搜索

Overrepresentation Analyses of Differentially Expressed Genes in the Smut Fungus Ustilago bromivora during Saprophytic and in planta Growth
腐生与植物内的黑粉病真菌-麦雀生黑粉菌在生长期间高差异表达基因分析   

评审
匿名评审
下载 PDF 引用 收藏 提问与回复 分享您的反馈 Cited by

本文章节

Abstract

We have established the Ustilago bromivoraBrachypodium spp. interaction as a new model pathosystem for biotrophic fungal plant infections of the head smut type (Rabe et al., 2016). In this protocol, the methodology used for comparing gene expression between saprophytic and in planta growth of the fungus is described. The experimental and analytical pipeline, how next generation RNA sequencing (Illumina RNA-Seq) analysis can be used to obtain lists of genes significantly up or down regulated in planta in comparison to axenic culture is given. Furthermore, different methods to identify functional categories that are over- or under-represented among specific classes of genes are presented.

Keywords: Plant infection(植物感染), Biotrophic plant pathogens(活体营养植物病原体), Fungal pathogens(真菌病原体), Smuts(黑粉病), Ustilago bromivora(麦雀生黑粉菌), RNA-seq(RNA-seq), Differential expression(差异表达), over/under representation analysis(过度/不足代表分析)

Background

RNA deep sequencing (RNA-Seq) is a powerful and versatile tool to gain insights into the responses of cells and organisms to environmental changes and their adaptations to new developmental stages. A striking change of life situation comes with the switch from yeast-like growth to filamentous, pathogenicity associated growth modes in non-obligate pathogens. We studied this switch in the biotrophic fungal plant pathogen Ustilago bromivora (Rabe et al., 2016). RNA-Seq from infected tissue is a special situation, since reads from both–the host and the pathogen–will be identified. Here necessary considerations are described. These include the sequencing depth required to sufficiently cover the pathogen in the host tissue, and the methods used to align and quantify the resulting mixed pool of reads. Over/underrepresentation analysis (ORA) is a method to link expression changes to potential biological responses by looking if certain classes of transcripts respond in a concerted way. Three methodologies are described that can be used to statistically test for over- or underrepresentation of classes of transcripts: The first method tests ORA individually for defined classes of interest, such as predicted secreted proteins, using Fisher exact test (example for R implementation given). The other two approaches are ‘explorative’ analyses that test over/underrepresentation across all functional classes defined in a given functional annotation framework (FunCat or Mapman annotation).

Materials and Reagents

  1. Pipetman Diamond tips, D200 (Gilson, catalog number: F161931 )
  2. Pipetman Diamond tips, D1000 (Gilson, catalog number: F161671 )
  3. 50 ml sterile disposable vial (SARSTEDT, catalog number: 62.547.254 )
  4. 1.5 ml microcentrifuge tubes (SARSTEDT, catalog number: 72.690.001 )
  5. Micro-homogenizer (Carl Roth, catalog number: K994.1 )
  6. Glycerol anhydrous (Applichem, catalog number: A1123,1000 )
  7. Liquid nitrogen
  8. Sodium hypochlorite ~10% (Honeywell International, catalog number: 71696-2.5L )
  9. Hydrochloric acid (HCl) 37% (Applichem, catalog number: 131020.1211 )
  10. TRIzol reagent (Thermo Fisher Scientific, InvitrogenTM, catalog number: 15596026 )
  11. TURBO DNA-free Kit (Thermo Fisher Scientific, InvitrogenTM, catalog number: AM1907 )
  12. Ribo-Zero rRNA Removal Kit (Plant) (Illumina, catalog number: MRZPL1224 )
  13. NEB Next Ultra RNA Library Prep Kit (New England Biolabs, catalog number: E7530S )
  14. Ampure XP beads (Beckman Coulter, catalog number: A63882 )
  15. Agilent RNA 6000 Nano Kit for Bioanalyzer (Agilent Technologies, catalog number: 5067-1511 ) or Standard Sensitivity RNA Analysis Kit (15 nt) (Advanced Analytical Technologies, catalog number: DNF-471 )
  16. Illumina PE Cluster Kit (Illumina, catalog number: FC-401-4003 )
  17. Illumina 250 cycle SBS reagents (Illumina, catalog number: PE-401-4001 )
  18. Potato dextrose broth (BD, catalog number: 254920 )
  19. Standard potting soil (Topfsubstrat ED63, Einheitserde, catalog number: SP ED63 T, obtained from GBC-Gartenbauzentrum Schwechat, catalog number: 013224 )
  20. Perlite (Perlite Premium 2-6 mm, Gramoflor, obtained from GBC-Gartenbauzentrum Schwechat, catalog number: 079568 )
  21. Silica sand (Quarzsand 0.5-2 mm, min2C, obtained from GBC-Gartenbauzentrum Schwechat, catalog number: 005989 )
  22. Germination soil (Aussaaterde, Huminsubstrat N3, Neuhaus, Klasmann-Deilmann, obtained from GBC-Gartenbauzentrum Schwechat, catalog number: 001318 )
  23. Potato dextrose liquid medium (PD medium) (see Recipes)
  24. Soil mix (see Recipes)

