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  • Open Access
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Genomes and virulence difference between two physiological races of Phytophthora nicotianae

†Contributed equally
GigaScience20165:3

https://doi.org/10.1186/s13742-016-0108-7

©  Liu et al. 2016

  • Received: 15 September 2015
  • Accepted: 6 January 2016
  • Published:
Open Peer Review reports

Abstract

Background

Black shank is a severe plant disease caused by the soil-borne pathogen Phytophthora nicotianae. Two physiological races of P. nicotianae, races 0 and 1, are predominantly observed in cultivated tobacco fields around the world. Race 0 has been reported to be more aggressive, having a shorter incubation period, and causing worse root rot symptoms, while race 1 causes more severe necrosis. The molecular mechanisms underlying the difference in virulence between race 0 and 1 remain elusive.

Findings

We assembled and annotated the genomes of P. nicotianae races 0 and 1, which were obtained by a combination of PacBio single-molecular real-time sequencing and second-generation sequencing (both HiSeq and MiSeq platforms). Gene family analysis revealed a highly expanded ATP-binding cassette transporter gene family in P. nicotianae. Specifically, more RxLR effector genes were found in the genome of race 0 than in that of race 1. In addition, RxLR effector genes were found to be mainly distributed in gene-sparse, repeat-rich regions of the P. nicotianae genome.

Conclusions

These results provide not only high quality reference genomes of P. nicotianae, but also insights into the infection mechanisms of P. nicotianae and its co-evolution with the host plant. They also reveal insights into the difference in virulence between the two physiological races.

Keywords

  • Black shank
  • Phytophthora nicotianae
  • Genomes
  • Hybrid assembly
  • RxLR effector

Data description

Background

Phytophthora nicotianae, also known as Phytophthora parasitica var. nicotianae, is a soil-borne bi-flagellated oomycete plant pathogen, which causes black shank in cultivated tobacco (Nicotiana tabacum), and root rot, leaf necrosis, and stem lesions in a variety of plants [1]. P. nicotianae is able to infect a wide range of hosts, spanning 255 genera in 90 different plant families. It devastates the production of a number of economically important plants, and causes millions of dollars worth of economic losses each year in the tobacco industry alone [2, 3]. So far, management strategies for P. nicotianae are limited to non-host crop rotation, cultivation of pathogen-resistant breeds, and the use of chemical control (e.g. mefenoxam) [4]. The primary reason for the difficulty in controlling P. nicotianae is the production and survival of chlamydospores in unfavorable growth conditions, as well as the production of motile zoospores. The ability of P. nicotianae to infect specific tobacco cultivars with different resistance genes defines four physiological races (0, 1, 2 and 3). The predominant physiological races, 0 and 1, are widely distributed throughout China, the United States and other major tobacco cultivation areas [5, 6]. Previous studies using tobacco cultivars with moderate or high levels of resistance have found that race 0 has better pathogenic and ecologic fitness levels than race 1, suggesting that the difference in virulence between the two races is affected by additional genetic factors [7]. To discover better and more efficient ways to control the pathogen, we undertook a global examination of the genes involved in the infection process from different races. Although five strains of P. parasitica are already public available [8], none of these includes any of the four physiological races of P. nicotianae. Here we report the genomes of P. nicotianae physiological races 0 and 1, sequenced using a combination of PacBio single-molecule real-time (SMRT) sequencing technology, and Illumina HiSeq and MiSeq sequencing technologies, and identify candidate genes that may cause the difference in virulence between them.

Isolation of P. nicotianae races and genomic DNA extraction

Tobacco plants infected by either P. nicotianae race 0 or race 1 were obtained from Yunnan Tobacco Research Institute. Any surface dirt on the infected plant was washed off under tap water. After drying, stem tissue from the lesion margin were cut into 5 × 5 mm squares, sterilized using 70 % ethanol for 1 minute, and then rinsed three times using sterile water. Sterilized tissue squares were then placed in lima bean agar (LBA) plates amended with 50 μg/ml ampicillin, 100 μg/ml rifampicin, and 50 μg/ml of pentachloronitrobenzene to suppress possible contaminant. LBA plates were incubated for 2–3 days in darkness at 25 °C. Color and texture of the colony and mycelium were used to confirm the identity of P. nicotianae. Mycelium was transferred to LBA slants and cultured for 7 days in darkness at 25 °C. Genomic DNA was extracted using the modified cetyltrimethyl ammonium bromide method [9].

