,832 (18.09 %)
11,105 (17.37 %)
16,828 (21.18 %)
2936 (8.63 %)
11,577 (21.88 %)
11,338 (21.34 %)
11,696 (17.88 %)
12,299 (19.24 %)
16,812 (21.16 %)
14,986 (44.08 %)
13,912 (26.29 %)
12,948 (24.37 %)
6710 (10.26 %)
7708 (12.06 %)
8232 (10.36 %)
6782 (19.95 %)
7495 (14.16 %)
6836 (12.87 %)
3328 (5.09 %)
3833 (5.10 %)
3879 (4.88 %)
3046 (8.96 %)
3586 (6.78 %)
3329 (6.27 %)
Functional classification by KEGG
Functional classification by GO
Identification of NBS encoding genes and defense response associated transcription factors
The majority of disease resistance genes in plants contain a nucleotide-binding site and leucine-rich repeat (NBS-LRR) domain [36, 37], which confers resistance to fungi, bacteria, viruses, and nematodes. In plants, based on the presence or absence of a TIR homology region at the N-terminus, the NBS-LRR genes can be subdivided into two main groups: TIR-NBS-LRR and non-TIR-NBS-LRR. The latter may have a coiled-coil (CC) motif in the N-terminal region and can be called as CC-NBS-LRR.
To control diseases in certain agriculturally important plants, the identification of resistance genes from their less susceptible relatives has been the top priority in crop breeding programs. In the case of Solanaceae species, the pepper Bs2 gene with NBS-LRR domain was introduced into tomato lines to develop resistance against bacterial spot disease . In tobacco, the TIR-NBS-LRR encoding N gene was introduced into N. benthamiana, which resulted in the acquirement of hypersensitivity response to tobacco mosaic virus (TMV) .
Classification of NBS encoding genes based on the predicted domains from six wild Nicotiana transcriptomes
Summary of ten transcription factors involved in plant defense in six wild Nicotiana transcriptomes
Identification of alkaloid transporter genes
Alkaloids are mainly produced in the root and then translocated via xylem transport towards the aerial parts. These toxic chemicals function as part of the chemical defense against invaders [19, 20]. To date, the plant alkaloid transporters are mainly characterized into the ATP-binding cassette (ABC) protein, multidrug and toxic compound extrusion (MATE), and purine permease (PUP) families. Some transporters were found to be required for the efficient biosynthesis of alkaloids in plants . In tobacco, several alkaloid transporter genes have been identified, such as tobacco jasmonate-inducible alkaloid tranporter1 (Nt-JAT1), Nt-JAT2, tobacco nicotine uptake permease1 (Nt-NUP1), NtMATE1 and NtMATE2 [46–50].
In the present study, we began our investigation by searching the assembled transcriptome for orthologous genes to known alkaloid transporter genes in the tobacco. Nt-JAT1 transports nicotine and other alkaloids in a proton gradient-dependent manner. Nt-JAT1 mRNA is expressed in leaves, stems, and roots. In leaf cells, Nt-JAT1 localizes to the tonoplast and might play a role in the vacuolar sequestration of nicotine . We found one orthologous gene for Nt-JAT1 in the assembled N. cordifolia, N. setchellii and N. tomentosiformis transcriptomes with high confidence, respectively. For Nt-JAT2 and NtNUP1 genes, we found orthologous genes in N. noctiflora, N. cordifolia, N. knightiana, N. setchellii and N. tomentosiformis. According to previous reports, Nt-JAT2 is specifically expressed in leaves. Nt-JAT2 contributes to the transportation of nicotine into the vacuole of leaves . Nt-NUP1 is a plasma membrane-localized nicotine transporter of the PUP family. It is involved in the movement of apoplastic nicotine into the cytoplasm of tobacco root cells, which affects nicotine metabolism and root growth. NUP1 transcripts are less abundant in the leaves, but are abundant in root tips where nicotine is actively synthesized . Two homologous MATE transporters, NtMATE1 and NtMATE2, were reported to be responsible for the vacuolar accumulation of nicotine in the root. We found one orthologous gene corresponding to the tobacco MATE1/2 genes in the leaf of N. noctiflora. This gene may have a different function.
