Thursday, 15 September 2016

Deciphering the Molecular Variations of Pine Wood Nematode Bursaphelenchus xylophilus with Different Virulence

Published Date

Author

Abstract

Bursaphelenchus xylophilus is the causative agent of pine wilt disease which has caused huge economic losses in many countries. It has been reported that two forms of pine wood nematodes existed in its native region, i.e., with strong virulence and weak virulence. However, little is known about the molecular differences between the two forms. To better understand their molecular variations, transcriptome and genome sequences of three strongly virulent and one weakly virulent strains were analyzed. We found 238 transcripts and 84 exons which showed notable changes between the two virulent forms. Functional analyses of both differentially expressed transcripts and exons indicated that different virulence strains showed dissimilar nematode growth, reproduction, and oxidoreductase activities. In addition, we also detected a small number of exon-skipping events in Bxylophilus. Meanwhile, 117 SNPs were identified as potential genetic markers in distinguishing the two forms. Four of them were further proved to have undergone allele specific expressions and possibly interrupted the target site of evolutionary conserved Bxylophilus miR-47. These particular SNPs were experimentally verified by including eight additional strains to ensure the validity of our sequencing results. These results could help researchers to better diagnose nematode species with different virulence and facilitate the control of pine wilt disease.

Introduction

Pine wood nematode (PWN), Bursaphelenchus xylophilus, is the causative agent of pine wilt disease (PWD) [1] and native to North America where it usually only damages exotic pine trees. It has spread to Asian and European countries such as Japan, China, South Korea, and Portugal [2]. It has a complex life cycle which involves beetles, pine trees, fungi, bacteria and causes enormous economic losses every year [3]. Although there are several hypotheses about the mechanism of PWD, such as cellulase (it was possible that the destruction of pine cells might be triggered by cell wall degrading enzymes such as cellulase), phytotoxin (some experiments showed that phytotoxins isolated from Bxylophilus can directly cause wilt symptoms) and terpenoid hypotheses (some scientists believe that cavitation and xylem water column breakage of pine tree are caused by terpenoids), the pathogenic mechanism of PWD still remains to be elucidated [47]. It is reported that two forms of PWN existed in its native region, i.e., strongly virulent (SV) and weakly virulent (WV) [89]. Usually, the virulence of PWN was evaluated by an inoculation test and Takemoto et al. (2005) reported another classification method based on PCR-RFLP patterns of heat-shock protein 70A [10]. Some previous studies proved that the lower reproductivity and increased developmental time of a generation were observed in weakly virulent strains rather than strongly virulent strains [1112]. Other studies indicated PWN with different virulence contained different enzymatic and non-enzymatic molecules which were involved in oxidative stress metabolism [13]. In the early stage of PWD, PWN has to fight with various plant immune responses. The initial host reaction to nematode invasion would be an oxidative burst [1415]. Also, high virulence isolates of Bxylophilus could withstand higher H2O2 concentrations in comparison with low virulence Bxylophilus [16]. Thus, the different oxidative abilities between the two forms could contribute to their virulence variation. Besides those reproductive and biochemical differences, limited information was found to describe the genetic variations between these two forms on a genomic scale. Thus, it is necessary to perform a genome-wide study on the two forms to gain insights on their genetic differences and to explore new ways for accurate virulence detection.
Since the first draft of the Bxylophilus genome was released in 2011, researchers have investigated this nematode on the genome level [3]. With the help of high-throughput sequencing, we were able to perform analyses on isoform alteration, SNP identification and allele-specific expression [1718]. A recent published paper focused on comparative transcriptome analysis between Bxylophilus and Bmucronatus indicated the transcriptome variations between these two close species. Meanwhile, genome wide SNP identifications proved the SNP diversity among different Bxylophilus populations [1920]. Another study also reported numerous novel parasitism genes which may be crucial for the mediation of interactions of Bxylophilus with its host using comparative transcriptomics [21]. Besides those studies focused on gene expression and SNP changes, it would also be interesting to observe if any allele-specific expression had existed in Bxylophilus since allele-specific expression can control gene expression and interruption of the regulation process could lead to disease [2223]. In this study, high-throughput RNA and DNA sequencing were used together to perform genome-wide analyses on Bxylophilus with different virulence. We selected and sequenced four nematode strains with different virulence to detect molecular differences between the two forms. Moreover, another eight nematode strains were included as additional experimental materials to better verify our sequencing results.
Generally, we found that different virulent strains exhibited different exons and transcript expression and that these changes mainly involved nematode growth, reproductivity and oxidoreductase activities. Also, we have selected and verified a subset of potential SNP markers for virulence detection.