Sequencing and quality control

Whole-genome sequencing yielded 41 Gb HiSeq paired-end reads, 5 Gb HiSeq mate pair reads, 5 Gb MiSeq reads, and 5 Gb PacBio long reads for race 0; and 46 Gb HiSeq pair-end reads, 4 Gb HiSeq mate pair reads, 3 Gb MiSeq reads, and 6 Gb PacBio long reads for race 1 (Table 1). Illumina HiSeq reads were first filtered out with >10 % N or with >40 bp low quality bases. Redundant reads resulting in duplicate base calls were filtered at a threshold of a Euclid distance ≤ 3 and a mismatch rate of ≤ 0.1. Where duplicated paired-end (PE) reads were identical, only one copy was retained. For adapter contamination caused by DNA adapter dimerization, empty loading, or too small an insert size (less than a read length), we filtered out if both read 1 and read 2 contained an adapter ≥ 10 bp with a mismatch rate ≤ 0.1. For PacBio reads, we first used the HGAP (SMRT Analysis v2.1.1) pipeline to perform self-correction (default parameters). Longer PacBio reads were selected automatically as seeds; the rest of the reads were aligned against these seed sequences for correction. For hybrid correction, we used LSC [10] (v1.0 alpha) with the parameter for bowtie2 set to very-fast; pacBioToCA [11] (wgs v8.0) using the parameter length 500. Corrected PacBio long reads were obtained by aligning high accuracy HiSeq short reads against PacBio long reads. We also used ECTools (July 6th 2014) to correct PacBio long reads from both P. nicotianae races 0 and 1. ECTools aligned unitigs assembled from MiSeq reads against PacBio long reads to perform correction.
Table 1

Sequencing and data size of P. nicotianae races 0 and race 1

Races

Library type

Instrument

Fragment size (bp)

Read length (bp)

Data (Gb)

Before quality control

After quality control

P. nicotianae race 0

Illumina paired-end

Hiseq

350

100

41

34

Illumina mate pair

Hiseq

2,000

100

5

3

 

Illumina paired-end

Miseq

500

~300

5

a3

 

SMRTbell

PacBio RS

10,000

~1,932

5

b3

P. nicotianae race 1

Illumina paired-end

Hiseq

350

100

46

20

Illumina mate pair

Hiseq

2,000

100

4

2

 

Illumina paired-end

Miseq

500

~300

3

a2

SMRTbell

PacBio RS

10,000

~2,333

6

b4

aTotal base pairs of Miseq reads after merged using Flash

bPacBio reads after correction with LSC

Assembly

Because of its relatively high heterozygosity, we used a hybrid assembly approach to assemble the genome of race 0 (Fig. 1). We also compared the performance of different assemblers including Velvet (v1.2.09) [12], ABySS (v3.81) [13], JR-Assembler (v1.0.3) [14], EULER-SR (v1.1.2) [15], SPAdes (v3.0.0) [16], SOAPdenovo2 (r240) [17], Celera Assembler (v8.0) [18] and Minimus2 (v 3.1) [19] on P. nicotianae race 0. Comparison showed that assemblies from PacBio reads were generally of better quality than those from HiSeq reads (Fig. 2). The final assembled genome sizes for race 0 and race 1 were 80 Mb and 69 Mb, respectively, which is slightly different from the previous estimation of P. nicotianae (90 Mb) [20]. The corresponding contig N50 sizes were 23 kB and 30 kB, respectively (Table 2). Over 95 % of core eukaryotic genes could be mapped to the two genomes using CEGMA [21], and over 90 % of Illumina HiSeq reads could be mapped back to the genome assemblies.
Fig. 1
Fig. 1

Assembly pipeline for P. nicotianae race 0. CA indicates Celera Assembler. Because of high heterozygosity of spores in P. nicotianae race 0, we used a hybrid approach including Celera Assembler, ABySS assembler, and Minimus2 to assemble this genome