Equipment

  1. Pipetman P1000 (Gilson, catalog number: F123602 )
  2. Pipetman P200 (Gilson, catalog number: F123601 )
  3. Centrifuge for 50 ml disposable vials (e.g., Eppendorf, model: 5810 R )
  4. Spectral photometer capable of measuring OD at 600 nm
  5. ND-1000 NanoDrop Spectrophotometer (Thermo Fisher Scientific, model: NanoDrop 1000 )
  6. 2100 Bioanalyzer (Agilent Technologies, model: 2100 Bioanalyzer ) or Fragment Analyzer 12 (Advanced Analytical Technologies, model: Fragment Analyzer 12)
  7. Illumina HiSeq2500 instrument (Illumina, model: HiSeq® 2500 )
  8. Fume hood

Software

  1. FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) (freely available)
  2. Cutadapt (Martin, 2011) (freely available)
  3. Kallisto (Bray et al., 2016) (freely available)
  4. R statistical environment (R Development Core Team, 2012) (freely available)
    For those not familiar with R, the following resources may be helpful:
    https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
    https://onlinecourses.science.psu.edu/statprogram/r
    https://www.youtube.com/watch?v=7cGwYMhPDUY
  5. DESeq2 R package (Love et al., 2014) (freely available)
  6. FunCat workflow (Ruepp et al., 2004) implemented on the Pedant home page (http://mips.helmholtz-muenchen.de/funcatDB/index_update.html) (freely available for publicly released species)
  7. Mercator online tool (http://www.plabipd.de/portal/web/guest/mercator-sequence-annotation) (freely available)
  8. Pageman tool (Usadel et al., 2006), part of the Mapman software suit: http://mapman.gabipd.org/web/guest/mapman (freely available)