Materials and Methods

PWN strains and virulence test

Bxylophilus strains AA3 and AMA3 from Anhui province, ZL1 from Zhejiang province, and YW4 from Yunnan province were used for next-generation DNA and RNA sequencing. Other additional strains used for experimental verification included: SG4 (Sichuan province), SC2 (Shandong province), AN5 (Anhui province), HE2 (Hubei province), GZ1 (Guizhou province), AW5 (Anhui province), JC1 (Jiangsu province), and JL1 (Jiangsu province). All these nematode strains were maintained in the Forest Protection Laboratory, Nanjing Forestry University and the virulence of these strains were demonstrated by previous inoculation tests on pine trees as described by Aikawa et al. [24]. Two-year-old Pinus thunbergii seedlings under similar growing conditions were used in inoculation studies. Pthunbergii stems were cut at 15 cm above the soil level with a sterilized scalpel. A piece of sterile absorbent cotton was placed over each wound and 200 μL nematodes suspension (about 2,000 individuals) was pipetted into wounds. Then the wounds were covered by parafilm. Control consisted of Pthunbergii seedlings inoculated with 200 μL sterile deionized water. The disease development of Pthunbergiiseedlings was observed at an interval of three days. Each treatment had six replicates. The disease severity of Pthunbergii seedlings was divided into five levels: 0, the seedlings remaining healthy with green needles and growing well; I, a few needles turning brown; II, half of the needles turning brown and the terminal shoots of seedlings bending; III, most of the needles turning brown and dead and the terminal shoots of seedlings drooping; IV, all of the needles turning brown and the whole seedling wilted. The disease severity index was calculated below:
All nematode strains were cultured on the fungus Botrytis cinerea at 25°C for almost one week. Nematodes were extracted with a Baermann funnel [2526]. After 12 h, nematodes were collected at the bottom of the funnel and stored in water suspension using a 1.5 ml centrifuge tube.

DNA and RNA extraction

Total RNA was extracted using Trizol reagent following the product descriptions (https://www.lifetechnologies.com/order/catalog/product/15596026). Genomic DNA was isolated from the Bxylophilus strains using the CTAB method [27]. Both DNA and RNA qualities were assessed by Nanodrop 2000 (http://www.nanodrop.com/Productnd2000overview.aspx) and visualized in agarose gel. Four DNA and RNA libraries for each strain were constructed and sequenced using Illumina platform un-stranded 100bp paired-end protocol. 8G DNA-seq and RNA-seq were obtained separately for each nematode strain (Novogene, Beijing).

ITS rDNA amplification and RFLP analysis

From a practical aspect, Bxylophilus, when isolated from infected trees, always contained more than one nematode species, i.e, Bmucronatus. In order to prove that the nematodes used here were not contaminated with Bmucronatus, an ITS-PCR-RFLP method was used to distinguish the two forms with one Bmucronatus strain as the negative control. A segment of rDNA was amplified by PCR according to Iwahori H’s method [28]. PCR was carried out in a 50 μl reaction mixture containing 2 μl of nematode DNA, 25 μl pre-mix, 19 μl sterilized distilled water and 2 μl forward and reverse primers. The procedure for PCR was as follows: 94°C for 2 min, followed by 40 cycles (94°C for 1 min, 55°C for 30 sec, and 72°C for 1 min), and finally 72°C for 10 min. The PCR product from each isolate was digested with 3 U of restriction endonucleases AluI, HaeIII, HinfI, MspI and RsaI according to the instructions provided by the manufacturer (Takara, Dalian). Restriction products were analyzed by electrophoresis on a 2% agarose gel.

Data analysis

All raw sequencing data were first assessed by Fastqc (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to ensure the sequencing quality. Adapters were then removed by cutadapt (https://cutadapt.readthe-docs.org/en/stable/). Filtered reads were used for subsequent analyses.

Alignment, assembly and annotation using RNA-seq.

Genome alignment and transcript assembly processes were done by following the protocol described by Cole Trapnell [29]. Firstly, RNA-seq data from YW4, AA3, AMA3 and ZL1 were aligned to the genome using Tophat allowing two mismatches (v2.0.10) [30]. Secondly, four transcripts sets were generated by Cufflinks using the frag-bias-correct option [31]. Thirdly, four transcripts were merged together into one final Bxylophilus transcriptome assembly using Cuffmerge based on genome data. This non-redundant transcriptome assembly was used as reference assembly for all the rest of our analyses. Previously assembled transcripts were annotated using Blast2Go by searching against nr, Gene Ontology and Pfam databases [3233]. GO enrichment analysis of differentially expressed transcripts and exons were performed by agriGO analysis tool kit based on Blast2Go annotation result (Pvalue<0.05) [34]. De novoassembly for each nematode strain was carried out using Trinity software (http://trinityrnaseq.github.io/).

Expression profiling of transcripts and exons.