We did not identify any orthologous genes for Nt-JAT2 and Nt-NUP1 in N. glauca. Since N. glauca can grow to a tree of several meters tall, it is possible that the acropetal transport of defensive alkaloid is relatively inefficient.
It is well-known that genetic diversity is essential for the continuous genetic modification and improvement of cultivated crops, as well as for many basic studies in plant biology. As an economic crop, cultivated tobacco has received a fair number of desired genes from wild Nicotiana relatives [51–57]. Although wild Nicotiana species have played important roles in many research areas of plant biology, their genomic resources have been slowly developed relative to most other major crop species. With this study, we provide the reference transcriptome sequences of six wild Nicotiana species for public use. By constructing phylogenetic trees, we confirmed the classification of Nicotiana into three sections and the placement of these wild species in each section. Our study will provide a better understanding of the genomic architecture of wild Nicotiana and help elucidate genes involved in plant defense. It is likely that these Nicotiana species will be used as model systems for investigating many aspects of general plant biology in future. These sequences will be an important resource for evolutionary and developmental genetics in the genus Nicotiana and will contribute significantly to the improvement of cultivated tobacco and other important Solanaceae crops.
Plant materials and RNA extraction
Six wild species of Nicotiana, including N. setchellii, N. cordifolia, N. knightiana, N. tomentosiformis, N. noctiflora, and N. glauca, were grown in a greenhouse in Guizhou Province under the same cultivation conditions. Fresh leaves from 30-day-old flowerless plants were collected, snap frozen in liquid nitrogen, and stored at −70 °C. RNA was purified using TRIzol (Invitrogen, CA, USA) from the frozen materials according to the manufacturer’s instructions. RNA degradation and contamination was monitored on 1 % agarose gels. RNA integrity was confirmed using the 2100 Bioanalyzer (Agilent Technologies) with a minimum RNA integrated number value of 8 after checking the RNA purity and concentration.
Library preparation and Illumina sequencing
RNA sequencing libraries were constructed in parallel from the six species using TruSeq RNA Sample Prep Kits (Illumina, SanDiego, USA). Briefly, first strand cDNA synthesis was performed with oligo-dT primer and Superscript II reverse transcriptase (Invitrogen, CA, USA). The second strand was synthesized with Escherichia coli DNA Pol I (Invitrogen, CA, USA). Double-stranded cDNA was purified with a Qiaquick PCR purification kit (Qiagen), and sheared with a nebulizer (Invitrogen, CA, USA) into 200–250 bp fragments. After the end repair and addition of a 3′-dA overhang, the cDNA was ligated to Illumina PE adapter oligo mix (Illumina). The products were then purified and enriched with PCR to create the final sequencing cDNA library. Both ends of the library were sequenced on the Illumina HiSeq 2000 platform.
De novo transcriptome assembly
Before performing the assembly, raw reads (FASTQ format) were cleaned by removing reads containing adaptor sequences, reads containing poly-N, and low-quality reads. For each species, de novo transcriptome assembly was performed using Trinity  (version: trinityrnaseq_r2012-06-08) with default settings except min_kmer_cov set to 2, which is a method for the efficient and robust de novo reconstruction of transcriptomes. Afterwards, transcripts with length <200 bp in each species were removed. The protein-coding region prediction program in the Trinity software suite (transcripts_to_best_scoring_ORFs.pl) was used to identify putative open reading frames (ORFs) consisting of at least 100 amino acids on the basis of nucleotide composition.