Fig. 2
Fig. 2

NGx plot for different assemblies. Contigs of length equal to or above NGx occupy x % of the reference genome. pacBioToCA + LSC + HGAP refers to assembly from non-redundant corrected PacBio reads of LSC, pacBioToCA and HGAP. pacBioToCA + ABySS means assembly using pacBioToCA corrected reads and unitigs assembled by ABySS. Assembly from pacBioToCA + ABySS was also merged with assembly from ECTools corrected PacBio reads to generate the final assembly, which was then used for annotation. As illustrated, the NG50 for hybrid assemblies such as pacBioToCA + ABySS were longer

Table 2

Genome and gene statistics of P. nicotianae races 0 and race 1

Races

Categories

Number

N50 (kb)

Longest size (kb)

Size (Mb)

Percentage of the assembly

P. nicotianae race 0

Total contigs

6142

23

203

81

-

Total scaffolds

6139

23

203

81

-

Genes annotated

17,797

1.8

26

27

33 %

Transposable elements

-

-

-

32

39 %

P. nicotianae race 1

Total contigs

6116

22

196

71

-

Total scaffolds

5317

30

318

71

-

Genes annotated

14,542

1.7

15

20

28 %

Transposable elements

-

-

-

27

38 %

Annotation

Known transposable elements (TEs) were identified with RepeatMasker (version 3.2.6) [22] using the Repbase TE library (v16.10) [23] and default parameters. Tandem repeats were predicted using TRF [24]. gypsy and copia types of long terminal repeat (LTR) were the main contributors to the repeat, making up 12.5 % and 3.5 % of the genome for race 0, and 11.5 % and 3.6 of the genome for race 1. For gene structure prediction, gene sets from 9 species including Phytophthora infestans [25], Phytophthora sojae [26], Phytophthora ramorum [26], Hyaloperonospora arabidopsis [27], Pythium aphanidermatum [28], Pythium arrhenomanes [28], Pythium irregulare [28], Pythium vexans [28], Pythium iwayamai [28] and Pythium ultimum [29] were used for homology-based prediction. GENSCAN [30], AUGUSTUS [31] and GlimmerHMM [32] were used for de novo gene prediction. Evidence derived from homology-based and de novo predictions were then integrated in GLEAN to generate a consensus gene set. A total of 17,797 and 14,542 protein-coding genes were annotated in P. nicotianae race 0 and race 1, respectively. Over 97 % of these genes could be aligned against KEGG [33], Swiss-Prot and TrEMBL databases [34]. Mean exon numbers per gene in P. nicotianae and related species varied between 2.2 and 2.8, suggesting that homology and de novo-based prediction were appropriate for annotation (Additional file 1). We also used publicly available expressed sequence tags (ESTs) from the appressorium [35] and mycelium [36, 37] of P. nicotianae to validate the annotation. We retrieved a total of 10,524 ESTs from the dbEST database. Using the threshold of match length >200 bp and E-value <1e-5, we aligned 8,043 ESTs to the race 0 genome and 7,618 ESTs to the race 1 genome. Additionally, 4,454 genes in race 0 and 3,604 genes in race 1 were supported by at least one EST (Additional file 2). Whole genome comparison using NUCmer [38] found that average identity was 99 % for 1-to-1 alignment, and 98.84 % for m-to-m alignment between P. nicotianae races 0 and 1. Using KaKs_Calculator, mean synonymous mutation ratio (Ks) was estimated to be 0.075 between race 0 and race 1 [39], and four genes were identified to be positively selected (Additional file 3).

Gene family clustering and evolution

Gene family clustering using OrthoMCL [40] revealed that over 72 % of gene families were shared between species pairs among P. nicotianae race 0, race 1, and related species (Additional file 4). The average number of genes per gene family was 1.19 to 1.50 in Phytophthora and 1.14 to 1.26 in Pythium, suggesting more copies of genes exist in the Phytophthora genus. A total of 1,604 single-copy genes were identified between P. nicotianae and the other 9 related species (Additional file 5). Gene family expansion and contraction estimated using CAFÉ [41] found that 1,237 gene families expanded and 294 gene families contracted in race 0, while 217 gene families expanded and 508 gene families contracted in race 1 (Fig. 3).
Fig. 3
Fig. 3