Differentially expressed transcripts were identified using cuffdiff and DESeq (Pvalue<0.05) [3135]. Self-developed R script was used to display general expressions of all transcripts. Hierarchy cluster analysis was made using MEV package (http://www.tm4.org/mev.html). Differentially expressed exons were identified and visualized by DEXSeq (Pvalue<0.05) and visualized using plotDEXSeq [36]. VennDiagram package (http://cran.r-project.org/web/packages/-VennDiagram/index.html) was used to show overlapped events. The expressions of DE transcripts were confirmed by quantitative RT-PCR using SYBR Premix Ex Taq (Takara, Dalian, China) according to the manufacturers’ instruction on 7500 real-time system (Applied Biosystems, Carlsbad, CA, USA) [37]. The results were normalized to the expression level of the Bxylophilus Actin gene (GenBank: EU100952). Relative expression levels were calculated using the method [38]. All RT-PCR experiments were done with three replicates.

Exon skipping identification.

Putative exon skipping events were identified by using GESS from RNA-seq data and each skipping event was recorded in gff3 format compatible with MISO. Thus, expressions of exon skipping events were evaluated by MISO (exon-centric” analysis) using GESS output results [3940]. Exon skipping events were visualized using Sashimi plot (http://miso.readthedocs.org/en/fastmiso/sashimi.html). Upstream and downstream primers were designed based on our assembly results for downstream PCR validation [41]. PCR was carried out by the following procedure: 94°C for 2 min, followed by 37 cycles (94°C for 30 sec, 58°C for 30 sec, and 72°C for 30 sec), and finally 72°C for 10 min. The PCR products were examined by 1% agarose gel electrophoresis.

DNA-seq analysis process.

Sequencing reads were aligned to the Bxylophilus genome using Burrows-wheeler Aligner [42]. Duplicates were removed by Picard (http://broadinstitute.github.io/picard/). Putative SNPs and Indels were called by samtools mpileup with–ugBDS option and freebayes, respectively [43]. Then, VCFtools was applied to remove Indels and generate genotypes matrix. Population splitting tree was generated by Treemix with default parameter [44]. Selected SNPs sites were validated by both PCR and clone sequencing. PCR was carried out by the following procedure: 94°C for 2 min, followed by 35 cycles (94°C for 30 sec, 50°C for 30 sec, and 72°C for 30 sec), and finally 72°C for 10 min. All PCR products were then cloned into a pMD19-T vector (Takara, Dalian) and sent for sequencing (20 clones per SNP sites).

Results

The virulence of all Bxylophilus strains were evaluated by inoculation tests (see materials and methods). 34 days after inoculation, 6 strains (AW5, YW4, GZ1, JL1, HE2, JC1) showed DSI values lower than 50 while another 6 strains (SG4, SC2, AN5, AMA3, AA3, ZL1) showed DSI values higher than 50 (S1 Table). The enzyme digestion results indicated that all Bxylophilusstrains shared the same band distributions which were different from the negative control (S1 Fig). Therefore, all Bxylophilus strains used in this study were not contaminated with other nematode species. Thus, three SV Bxylophilus strains (AMA3, DSI: 100; AA3, DSI: 87.5; and ZL1, DSI: 79.2) with highest DSI values and one WV strain (YW4, DSI: 4.2) with lowest DSI value were selected to perform transcriptome and genome sequencing while other additional strains were used for experimental validation.

Genome based transcript assembly and annotation

After barcode removal and quality control, ~8G paired end data with 100bp read lengths per sample were obtained. Overall, 78% of the RNA-seq data were successfully aligned to the Bxylophilus genome. Then, a non-redundant transcript assembly was generated with 20,465 transcripts derived from 17,199 genes. The length distributions of assembled transcripts mostly ranged from 100bp to 2.5kb, and seven transcripts were identified with their length over 20kb (S2A Fig). A de novo assembly approach using RNA-seq alone was also applied in this research to evaluate our assembly result. The transcript lengths of de novo assembly were obviously shorter than the genome based method and YW4 assembly was poorly assembled without the reference genome (S2A Fig).
Transcripts annotations indicated that only 40% of the assembled transcripts (8304) could be assigned with known functions. The most enriched GO terms were nematode development, protein binding and hydrolase activity (S2B Fig).

Identification of differentially expressed transcripts

Two popular gene quantification packages, Cuffdiff and DESeq, were applied to detect differentially expressed (DE) transcripts [2945]. Finally, 238 overlapped DE transcripts were identified using the two packages together (Pvalue<0.05). To better supported our findings, additional strains (DSI>50: SG4, SC2, AN5; DSI<50: HE2, GZ1, AW5, JC1, JL1) were also included in qPCR validation of four up and down regulated transcripts (Fig 1A). The results suggested that up regulated transcripts TCONS_00004445 and TCONS_00009924 showed higher expression levels within SV strains. As for down regulated transcripts, TCONS_00006019 and TCONS_00014457 from WV showed more than two times higher expressions than those from SV strains. Other additional strains with DSI<50 shared the same expression patterns as WV strains while strains with DSI>50 showed the same expression patterns as SV strains. Functional annotation revealed that DE transcripts were mainly involved in nematode reproduction, development, and oxidoreductase activities (Fig 1B). The hierarchy clustering results of DE transcripts showed that SV strains were grouped together rather than the WV strain (S3 Fig).
thumbnail
Fig 1. Validation and GO enrichment analysis of differentially expressed transctipts.
(a) qPCR validation of four selected transcripts. (b) GO enrichment results of differentially expressed transcripts (Pvalue<0.05).