Functional annotation and expression level analysis
Peptide annotation for the ORFs obtained in this study was performed by BLASTp (version 2.2.29 +)  searching in the NCBI NR database (24th June 2014) and Swiss-Prot database (28th April 2015). An e-value cutoff of 10−5 was used and only one best hit was retained for each sequence query. To assign preliminary GO terms to the ORFs, InterProScan (version 5.4)  was used to screen the annotated peptide sequences against all the default databases. GO classification of the ORFs was conducted based on biological processes, molecular function, and cellular component and subsequently was visualized by the WEGO online tool . Pathway assignments were carried out based on the KEGG database (8th March 2011) . The ORFs from each species were first compared with KEGG database using BLASTp (version 2.2.29 +) with an e-value less than 10−5. An in-house Perl script (https://github.com/NiLong/kegg_stat/) was developed to retrieve KO (KEGG Orthology) information from BLASTp results and correlation between peptides and database pathway was established. The RSEM software (version 1.2.13)  was used to quantify the expression level of the ORFs of six wild Nicotiana species measured as FPKM values.
Gene family and phylogenetic analysis
The initial set of annotation contained a high level of redundancy as more than half of the annotated transcripts were alternative splicing isoforms . To avoid this redundancy in subsequent analyses, ORFs from six Nicotiana species were clustered using cd-hit-est command in CD-HIT v4.6.1-2012-08-27  with >95 % similarity cutoff and only the representative ORFs in each cluster were retained. This yielded non-redundant sequence datasets for N. glauca (22,934 genes), N. noctiflora (26,788 genes), N. cordifolia (29,356 genes), N. knightiana (22,168 genes), N. setchellii (26,579 genes) and N. tomentosiformis (24,213 genes). These non-redundant sequences were defined as unigenes. Similarly, tomato coding sequences (CDSs) obtained from ITAG2.4 were also clustered and 33,721 genes were obtained. OrthoMCL v1.4  was used to identify ortholog relationships between the six wild Nicotiana species and tomato.
Phylogenetic trees were constructed using sequences of 2491 single copy orthologs from six Nicotiana species with S. lycopersicum (tomato) as an outgroup. Multiple sequence alignments were performed by MUSCLE v3.8.31 . Two methods were used to reconstruct phylogenetic trees: (1) the neighbor-joining method in Phylip v3.696  and bootstrapped with 1000 replicates, and (2) the maximum likelihood method in PhyML v3.0 .
Identification of NBS containing genes and transcription factors related to disease resistance
For the identification of the NBS-encoding genes in this study, we followed the method described for diploid cotton Gossypium raimondii . Firstly, the protein sequences of 112 manually curated reference disease resistance genes were collected from the plant resistance gene database (http://www.prgdb.org) . Non-redundant protein sequences (unigenes) from the six wild Nicotiana species were subsequently checked for sequence homology with at least one resistance protein contained in the reference dataset using BLASTp (version 2.2.29+) (scores ≥100 and e-values ≤10−5). In a second step, all the BLAST hits were used for further analysis and were screened for protein domains by InterProScan version 5.4 . In the third step, the genes with NBS domain were filtered out according to the NBS domain annotation (PF00931) given by the Pfam database (v27.0) . Subsequently, the Pfam database, SMART protein motif analyses (Simple Modular Architecture Research Tool) (v6.2) , and the ncoils program (version 2.2)  were used to classify the NBS genes based on TIR, NBS, LRR and CC motifs. The program ncoils was used by InterProScan version 5.4 with default settings to predict coiled-coils domains. Pfam database (v27.0) and SMART protein motif analysis (v6.2) were used to detect the TIR (PF01582) and LRR domains (PF00560, PF07723, PF07725, PF12799, PF13306, PF13504, PF13516, PF13855, and PF14580).
Ten different TF families involved in disease resistance were retrieved from the literature by Sood et al. . Then the unigenes were searched against the domains of ten TFs, including MYB (PF00249, PF13921, PF14379), WRKY (PF03106), ERF-type/AP2-EREBP (PF00847), CBF (PF02312, PF00808, PF03914), bZIP (PF00170, PF03131, PF07716, PF12498), SBP/SPL6 (PF03110), NAC domain/NAM (PF02365, PF14303), TFIIA (PF03153, PF02268, PF02751), Homeo-domain (PF00046, PF05920, PF00157, PF13384, PF13565) and Whirly (PF08536) using the Pfam database from InterProScan version 5.4, respectively. Finally, proteins matching with the Pfam IDs were selected as TFs associated with disease resistance.