Procedure

  1. Obtain samples of saprophytically grown U. bromivora
    1. To obtain samples of saprophytically grown U. bromivora, PD medium (see Recipes) was inoculated from a mating type 1 strain (UB1) glycerol stock kept at -80 °C. The glycerol stock was produced by mixing 1 ml UB1 overnight culture with 1 ml 50% glycerol. For the original isolation of U. bromivora spore lines please refer to (Bosch and Djamei, 2017).
    2. Cell are grown at 21 °C and 200 rpm to an OD600 nm = ~0.8 in three independent biological replicates. Fungal material is sampled by centrifugation in 50 ml vials at 1,200 x g for 5 min. The supernatant is carefully poured off and the pellet is shock frozen in liquid nitrogen. If other species are grown, it can be the case that the pellet is less stable and the supernatant should be pipetted off.
  2. Obtain samples of the fungus growing in planta
    1. To obtain samples of the fungus growing in planta, caryopses of Brachypodium hybridum Bd28 are gas sterilized by putting them into a closed exicator together with two 50 ml tubes each with 25 ml 5% sodium hypochlorite solution + 0.75 ml 37% HCl for 2 h.
    2. After gas sterilization, seeds are left in a 50 ml tube in a fume hood without cover for 1 h, so that any residual chlorine gas can evaporate.
    3. The seeds are then soaked in sterile filtered tap water for 1-2 h, and germinated for 2-3 weeks at 4 °C in the dark.
    4. The seedlings are subsequently infected with U. bromivora spore solution. The spore solution is obtained by grinding spore sori filled spikelets of infected plants in a small amount (e.g., 1 ml) of filtered tap water with a micro homogenizer in a disposable vial with a V-shaped bottom (e.g., 1.5 ml micro centrifuge tube). The seedlings are kept moistened (but not submerged) with spore solution at 4 °C for one week and are then planted in soil (see soil mix in the recipes section).
    5. 12 days after planting, plant stems are harvested and shock frozen in liquid nitrogen.
  3. RNA extraction
    a. RNA is extracted using TRIzol (Chomczynski and Sacchi, 2006) according to the manufacturer’s protocol.
    b. Residual DNA is removed with the TURBO DNA-free Kit according to the manufacturer’s instructions.
  4. Determination of RNA quality and quantity
    1. RNA quality should be verified: To determine RNA concentration and purity, the RNA should be measured with a NanoDrop spectrophotometer. A 260:280 ratio close to 2 and a 260:230 ratio of approximately 2-2.2 is desirable. A minimal amount of 100-200 ng RNA is required for subsequent library preparation. To determine that the isolated RNA is not significantly degraded, a Bioanalyzer or Fragment Analyzer with appropriate kits can be used (see Equipment). Measurements are conducted according to the manufacturer’s instructions. The RNA integrity number (RIN) provided by the Bioanalyzer software is not applicable to the in planta samples, since they contain plant and fungal rRNAs (example: see Figure 1). However, the Bioanalyzer or Fragment Analyzer plots should be manually evaluated for the following criteria: All rRNA peaks should be narrow and near symmetrically shaped and the baseline between peaks should be flat and close to 0.
    2. Alternatively, an RNA gel (1% agarose gel in TBE) can be used to assess RNA integrity, even though this method is less precise than using the Bioanalyzer/Fragment Analyzer. For high quality RNA samples from saprophytic growth two clear bands (rRNAs) and for in planta samples four bands should be seen, with almost no additional smear.


      Figure 1. Bioanalyzer plot for a high quality RNA sample extracted from U. bromivora infected Brachypodium tissue

  5. Library construction
    1. Before library preparation ribosomal RNA is removed from the samples using Ribo-Zero rRNA Removal Kit following manufacturer instructions.
    2. The libraries are prepared using the NEB Next Ultra RNA Library Prep Kit. Size selection is performed using Ampure XP beads. The Bioanalyzer/Fragment Analyzer is used to test the size distribution of the libraries, followed by qPCR to determine the correct concentration needed for cluster generation.
  6. The libraries are sequenced in paired end mode (PE125), using an Illumina HiSeq2500 instrument.
    Notes:
    1. An important consideration, when deciding on the sequencing depth, is sufficient coverage of pathogen transcripts in libraries derived from infected host tissue (see Notes).
    2. We sequenced the in planta samples to an average depth of 130,000,000 raw reads and the axenic culture samples to an average depth of 23,000,000 raw reads.
  7. The quality of the resulting reads is assessed using FastQC: Properties examined are the per-base quality to exclude sequencing problems like quality drop-off towards the end or failed cycles, kmer-distribution and overrepresented sequences to identify adapter dimers and short inserts and duplication rate to exclude overamplification or other library problems. Except for low amounts of reads showing Illumina adapter sequences on the 3’ end we did not experience any obvious problems. The adapter sequences are removed from the reads using cutadapt v1.4.2.

Data analysis

Figure 2 shows a flow diagram of the different steps involved in RNA-Seq data analysis, indicating the software tools used.