Differentially expressed exon and exon-skipping event detection

Here, we tried to use RNA-seq data to observe the expression change of certain exon and exon-skipping event [36]. Expression profiling of exons revealed that there were 75 genes containing 84 DE exons (S2 Table). For instance, XLOC_006174, known as Bx-eng-1 gene was not identified as a DE gene but contained two DE exons (Fig 2A). GO enrichment analysis indicated transcripts contained those DE exons were mainly associated with functions like: growth rate regulation and larval development (Fig 2B).
thumbnail
Fig 2. Exon-skipping events and differentially expressed exons found in Bxylophilus.
(a) Visualization of differentially expressed exons in Bx-eng-1 gene (highlighted in red). (b) GO enrichment results of differentially expressed exons (P-value<0.05). (c) Venn-diagram showing the predicted exon-skipping events. (d) Sashimi plot of exon-skipping event in XLOC_007524. (e) Experimental validation of exon-skipping events using PCR amplification. (lane 1 indicated exon-skipping events found in XLOC_002568, lane 2: XLOC_007524, lane 3: XLOC_002051, lane 4: XLOC_001151, lane 5: XLOC_000101)
Meanwhile, 63, 65, 53 and 56 exon-skipping candidates were identified from AA3, AMA3, ZL1 and YW4, respectively, using RNA-seq alone (Fig 2C). Among them, 35 recurrent exon-skipping candidates were detected in all nematode strains. Detailed information for all identified exon-skippings was presented in S3 Table and visualized using sashimi plot (Fig 2D). Unfortunately, we were not able to find any DE exon-skipping events after comparing SV with WV strains. Five exon-skipping events from XLOC_002568, XLOC_007524, XLOC_002051, XLOC_001151 and XLOC_000101) were validated by PCR amplification (Fig 2E).

Screening for SNPs and allele specific expressions

Samtools (http://samtools.sourceforge.net/) and freebayes (https://github.com/ekg/freebayes) packages were used together for searching SNPs to ensure the reliability of the results. Finally, 310,014 overlapped SNPs sites were detected after merging the output from these two packages. Among them, 82,577 (26.6%) of SNPs were located in exons while 227,437 (73.4%) were located in introns or other unannotated regions. The population splitting tree generated based on SNP allele frequency provided a good support for all samples used in this study. WV strains were separated from SV strains (Fig 3). After that, 117 SNPs were selected as possible markers to distinguish these two forms based on genotype variations. These 117 SNPs shared uniform genotypes among SV but exhibited other genotypes in WV. In addition, 45 of them were located in exons and 72 of them located in introns (S4 Table). Particularly, we found there were four SNPs which could undergo allele specific expressions since the genome variations were not found in corresponding transcripts (Fig 4A and 4BTable 1). Moreover, those allele specific expressions would affect the target reconition of miR-47 which was identified in our previous research (Fig 4C) [46]. Theoretically, it would be more reasonable to test those markers with additional Bxylophilus strains. Thus, we included additional strains to ensure the prediction by amplifying and sequencing those particular four SNPs from both genome and transcriptome levels. Both PCR and clone sequencing were used to verify SNPs sites (Figs 4Aand 5S4 Fig), while PCR sequencing alone was used to prove the corresponding allele specific expressions using cDNA (Fig 4A and 4B). All sequencing results indicated that additional Bxylophilus strains with DSI <50 shared the same genotypes as WV strains while Bxylophilus strains with DSI >50 shared the same genotypes as SV strains.
thumbnail
Fig 3. Population splitting tree generated by Treemix based on SNP allele frequency.
thumbnail
Fig 4. Sanger sequencing results of four SNPs with allele specific expressions found in Bxylophilus.
(a) Sequencing chromatograms of SNPs sites amplified from genomic DNA (SNPs sites are highlighted in yellow) (b) Sequencing chromatograms of SNPs sites amplified from cDNA (allele specific expressions are highlighted in yellow). (c) miR-47 targeting region interrupted by allele specific expressions, (red indicated the seed region of miR-47, green indicated the nucleotides underwent allele specific expressions).
thumbnail
Fig 5. Sanger sequencing results of additional strains indicated the validity of SNPs identification.
(SNPs are highlighted in yellow).