Identification of alkaloid transporter genes
The coding sequences of Nt-JAT1 (accession number AM991692), Nt-JAT2 (accession number AB922128), NtMATE1 (accession number AB286961), NtMATE2 (accession number AB286962) and NtNUP1 (accession number GU174267) of N. tabacum were retrieved from NCBI. To identify orthologs of the alkaloid transporter genes in each species, we first performed a bidirectional BLAST search of the alkaloid transporter genes and unigenes from the six species against each other (identity ≥80 %, e-values ≤10−5, query coverage ≥50 %).
Availability of supporting data
RNA-seq data have been deposited in the NCBI sequence Read Archive under the accession numbers SRR2106216, SRR2106514, SRR2106516, SRR2106517, SRR2106530 and SRR2106531. The data set supporting the results of this article is included within the additional files.
NL analyzed the data and drafted the manuscript. XLR provided the plants and revised the manuscript. WTW performed the experiment, prepared the mRNA and performed sequencing. ZDX performed data preprocess. YD designed the project and revised the manuscript. All authors read and approved the final manuscript.
We thank Guizhou Tobacco Research Institute for their assistance in providing wild Nicotiana materials.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Goodspeed TH. Cytotaxonomy of Nicotiana. Bot Rev. 1945;11:533–92.View ArticleGoogle Scholar
- Goodspeed TH, Thompson MC. Cytotaxonomy of Nicotiana. II. Bot Rev. 1959;25:385–415.View ArticleGoogle Scholar
- Knapp S, Chase MW, Clarkson JJ. Nomenclatural changes and a new sectional classification in Nicotiana (Solanaceae). Taxon. 2004;53:73–82.View ArticleGoogle Scholar
- Lewis RS. Nicotiana. In: Kole C, editor. Wild crop relatives: genomic and breeding resources, plantation and ornamental crops. Berlin: Springer; 2011. p. 185–208.View ArticleGoogle Scholar
- Battey JN, Sierro N, Bakaher N, Ivanov NV. Advances in Nicotiana genetic and “omics” resources. In: Tuberosa R, Graner A, Frison E, editors. Genomics of plant genetic resources. Dordrecht: Springer; 2014. p. 511–32.View ArticleGoogle Scholar
- Lewis R, Linger L, Wolff M, Wernsman E. The negative influence of N-mediated TMV resistance on yield in tobacco: linkage drag versus pleiotropy. Theor Appl Genet. 2007;115:169–78.View ArticlePubMedGoogle Scholar
- Whitham S, Dinesh-Kumar SP, Choi D, Hehl R, Corr C, Baker B. The product of the tobacco mosaic virus resistance gene N: similarity to toll and the interleukin-1 receptor. Cell. 1994;78:1101–15.View ArticlePubMedGoogle Scholar
- Sanseverino W, Hermoso A, D’Alessandro R, Vlasova A, Andolfo G, Frusciante L, et al. PRGdb 2.0: towards a community-based database model for the analysis of R-genes in plants. Nucleic Acids Res. 2013;41:D1167–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Michelmore RW, Meyers BC. Clusters of resistance genes in plants evolve by divergent selection and a birth-and-death process. Genome Res. 1998;8:1113–30.PubMedGoogle Scholar