Gene family expansion and contraction in P. nicotianae races 0 and 1, and related species. The proportions of expanded (blue) and contracted (yellow) gene families are shown as pie charts at each branch terminus. MRCA represents most recent common ancestor

ABC transporter expanded in P. nicotianae

The ATP-binding cassette transporter (ABC transporter) superfamily facilitates the transport of ions, proteins, lipids and toxins across plant membranes [42]. Interestingly, a domain-centric study found this gene family to be enriched in the oomycete plant pathogen genomes [43]. It was proposed that an important function of ABC transporters in pathogens involves exporting toxic phytoalexins [44, 45]. Based on the result of CAFÉ analysis, we found the ABC transporter gene family to be significantly expanded in the branch of P. nicotianae (likelihood ratio test, p-value < 0.05), but not in the branch of P. infestans (likelihood ratio test, p = 0.9). To verify this result, we used Pfam to annotate ABC transporter domains (PF00005.22, PF00664.18, PF01061.19) between P. infestans, and P. nicotianae races 0 and 1 (Additional file 6). The portions of ABC transporters in P. nicotianae were significantly larger than that in P. infestans (chi-square test, p < 0.05). This result suggests that the ABC transporter family plays important roles in P. nicotianae in its adaptive evolution to the host.

Distribution of effectors and their differences in races 0 and 1

Plant pathogens have evolved to secrete effectors, which can manipulate the host immune system and suppress host defense. Based on their target sites in the host plant, effectors can be classified into two classes: (1) apoplastic effectors, which are secreted into plant extracellular spaces; and (2) cytoplasmic effectors, which are translocated into the plant cell. Some effector genes, e.g. ATR5 in H. arabidopsidis, are found to be avirulence genes [46]. These genes are under selective pressure to evade host recognition while maintaining their original functions.

RxLR effectors are important cytoplasmic effectors that contain a conserved N-terminal motif (Arg-X-Leu-Arg). The RxLR motif is involved in translocation into host cells [47]. During infection, the RxLR family functions to suppress host immunity. This process usually involves manipulating plant immunity-associated signaling pathways. For example, PexRD2 can perturb MAPKKKε signaling pathways to suppress NB-LRR-mediated immunity in P. infestans [48]. In addition, a set of RxLR effectors from P. infestans can suppress the signaling pathway induced by flg22, a kind of microbe-associated molecular pattern (MAMP) [49], while some RxLR effectors such as Avr1b-1 and Avr1k can be recognized by NB-LRR immune receptors to confer resistance [50]. Research in 2015 also found that the evolution of RxLR effectors varies between the genus Phytophthora and downy mildews: more conserved RxLR effectors were observed in the genus Phytophthora [51]. We performed a whole-genome scan for RxLR effector genes in race 0 and race 1. The analysis showed that most RxLR genes were distributed in repeat-rich, gene-sparse regions (Fig. 4), suggesting rapid evolution of RxLR effectors. Specifically, a total of 308 RxLR effector genes were predicted in race 0, and 199 genes in race 1 (Additional files 7 and 8). The difference in RxLR effector gene numbers between race 0 and race 1 may contribute to the variance in the aggressiveness of these pathogens in cultivated tobacco.
Fig. 4
Fig. 4

Bin plot showing repeat and gene density distribution. Bins of gene density were sorted and plotted on the basis of 5’ and 3’ intergenic border lengths. The color of each bin represents the number of genes. a Distribution in P. nicotianae race 0 RxLR effectors. b Distribution of P. nicotianae race 0 genes. c Distribution of P. nicotianae race 1 RxLR effectors. d Distribution of P. nicotianae race 1 genes

Crinkler (CRN) effectors are another important class of effectors that cause leaf crinkling in plants [52]. To investigate CRN effectors in P. nicotianae races 0 and 1, we first used EMBOSS getorf (−minsize 300) to extract open reading frames (ORFs) from the whole genome, and then used HMMer (−E 1e-5) with existing profiles [25]. Predicted CRN effectors were filtered by the presence of the LxLFLAK motif. A total of 32 and 26 CRN effectors were annotated in P. nicotianae races 0 and 1, respectively. However, the number of CRN effectors may be underestimated, given the model we used [53].