Discussion

All subsequent analyses on DE transcripts, DE exons and splicings required reliable transcript assembly. Previous research on the Bxylophilus transcriptome reported a transcript assembly comprised 23,765 mRNA contigs with maximum length of 5,847 using de novo methods [19]. Compared with the early de novo assembly, the genome-based transcript assembly reduced the transcripts number to 20,465 and increased the maximal length to 24,372 (S2A Fig). In addition, there were 904 transcripts with lengths longer than 5847. Successful amplifications of predicted transcripts and exon-skipping events demonstrated the accuracy of our assembly (Figs 1A and 2E). Consequently, this transcript assembly could be widely used for relevant research on Bxylophilus.
Previous study reported that the virulence level of Bxylophilus can be estimated by investigating the reproductive and growth ability on Bcinerea or in branch sections of Pthunbergii trees [11]. Consequently, it is reasonable to find that DE transcripts were mainly involved with growth regulation (Fig 1BS5 Table). This discovery supported the fact that different nematode virulence strains may have different growth abilities. Meanwhile, oxidoreductase activity was identified as another enriched function in DE transcripts (Fig 1B). Recent studies indicated that more than ten anti-oxidant proteins were identified in the secretome of Bxylophilus. The secreted anti-oxidant enzymes would play an important role in protecting Bxylophilus itself from oxygen free radicals in the pine tree [347]. Another study also proved that different virulent Bxylophilus exhibited different tolerance to oxidative stress which is crucial component in host defense mechanisms [16]. Thus, the up regulation of DE transcripts, for instance: TCONS_00004445 and TCONS_00009924, suggested a more dynamic oxidoreductase activity within SV strains and this was in line with the above conclusions (Fig 1AS5 Table). In addition, up-regulated transcript TCONS_00010273 was annotated as Proteinase inhibitor I29 which had been widely detected in the Bxylophilussecretome [47]. It contained inhibitors of cysteine peptidases while the plant cysteine peptidases were involved in the regulation of the defense system against pathogenic microbes and nematodes [4849]. Therefore, the I29 inhibitor could possibly target the plant cysteine peptidases and fight against the plant defense system. Higher expression of this transcript may facilitate the pine tree infection process in SV strains.
Transcripts are composed of exons and expressions of exons could be more complicated [50]. The differential usage of exons between these two forms could cause the change of protein sequences and thus lead to certain functional changes. However, descriptions on DE exons seemed to have been missed in previous research on Bxylophilus. Here, we found that most DE exons were found within transcripts annotated as nematode growth regulation and reproductive ability (Fig 2B). The result was also consistent with the conclusion that different growth rates were found between Bxylophilus strains with different virulence. Furthermore, we also attempted to detect around 60 exon-skipping events which had not been reported before (Fig 2C). Unfortunately, we did not observe any DE exon-skipping evens between different virulence strains, but it could be a new perspective to investigate the functional changes of the PWN. We may get some interesting results with the help of deeper sequencing coverage or even more complete assembled and annotated genome. Nevertheless, it could be a new perspective to study this important parasite.
One of the crucial applications of DNA-seq is to investigate genetic variations between individuals using whole-genome sequencing [51]. Meanwhile, SNPs are favorable markers for many applications in population ecology, evolution and conservation genetics [52]. In this research, a total of 310,014 SNPs were found in Bxylophilus [3]. Similar research on pine wood nematode SNPs detection in Japan detected many more SNPs sites than our results [20]. This could be explained by different strains used for analysis since we used nematode strains isolated from China rather than Japan. Also, quite stringent parameters used in our SNP detection pipeline could be another main reason for the reduction of SNPs (see material and methods for detail). Despite that, both of the studies proved that more than 95% of the SNPs were homozygous and more than 60% of the SNPs were located within intergenic and intron regions. Importantly, we reported 117 potential SNPs markers to classify Bxylophilus strains with different virulence after comparing the genotypes of SV and WV strains. However, it seemed that the level of diversity in the Bxylophilus genome was high but it had been observed in other hyper-diverse organisms. Previous studies indicated that organisms with large population size, short generation times, small body sizes were more likely to be hyper-diverse[53]. Bxylophilus was known to have all aforementioned features and it was possible to find some sequence polymorphisms among different Bxylophilus populations in a local area. Future study should be focused on using more nematode strains to calibrate our results. However, those potential markers will provide another possible way for researchers to evaluate nematode virulence other than using traditional inoculation tests.
Here, four allele specific expressions located in TCONS_00014322 were observed for the first time in SV strains. Interestingly, the seed region of miR-47 was complementary to 3’ region of TCONS_00014322 while two allele specific expressions also resided in this target region (Fig 4C). It is known that the RNA-induced silencing complex recognized their target messages based on perfect complementarity between the miRNAs and their target genes [54]. Consequently, these two allele specific expressions ensured that miR-47 could successfully target TCONS_00014322 (Fig 4C). Possibly, those allele specific expressions may alter the recognition site of miRNA to conduct post transcriptional regulation.