- Bai J, Pennill LA, Ning J, Lee SW, Ramalingam J, Webb CA, et al. Diversity in nucleotide binding site-leucine-rich repeat genes in cereals. Genome Res. 2002;12:1871–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Cannon SB, Zhu H, Baumgarten AM, Spangler R, May G, Cook DR, et al. Diversity, distribution, and ancient taxonomic relationships within the TIR and non-TIR NBS-LRR resistance gene subfamilies. J Mol Evol. 2002;54:548–62.View ArticlePubMedGoogle Scholar
- Alvarenga SM, Caixeta ET, Hufnagel B, Thiebaut F, Maciel-Zambolim E, Zambolim L, et al. In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases. Genet Mol Biol. 2010;33:795–806.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu Z, Crampton M, Todd A, Kalavacharla V. Identification of expressed resistance gene-like sequences by data mining in 454-derived transcriptomic sequences of common bean (Phaseolus vulgaris L.). BMC Plant Biol. 2012;12:42.View ArticlePubMedPubMed CentralGoogle Scholar
- Joshi RK, Kar B, Nayak S. Survey and characterization of NBS-LRR (R) genes in Curcuma longa transcriptome. Bioinformation. 2011;6:360–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Rajesh MK, Rachana KE, Naganeeswaran SA, Shafeeq R, Thomas RJ, Shareefa M, et al. Identification of expressed resistance gene analog sequences in coconut leaf transcriptome and their evolutionary analysis. Turk J Agric For. 2015;39:489–502.View ArticleGoogle Scholar
- Burk L, Heggestad H. The genus Nicotiana: a source of resistance to diseases of cultivated tobacco. Econ Bot. 1966;20:76–88.View ArticleGoogle Scholar
- Durbin RD. Nicotiana: procedures for experimental use. Washington: Technical Bulletins; 1979.Google Scholar
- Trojak-Goluch A, Berbeć A. Potential of Nicotiana glauca (Grah.) as a source of resistance to black root rot Thielaviopsis basicola (Berk. and Broome) Ferr. in tobacco improvement. Plant Breed. 2005;124:507–10.View ArticleGoogle Scholar
- Baldwin IT. Mechanism of damage-induced alkaloid production in wild tobacco. J Chem Ecol. 1989;15:1661–80.View ArticlePubMedGoogle Scholar
- Steppuhn A, Gase K, Krock B, Halitschke R, Baldwin IT. Nicotine’s defensive function in nature. PLoS Biol. 2004;2:E217.View ArticlePubMedPubMed CentralGoogle Scholar
- Bombarely A, Rosli HG, Vrebalov J, Moffett P, Mueller LA, Martin GB. A draft genome sequence of Nicotiana benthamiana to enhance molecular plant-microbe biology research. Mol Plant Microbe Interact. 2012;25:1523–30.View ArticlePubMedGoogle Scholar
- Nakasugi K, Crowhurst RN, Bally J, Wood CC, Hellens RP, Waterhouse PM. De novo transcriptome sequence assembly and analysis of RNA silencing genes of Nicotiana benthamiana. PLoS ONE. 2013;8:e59534.View ArticlePubMedPubMed CentralGoogle Scholar
- Sierro N, Battey JN, Ouadi S, Bovet L, Goepfert S, Bakaher N, et al. Reference genomes and transcriptomes of Nicotiana sylvestris and Nicotiana tomentosiformis. Genome Biol. 2013;14:R60.View ArticlePubMedPubMed CentralGoogle Scholar
- Nakasugi K, Crowhurst R, Bally J, Waterhouse P. Combining transcriptome assemblies from multiple de novo assemblers in the allo-tetraploid plant Nicotiana benthamiana. PLoS ONE. 2014;9:e91776.View ArticlePubMedPubMed CentralGoogle Scholar
- Sierro N, Battey JND, Ouadi S, Bakaher N, Bovet L, Willig A, et al. The tobacco genome sequence and its comparison with those of tomato and potato. Nat Commun. 2014; 5.