Availability of supporting data

The genome assembly, annotation and sequencing reads of each sequencing library are available in the NCBI repository, project ID PRJNA294216. The genome assembly and annotation can also be accessed via the GigaScience GigaDB database [54].

Notes

Abbreviations

ABC transporter: 

ATP-binding cassette transporter

CRN: 

Crinkler effector

EST: 

expressed sequence tag

LBA: 

lima bean agar

LTR: 

long terminal repeat

ORF: 

open reading frame

PE: 

paired-end

RxLR: 

effectors with Arg-X-Leu-Arg motif

SMRT: 

single-molecular real-time sequencing

Declarations

Acknowledgements

We owe sincere thanks to Dr. Chen Wei for his pre-submission review of the article. We thank Yunnan Tobacco Research Institute for kindly providing infected tobacco plants. We thank Hou Yujie for help on gene annotation and Dr. Shi Xiaofei for useful discussions of the article.

Funding

This work was supported by grants from the China National Tobacco Corporation (110201201003 [JY-03]) and 110201301006 [JY-06]), and Yunnan Tobacco Corporation (2012YN01 and 2013YN01).

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.

Authors’ Affiliations

(1)
CAS-Max Planck Junior Research Group, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
(2)
University of Chinese Academy of Sciences, Beijing, 100049, China
(3)
Yunnan Agricultural University, Kunming, 650100, China
(4)
Yunnan Academy of Tobacco Agricultural Sciences, Yuantong Street No.33, Kunming, Yunnan, 650021, China
(5)
Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, China