Conclusions

Our results demonstrated that the Bxylophilus SV and WV strains showed dissimilar expression patterns on both the transcripts and exon levels. The most significant variation between the two forms was observed in growth, reproduction and oxidoreductase activities. The establishment of putative SNPs markers will provide potential methods to distinguish these two nematode forms on the molecular level. This will assist researchers to better diagnose this nematode species and facilitate the control of pine wilt disease.

Supporting Information

S1 Fig.TIF
(marker: DL 1000)
(TIF)

S1 Fig. ITS-PCR-RFLP analysis of the Bxylophilus and Bmucronatus strains.

(marker: DL 1000)
doi:10.1371/journal.pone.0156040.s001
(TIF)

S2 Fig. A general overview of Bxylophilus transcript assembly and annotation.

(a) Length distributions of different assembly methods. (b) Blast2go annotation results of Bxylophilus transcript assembly.
doi:10.1371/journal.pone.0156040.s002
(TIF)

S3 Fig. Hierarchy clustering of Bxylophilus differentially expressed transcripts.

doi:10.1371/journal.pone.0156040.s003
(TIF)

S4 Fig. Clone sequencing of Bxylophilus strains for SNPs verification.

doi:10.1371/journal.pone.0156040.s004
(TIF)

S1 Table. Disease severity index of Bxylophilus based on Pthunbergii inoculation tests.

doi:10.1371/journal.pone.0156040.s005
(XLSX)

S2 Table. Detailed information of differentially expressed exons generated by DEXSeq.

doi:10.1371/journal.pone.0156040.s006
(PDF)

S3 Table. An overview of all exon-skipping events based on RNA-seq data.

doi:10.1371/journal.pone.0156040.s007
(XLS)

S4 Table. Genomic coordinates of SNPs which showed different genotypes between strongly and weakly virulent strains.

doi:10.1371/journal.pone.0156040.s008
(XLS)

S5 Table. GO enrichment analysis of differentially expressed transcripts and exons.

doi:10.1371/journal.pone.0156040.s009
(XLSX)

Acknowledgments

We are very grateful to Dr. James LaMondia, The Connecticut Agricultural Experiment Station, USA and Dr. Kai Wang, University of Southern California for reviewing the manuscript. We thank Jiang-Chuan Wang, Lan Shen and Bin-Yan Wu at Nanjing Forestry University for carrying out some of the experiments.

Author Contributions

Conceived and designed the experiments: JY XD XW. Performed the experiments: XD SL BN. Analyzed the data: XD. Contributed reagents/materials/analysis tools: SL. Wrote the paper: XD DL.