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.View ArticlePubMedPubMed CentralGoogle Scholar
- Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23:1061–7.View ArticlePubMedGoogle Scholar
- Felsenstein J. PHYLIP-phylogeny inference package (version 3.2). Cladistics. 1989;5:164–6.Google Scholar
- Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.View ArticlePubMedGoogle Scholar
- Goodspeed TH. The genus Nicatiana: origins, relationships and evolution of its species in light of their distribution, morphology and cytogenetics. Waltham: Chronica Botanica Co; 1954.Google Scholar
- Chase MW, Knapp S, Cox AV, Clarkson JJ, Butsko Y, Joseph J, et al. Molecular systematics, GISH and the origin of hybrid taxa in Nicotiana (Solanaceae). Ann Bot. 2003;92:107–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Wenping H, Yuan Z, Jie S, Lijun Z, Zhezhi W. De novo transcriptome sequencing in Salvia miltiorrhiza to identify genes involved in the biosynthesis of active ingredients. Genomics. 2011;98:272–9.View ArticlePubMedGoogle Scholar
- La Camera S, Gouzerh G, Dhondt S, Hoffmann L, Fritig B, Legrand M, et al. Metabolic reprogramming in plant innate immunity: the contributions of phenylpropanoid and oxylipin pathways. Immunol Rev. 2004;198:267–84.View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Meyers BC, Kozik A, Griego A, Kuang H, Michelmore RW. Genome-wide analysis of NBS-LRR-encoding genes in Arabidopsis. Plant Cell. 2003;15:809–34.View ArticlePubMedPubMed CentralGoogle Scholar
- Raju KS, Sheshumadhav M, Murthy T. Molecular diversity in the genus Nicotiana as revealed by randomly amplified polymorphic DNA. Physiol Mol Biol Plants. 2008;14:377–82.View ArticleGoogle Scholar
- Tai TH, Dahlbeck D, Clark ET, Gajiwala P, Pasion R, Whalen MC, et al. Expression of the Bs2 pepper gene confers resistance to bacterial spot disease in tomato. Proc Natl Acad Sci USA. 1999;96:14153–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu Y, Schiff M, Marathe R, Dinesh-Kumar SP. Tobacco Rar1, EDS1 and NPR1/NIM1 like genes are required for N-mediated resistance to tobacco mosaic virus. Plant J. 2002;30:415–29.View ArticlePubMedGoogle Scholar
- Katiyar A, Smita S, Lenka SK, Rajwanshi R, Chinnusamy V, Bansal KC. Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis. BMC Genom. 2012;13:544.View ArticleGoogle Scholar
- Pandey SP, Somssich IE. The role of WRKY transcription factors in plant immunity. Plant Physiol. 2009;150:1648–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Alves MS, Dadalto SP, Goncalves AB, De Souza GB, Barros VA, Fietto LG. Plant bZIP transcription factors responsive to pathogens: a review. Int J Mol Sci. 2013;14:7815–28.View ArticlePubMedPubMed CentralGoogle Scholar
- Desveaux D, Subramaniam R, Després C, Mess J-N, Lévesque C, Fobert PR, et al. A “Whirly” transcription factor is required for salicylic acid-dependent disease resistance in Arabidopsis. Dev Cell. 2004;6:229–40.View ArticlePubMedGoogle Scholar
- Shin R, Han J-H, Lee G-J, Peak K-H. The potential use of a viral coat protein gene as a transgene screening marker and multiple virus resistance of pepper plants coexpressing coat proteins of cucumber mosaic virus and tomato mosaic virus. Transgenic Res. 2002;11:215–9.View ArticlePubMedGoogle Scholar
- Shitan N, Kato K, Shoji T. Alkaloid transporters in plants. Plant Biotechnology. 2014;31:453–63.View ArticleGoogle Scholar