References

  1. Lamour K. Phytophthora: a global perspective. Knoxville: CAB International; 2013.View ArticleGoogle Scholar
  2. Drenth A, Sendall B. Economic impact of Phytophthora diseases in Southeast Asia. In: Drenth A, Guest DI, editors. Diversity and management of Phytophthora in Southeast Asia. Canberra, Australia: Australian Centre for International Agricultural Research; 2004.Google Scholar
  3. Kamoun S, van West P, Vleeshouwers V, de Groot KE, Govers F. Resistance of Nicotiana benthamiana to Phytophthora infestans is mediated by the recognition of the elicitor protein INF1. Plant Cell. 1998;10:1413–26.PubMedPubMed CentralView ArticleGoogle Scholar
  4. Hu JH, Hong CX, Stromberg EL, Moorman GW. Mefenoxam sensitivity and fitness analysis of Phytophthora nicotianae isolates from nurseries in Virginia, USA. Plant Pathol. 2008;57:728–36.View ArticleGoogle Scholar
  5. Apple JL. Physiological specialization within Phytophthora parasitica var nicotianae. Phytopathology. 1962;52:351–4.Google Scholar
  6. Li X, Kong F, Li X, Wang J, Zhang C, Feng C, et al. Preliminary Report on Physiological Race of Phytophthora parasitica in Hubei. Chin Tob Sci. 2011;32:84–8.Google Scholar
  7. Sullivan MJ, Melton TA, Shew HD. Fitness of Races 0 and 1 of Phytophthora parasitica var. nicotianae. Plant Dis. 2005;89:1220–8.View ArticleGoogle Scholar
  8. Phytophthora parasitica Assembly Dev initiative, Broad Institute (broadinstitute.org). https://olive.broadinstitute.org/projects/phytophthora_parasitica. Accessed 2 Sept 2015.
  9. Doyle J, Doyle JL. Genomic plant DNA preparation from fresh tissue-CTAB method. Phytochem Bull. 1987;19:11–5.Google Scholar
  10. Au KF, Underwood JG, Lee L, Wong WH. Improving PacBio long read accuracy by short read alignment. PLoS One. 2012;7:e46679.PubMedPubMed CentralView ArticleGoogle Scholar
  11. Koren S, Schatz MC, Walenz BP, Martin J, Howard JT, Ganapathy G, et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat Biotechnol. 2012;30:693–700.PubMedPubMed CentralView ArticleGoogle Scholar
  12. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.PubMedPubMed CentralView ArticleGoogle Scholar
  13. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM, Birol İ. ABySS: a parallel assembler for short read sequence data. Genome Res. 2009;19:1117–23.PubMedPubMed CentralView ArticleGoogle Scholar
  14. Chu T-C, Lu C-H, Liu T, Lee GC, Li W-H, Shih AC-C. Assembler for de novo assembly of large genomes. Proc Natl Acad Sci USA. 2013;110:E3417–24.PubMedPubMed CentralView ArticleGoogle Scholar
  15. Chaisson MJ, Pevzner PA. Short read fragment assembly of bacterial genomes. Genome Res. 2008;18:324–30.PubMedPubMed CentralView ArticleGoogle Scholar
  16. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience. 2012;1:18.PubMedPubMed CentralView ArticleGoogle Scholar
  18. Myers EW, Sutton GG, Delcher AL, Dew IM, Fasulo DP, Flanigan MJ, et al. A whole-genome assembly of Drosophila. Science. 2000;287:2196–204.PubMedView ArticleGoogle Scholar
  19. Treangen TJ, Sommer DD, Angly FE, Koren S, Pop M. Next generation sequence assembly with AMOS. Curr Protoc Bioinformatics 2011;CHAPTER:Unit11.8–Unit11.8.Google Scholar
  20. Shan W, Hardham AR. Construction of a bacterial artificial chromosome library, determination of genome size, and characterization of an Hsp70 gene family in Phytophthora nicotianae. Fungal Genet Biol. 2004;41:369–80.PubMedView ArticleGoogle Scholar
  21. Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23:1061–7.PubMedView ArticleGoogle Scholar
  22. Bedell JA, Korf I, Gish W. MaskerAid: a performance enhancement to RepeatMasker. Bioinformatics. 2000;16:1040–1.PubMedView ArticleGoogle Scholar
  23. Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005;110:462–7.PubMedView ArticleGoogle Scholar
  24. Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27:573–80.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Haas BJ, Kamoun S, Zody MC, Jiang RHY, Handsaker RE, Cano LM, et al. Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans. Nature. 2009;461:393–8.PubMedView ArticleGoogle Scholar
  26. Tyler BM, Tripathy S, Zhang X, Dehal P, Jiang RHY, Aerts A, et al. Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis. Science. 2006;313:1261–6.PubMedView ArticleGoogle Scholar
  27. Baxter L, Tripathy S, Ishaque N, Boot N, Cabral A, Kemen E, et al. Signatures of adaptation to obligate biotrophy in the Hyaloperonospora arabidopsidis genome. Science. 2010;330:1549–51.PubMedPubMed CentralView ArticleGoogle Scholar
  28. Adhikari BN, Hamilton JP, Zerillo MM, Tisserat N, LÃ vesque CA, Buell CR. Comparative genomics reveals insight into virulence strategies of plant pathogenic oomycetes. PLoS One. 2013;8:e75072.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Lévesque CA, Brouwer H, Cano L, Hamilton JP, Holt C, Huitema E, et al. Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biol. 2010;11:R73.PubMedPubMed CentralView ArticleGoogle Scholar