References

  1. 1.Jones JT, Moens M, Mota M, Li H, Kikuchi T. Bursaphelenchus xylophilus: opportunities in comparative genomics and molecular host-parasite interactions. Mol Plant Pathol. 2008;9(3):357–368. doi: 10.1111/j.1364-3703.2007.00461.x. pmid:18705876
  2. 2.Futai K. Pine Wood Nematode, Bursaphelenchus xylophilus. Annu Rev Phytopathol. 2013;51:61–83. doi: 10.1146/annurev-phyto-081211-172910. pmid:23663004
  3. 3.Kikuchi T, Cotton JA, Dalzell JJ, Hasegawa K, Kanzaki N, McVeigh P, et al. Genomic insights into the origin of parasitism in the emerging plant pathogen Bursaphelenchus xylophilus. Plos Pathog. 2011; doi: 10.1371/journal.ppat.1002219
  4. 4.Wang Z, Wang CY, Fang ZM, Zhang DL, Liu L, Lee MR, et al. Advances in research of pathogenic mechanism of pine wilt disease. Afr J Microbiol Res. 2010;4(6):437–442. 
  5. 5.Zhao BG, Wang HL, Han SF, Han ZM. Distribution and pathogenicity of bacteria species carried by Bursaphelenchus xylophilus in China. Nematology. 2003;5:899–906. doi: 10.1163/156854103773040817 
  6. 6.Zhang Q, Bai G, Yang WB, Li HY, Xiong HL. Pathogenic cellulase assay of pine wilt disease and immunological localization. Biosci Biotech Bioch. 2006;70(11):2727–2732. doi: 10.1271/bbb.60330 
  7. 7.Utsuzawa S, Fukuda K, Sakaue D. Use of magnetic resonance Microscopy for the nondestructive observation of xylem cavitation caused by pine wilt disease. Phytopathology. 2005;95(7):737–743. doi: 10.1094/PHYTO-95-0737. pmid:18943004
  8. 8.Bolla RI, Boschert M. Pinewood nematode species complex: interbreeding potential and chromosome number. J Nematol. 1993;25(2):227–238. pmid:19279762
  9. 9.Akiba M, Ishihara M, Sahashi N, Nakamura K, Ohira M, Toda T. Virulence of Bursaphelenchus xylophilus isolated from naturally infested pine forests to five resistant families of Pinus thunbergii. Plant Dis. 2012;96(2):249–252. doi: 10.1094/pdis-12-10-0910 
  10. 10.Takemoto S, Kanzaki N, Futai K. PCR-RFLP image analysis: a practical method for estimating isolate-specific allele frequency in a population consisting of two different strains of the pinewood nematode, Bursaphelenchus xylophilus (Aphelenchida: Aphelencoididae). Appl Entomol Zool. 2005;40(3):529–535. doi: 10.1303/aez.2005.529 
  11. 11.Aikawa T, Kikuchi T. Estimation of virulence of Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) based on its reproductive ability. Nematology. 2007;9:371–377. doi: 10.1163/156854107781352007 
  12. 12.Asai E, Futai K. Effects of inoculum density of pinewood nematode on the development of pine wilt disease in Japanese black pine seedlings pretreated with simulated acid rain. Forest Pathol. 2005;35(2):135–144. doi: 10.1111/j.1439-0329.2004.00396.x 
  13. 13.Henkle-Duhrsen K, Kampkotter A. Antioxidant enzyme families in parasitic nematodes. Mol Biochem Parasit. 2001;114(2):129–142. doi: 10.1016/s0166-6851(01)00252-3 
  14. 14.D'Autreaux B, Toledano MB. ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis. Nat Rev Mol Cell Bio. 2007;8(10):813–824. doi: 10.1038/nrm2256 
  15. 15.Quan LJ, Zhang B, Shi WW, Li HY. Hydrogen peroxide in plants: A versatile molecule of the reactive oxygen species network. J Integr Plant Biol. 2008;50(1):2–18. doi: 10.1111/j.1744-7909.2007.00599.x. pmid:18666947
  16. 16.Vicente CSL, Ikuyo Y, Shinya R, Mota M, Hasegawa K. Catalases induction in high virulence pine wood nematode Bursaphelenchus xylophilus under Hydrogen Peroxide-Induced stress. Plos One. 2015; doi: 10.1371/journal.pone.0123839
  17. 17.van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30(9):418–426. doi: 10.1016/j.tig.2014.07.001. pmid:25108476
  18. 18.Bell GD, Kane NC, Rieseberg LH, Adams KL. RNA-seq analysis of allele-specific expression, hybrid effects, and regulatory divergence in hybrids compared with their parents from natural populations. Genome biology and evolution. 2013;5(7):1309–1323. doi: 10.1093/gbe/evt072. pmid:23677938
  19. 19.Yan X, Cheng XY, Wang YS, Luo J, Mao ZC, Ferris VR, et al. Comparative transcriptomics of two pathogenic pinewood nematodes yields insights into parasitic adaptation to life on pine hosts. Gene. 2012;505(1):81–90. doi: 10.1016/j.gene.2012.05.041. pmid:22705985
  20. 20.Palomares-Rius JE, Tsai IJ, Karim N, Akiba M, Kato T, Maruyama H, et al. Genome-wide variation in the pinewood nematode Bursaphelenchus xylophilus and its relationship with pathogenic traits. BMC genomics. 2015; doi: 10.1186/s12864-015-2085-0. 
  21. 21.Espada M, Silva Ana Cláudia, Eves-van den Akker Sebastian, Cock Peter J.A., Mota Manuel & Jones John T.. Identification and characterization of novel effectors from the pinewood nematode Bursaphelenchus xylophilus. Mol Plant Pathol. 2015;17(2):286–295. doi: 10.1111/mpp.12280. pmid:25981957
  22. 22.Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11(8):533–538. doi: 10.1038/nrg2815. pmid:20567245
  23. 23.Costa V, Aprile M, Esposito R, Ciccodicola A. RNA-Seq and human complex diseases: recent accomplishments and future perspectives. Eur J Hum Genet. 2013;21(2):134–142. doi: 10.1038/ejhg.2012.129. pmid:22739340
  24. 24.Aikawa T, Kikuchi T, Kosaka H. Demonstration of interbreeding between virulent and avirulent populations of Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) by PCR-RFLP method. Appl Entomol Zool. 2003;38(4):565–569. doi: 10.1303/aez.2003.565 