- Morita M, Shitan N, Sawada K, Van Montagu MC, Inzé D, Rischer H, et al. Vacuolar transport of nicotine is mediated by a multidrug and toxic compound extrusion (MATE) transporter in Nicotiana tabacum. Proc Natl Acad Sci USA. 2009;106:2447–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Shoji T, Inai K, Yazaki Y, Sato Y, Takase H, Shitan N, et al. Multidrug and toxic compound extrusion-type transporters implicated in vacuolar sequestration of nicotine in tobacco roots. Plant Physiol. 2009;149:708–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Hildreth SB, Gehman EA, Yang H, Lu RH, Ritesh KC, Harich KC, et al. Tobacco nicotine uptake permease (NUP1) affects alkaloid metabolism. Proc Natl Acad Sci USA. 2011;108:18179–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Shitan N, Minami S, Morita M, Hayashida M, Ito S, Takanashi K, et al. Involvement of the leaf-specific multidrug and toxic compound extrusion (MATE) transporter Nt-JAT2 in vacuolar sequestration of nicotine in Nicotiana tabacum. PLoS ONE. 2014;9:e108789.View ArticlePubMedPubMed CentralGoogle Scholar
- Kato K, Shitan N, Shoji T, Hashimoto T. Tobacco NUP1 transports both tobacco alkaloids and vitamin B6. Phytochemistry. 2015;113:33–40.View ArticlePubMedGoogle Scholar
- Yi Y, Rufty R. RAPD markers elucidate the origin of the root-knot nematode resistance gene (Rk) in tobacco. Tob Sci. 1998;42:58–63.Google Scholar
- Johnson E, Wolff M, Wernsman E, Atchley W, Shew H. Origin of the black shank resistance gene, Ph, in tobacco cultivar Coker 371-Gold. Plant Dis. 2002;86:1080–4.View ArticleGoogle Scholar
- Johnson E, Wolff M, Wernsman E, Rufty R. Marker-assisted selection for resistance to black shank disease in tobacco. Plant Dis. 2002;86:1303–9.View ArticleGoogle Scholar
- Lewis RS. Transfer of resistance to potato virus Y (PVY) from Nicotiana africana to Nicotiana tabacum: possible influence of tissue culture on the rate of introgression. Theor Appl Genet. 2005;110:678–87.View ArticlePubMedGoogle Scholar
- Milla S, Lewin J, Lewis R, Rufty R. RAPD and SCAR markers linked to an introgressed gene conditioning resistance to Peronospora tabacina DB Adam in tobacco. Crop science. 2005;45:2346–54.View ArticleGoogle Scholar
- Lewis RS, Milla SR, Kernodle SP. Analysis of an introgressed Nicotiana tomentosa genomic region affecting leaf number and correlated traits in Nicotiana tabacum. Theor Appl Genet. 2007;114:841–54.View ArticlePubMedGoogle Scholar
- Moon H, Nicholson J. AFLP and SCAR markers linked to resistance in tobacco. Crop Sci. 2007;47:1887–94.View ArticleGoogle Scholar
- Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006;34:W293–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:D277–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:1.Google Scholar
- Zhou X, Rinker DC, Pitts RJ, Rokas A, Zwiebel LJ. Divergent and conserved elements comprise the chemoreceptive repertoire of the nonblood-feeding mosquito Toxorhynchites amboinensis. Genome Biol Evol. 2014;6:2883–96.View ArticlePubMedPubMed CentralGoogle Scholar
- Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.View ArticlePubMedPubMed CentralGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Wei H, Li W, Sun X, Zhu S, Zhu J. Systematic analysis and comparison of nucleotide-binding site disease resistance genes in a diploid cotton Gossypium raimondii. PLoS ONE. 2013;8:e68435.View ArticlePubMedPubMed CentralGoogle Scholar
- Sonnhammer EL, Eddy SR, Durbin R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 1997;28:405–20.View ArticlePubMedGoogle Scholar
- Schultz J, Milpetz F, Bork P, Ponting CP. SMART, a simple modular architecture research tool: identification of signaling domains. Proc Natl Acad Sci USA. 1998;95:5857–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Lupas A, Van Dyke M, Stock J. Predicting coiled coils from protein sequences. Science. 1991;252:1162–4.View ArticlePubMedGoogle Scholar
- Sood A, Jaiswal V, Chanumolu SK, Malhotra N, Pal T, Chauhan RS. Mining whole genomes and transcriptomes of Jatropha (Jatropha curcas) and Castor bean (Ricinus communis) for NBS-LRR genes and defense response associated transcription factors. Mol Biol Rep. 2014;41:7683–95.View ArticlePubMedGoogle Scholar