  30. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol. 1997;268:78–94.PubMedView ArticleGoogle Scholar
  31. Stanke M, Steinkamp R, Waack S, Morgenstern B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004;32:W309–12.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20:2878–9.PubMedView ArticleGoogle Scholar
  33. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28:45–8.PubMedPubMed CentralView ArticleGoogle Scholar
  35. Le Berre J-Y, Engler G, Panabières F. Exploration of the late stages of the tomato-Phytophthora parasitica interactions through histological analysis and generation of expressed sequence tags. New Phytol. 2008;177:480–92.PubMedGoogle Scholar
  36. Kebdani N, Pieuchot L, Deleury E, Panabieres F, Le Berre J-Y, Gourgues M. Cellular and molecular characterization of Phytophthora parasitica appressorium-mediated penetration. New Phytol. 2010;185:248–57.PubMedView ArticleGoogle Scholar
  37. Panabières F, Amselem J, Galiana E, Le Berre J-Y. Gene identification in the oomycete pathogen Phytophthora parasitica during in vitro vegetative growth through expressed sequence tags. Fungal Genet Biol. 2005;42:611–23.PubMedView ArticleGoogle Scholar
  38. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, et al. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Wang D, Zhang Y, Zhang Z, Zhu J, Yu J. KaKs_Calculator 2.0: a toolkit incorporating gamma-series methods and sliding window strategies. Genomics Proteomics Bioinformatics. 2010;8:77–80.PubMedView ArticleGoogle Scholar
  40. Li L, Stoeckert CJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.PubMedPubMed CentralView ArticleGoogle Scholar
  41. De Bie T, Cristianini N, Demuth JP, Hahn MW. CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006;22:1269–71.PubMedView ArticleGoogle Scholar
  42. Higgins CF. ABC transporters: from microorganisms to man. Ann Rev Cell Biol. 1992;8:67–113.PubMedView ArticleGoogle Scholar
  43. Seidl MF, Van den Ackerveken G, Govers F, Snel B. A domain-centric analysis of oomycete plant pathogen genomes reveals unique protein organization. Plant Physiol. 2011;155:628–44.PubMedPubMed CentralView ArticleGoogle Scholar
  44. Del Sorbo G, Schoonbeek H, De Waard MA. Fungal transporters involved in efflux of natural toxic compounds and fungicides. Fungal Genet Biol. 2000;30:1–15.PubMedView ArticleGoogle Scholar
  45. Latijnhouwers M, de Wit PJGM, Govers F. Oomycetes and fungi: similar weaponry to attack plants. Trends Microbiol. 2003;11:462–9.PubMedView ArticleGoogle Scholar
  46. Bailey K, Çevik V, Holton N, Byrne-Richardson J, Sohn KH, Coates M, et al. Molecular cloning of ATR5Emoy2 from Hyaloperonospora arabidopsidis, an avirulence determinant that triggers RPP5-mediated defense in Arabidopsis. Mol Plant Microbe Interact. 2011;24:827–38.PubMedView ArticleGoogle Scholar
  47. Win J, Morgan W, Bos J, Krasileva KV, Cano LM, Chaparro-Garcia A, et al. Adaptive evolution has targeted the C-terminal domain of the RXLR effectors of plant pathogenic oomycetes. Plant Cell. 2007;19:2349–69.PubMedPubMed CentralView ArticleGoogle Scholar
  48. King SRF, McLellan H, Boevink PC, Armstrong MR, Bukharova T, Sukarta O, et al. Phytophthora infestans RXLR effector PexRD2 interacts with host MAPKKKε to suppress plant immune signaling. Plant Cell. 2014;26:1345–59.PubMedPubMed CentralView ArticleGoogle Scholar
  49. Zheng X, McLellan H, Fraiture M, Liu X, Boevink PC, Gilroy EM, et al. Functionally redundant RXLR effectors from Phytophthora infestans act at different steps to suppress early flg22-triggered immunity. PLoS Pathog. 2014;10:e1004057.PubMedPubMed CentralView ArticleGoogle Scholar
  50. Qiao Y, Shi J, Zhai Y, Hou Y, Ma W. Phytophthora effector targets a novel component of small RNA pathway in plants to promote infection. Proc Natl Acad Sci USA. 2015;112:5850–5.PubMedPubMed CentralView ArticleGoogle Scholar
  51. Sharma R, Xia X, Cano LM, Evangelisti E, Kemen E, Judelson H, et al. Genome analyses of the sunflower pathogen Plasmopara halstedii provide insights into effector evolution in downy mildews and Phytophthora. BMC Genomics. 2015;16:741.PubMedPubMed CentralView ArticleGoogle Scholar
  52. Torto TA, Li S, Styer A, Huitema E, Testa A, Gow NAR, et al. EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora. Genome Res. 2003;13:1675–85.PubMedPubMed CentralView ArticleGoogle Scholar
  53. Stam R, Jupe J, Howden AJM, Morris JA, Boevink PC, Hedley PE, et al. Identification and characterisation CRN effectors in Phytophthora capsici shows modularity and functional diversity. PLoS One. 2013;8:e59517.PubMedPubMed CentralView ArticleGoogle Scholar
  54. Liu H, Ma X, Yu HQ, Fang DH, Wang X, Wang W, et al. Genomic assembled sequences and annotation for Phytophthora nicotianae race 0 and race 1. GigaScience Database. 2016. http://dx.doi.org/10.5524/100174. Accessed date 01 December 2015.

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