  25. 25.Viglierchio DR, Schmitt RV. On the methodology of nematode extraction from field samples: baermann funnel modifications. J Nematol. 1983;15(3):438–444. pmid:19295830
  26. 26.Zhu LH, Ye JR, Negi S, Xu XL, Wang ZL, Ji JY. Pathogenicity of aseptic Bursaphelenchus xylophilus. Plos One. 2012; doi: 10.1371/journal.pone.0038095
  27. 27.Stewart CN Jr., Via LE. A rapid CTAB DNA isolation technique useful for RAPD fingerprinting and other PCR applications. BioTechniques. 1993;14(5):748–750. pmid:8512694
  28. 28.Iwahori H TK, Kanzaki N, Izui K, Futai K. PCR-RFLP and sequencing analysis of ribosomal DNA of Bursaphelenchus nematodes related to pine wilt disease. Fundam Appl Nematol. 1998;21:655–666. 
  29. 29.Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–578. doi: 10.1038/nprot.2012.016. pmid:22383036
  30. 30.Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013; doi: 10.1186/gb-2013-14-4-r36. 
  31. 31.Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31(1):46–53. doi: 10.1038/nbt.2450. pmid:23222703
  32. 32.Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–3676. pmid:16081474 doi: 10.1093/bioinformatics/bti610 
  33. 33.Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014; doi: 10.1093/nar/gkt1223. 
  34. 34.Du Z, Zhou X, Ling Y, Zhang Z, Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010; doi: 10.1093/nar/gkq310. 
  35. 35.Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; doi: 10.1186/gb-2010-11-10-r106. 
  36. 36.Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22(10):2008–2017. doi: 10.1101/gr.133744.111. pmid:22722343
  37. 37.Tang Z, Zhang L, Xu C, Yuan S, Zhang F, Zheng Y, et al. Uncovering small RNA-mediated responses to cold stress in a wheat thermosensitive genic male-sterile line by deep sequencing. Plant physiology. 2012;159(2):721–738. doi: 10.1104/pp.112.196048. pmid:22508932
  38. 38.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(-Delta Delta C) method. Methods. 2001;25(4):402–408. pmid:11846609 doi: 10.1006/meth.2001.1262 
  39. 39.Ye ZQ, Chen Z, Lan X, Hara S, Sunkel B, Huang THM, et al. Computational analysis reveals a correlation of exon-skipping events with splicing, transcription and epigenetic factors. Nucleic Acids Res. 2014;42(5):2856–2869. doi: 10.1093/nar/gkt1338. pmid:24369421
  40. 40.Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010;7(12):1009–1015. doi: 10.1038/nmeth.1528. pmid:21057496
  41. 41.Shahjahan RM, Hughes KJ, Leopold RA, DeVault JD. Lower incubation temperature increases yield of insect genomic DNA isolated by the CTAB method. BioTechniques. 1995;19(3):332–334. pmid:7495537
  42. 42.Laurent L, Wong E, Li G, Huynh T, Tsirigos A, Ong CT, et al. Dynamic changes in the human methylome during differentiation. Genome Res. 2010;20(3):320–331. doi: 10.1101/gr.101907.109. pmid:20133333
  43. 43.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–2079. doi: 10.1093/bioinformatics/btp352. pmid:19505943
  44. 44.Pickrell JK, Pritchard JK. Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data. Plos Genet. 2012; doi: 10.1371/journal.pgen.1002967
  45. 45.Anders S, McCarthy DJ, Chen YS, Okoniewski M, Smyth GK, Huber W, et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc. 2013;8(9):1765–1786. doi: 10.1038/nprot.2013.099. pmid:23975260
  46. 46.Ding X, Ye J, Wu X, Huang L, Zhu L, Lin S. Deep sequencing analyses of pine wood nematode Bursaphelenchus xylophilus microRNAs reveal distinct miRNA expression patterns during the pathological process of pine wilt disease. Gene. 2015;555(2):346–356. doi: 10.1016/j.gene.2014.11.030. pmid:25447893
  47. 47.Shinya R, Morisaka H, Kikuchi T, Takeuchi Y, Ueda M, Futai K. Secretome analysis of the pine wood nematode Bursaphelenchus xylophilus reveals the tangled roots of parasitism and its potential for molecular mimicry. Plos One. 2013; doi: 10.1371/journal.pone.0067377
  48. 48.van der Hoorn RAL, Jones JD. The plant proteolytic machinery and its role in defence. Curr Opin Plant Biol. 2004;7(4):400–407. pmid:15231262 doi: 10.1016/j.pbi.2004.04.003 
  49. 49.Grudkowska M, Zagdanska B. Multifunctional role of plant cysteine proteinases. Acta Biochim Pol. 2004;51(3):609–624. pmid:15448724
  50. 50.Wan L, Sun FZ. CEDER: Accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq. IEEE ACM T Comput Biol Bioinform. 2012;9(5):1281–1292. doi: 10.1109/tcbb.2012.83 
  51. 51.Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 2009;19(6):1124–1132. doi: 10.1101/gr.088013.108. pmid:19420381
  52. 52.Morin PA, Luikart G, Wayne RK, Grp SW. SNPs in ecology, evolution and conservation. Trends Ecol Evol. 2004;19(4):208–216. doi: 10.1016/j.tree.2004.01.009 
  53. 53.Cutter AD, Jovelin R, Dey A. Molecular hyperdiversity and evolution in very large populations. Molecular ecology. 2013;22(8):2074–2095. doi: 10.1111/mec.12281. pmid:23506466
  54. 54.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281–297. pmid:14744438 doi: 10.1016/s0092-8674(04)00045-5 


For further details log on website :
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0156040

No comments:

Post a Comment