Figure 2. Workflow of RNA-Seq data analysis. Dark blue background: Input from lllumina RNA sequencing; Light blue: Sequencing quality control and read quantification; Green: Identification of differentially expressed genes; Orange: Three methods for over/under-representation analysis.

  1. Identification of differentially regulated genes
    1. The trimmed RNA-Seq reads are then quantified against the combined transcriptomes extracted from Brachypodium distachyon Bd21 (Bdistachyon_283_v2.1) and Ustilago bromivora UB1 annotations, with the Kallisto software, using default parameters. This constitutes a pseudo-alignment against the transcriptome. In cases where more than one splicing variant encoded by the same gene is identified (in our dataset 20 cases), the splicing variants can be treated and counted as individual genes in all subsequent analyses.
    2. Further analysis of the dataset can be conducted with the statistical environment R. For comparison of U. bromivora gene expression between the saprophytic and in planta growth conditions, only U. bromivora transcripts (i.e., those with a gene identifier starting with ‘UBRO’) are retained. Differential expression statistics are computed using the DESeq2 R package. To estimate the size factors between samples, the default assumption that the overall fungal expression levels are similar between all samples is used (estimateSizeFactors:type = ‘ratio’).
    3. Transcripts are considered significantly up- or downregulated in planta, if the log2-fold-change compared to axenic culture is > 2/< -2 and the Benjamini-Hochberg (Hochberg and Benjamini, 1990) corrected P-value is < 0.1.
      Additionally, to reliably assess downregulation in planta despite the lower coverage of fungal reads in the in planta samples, we required an average of at least 150 reads for a transcript in the axenic samples to consider it in planta downregulated. (See more details in the Notes section).

  2. Over/Underrepresentation analysis
    1. To test over-/underrepresentation (ORA) of transcript classes of interest among the in planta up- and downregulated transcripts, one of the following strategies can be used depending on the situation:
      1. For user-defined lists of transcripts (e.g., in our case the list of transcripts encoding for predicted secreted proteins), over/underrepresentation of the given class among the lists of significantly responding transcripts compared to the representation of the class among all predicted or expressed transcripts can be calculated using Fisher exact test in the R statistical environment. The following code can be used:

        input <- cbind(total=c(Nrtotal_secreted, Nrtotal non-secreted), in_planta_up=c(Nrin-planta up secreted, Nrin-planta up non-secreted))
        fisher.test(input)


        In the fisher.test() function the parameter alternative=”greater” or alternative=”less” can be added to test for only overrepresentation or only underrepresentation in cases where one of the two options can be precluded.
        Nrtotal_secreted .. the number of transcripts encoding for predicted secreted proteins among all predicted or expressed transcripts
        Nrtotal_non-secreted .. the number of transcripts encoding for predicted non-secreted proteins among all predicted or expressed transcripts
        Nrin-planta up secreted .. the number of transcripts encoding for predicted secreted proteins among those transcripts identified to significantly upregulated in-planta compared to saprophytic growth
        Nrin-planta up non-secreted .. the number of transcripts encoding for predicted non-secreted proteins among those transcripts identified to significantly upregulated in-planta compared to saprophytic growth.

      2. To systemically test for overrepresentation of functional classes among the transcripts significantly up or down regulated in planta, the available FunCat classification and ORA workflow for the predicted U. bromivora transcripts http://mips.helmholtz-muenchen.de/funcatDB/index_update.html can be used.
      3. Alternatively, the following workflow can be used for all species, also when FunCat annotation is not available:
        1. Use of the Mercator online tool to generate a functional annotation in the Mapman format–predicted transcripts have to be submitted in FASTA format.
        2. Use of the Pageman tool (integrated in the Mapman software suit http://mapman.gabipd.org/web/guest/mapman) to conduct ORA. We used Fisher test statistics and the p-values were corrected for multiple testing by the Benjamini-Hochberg algorithm.

Notes

  1. All sequencing raw data (.bam files) as well as counts of each U. bromivora gene in each sample from our experiment published in Rabe et al. (2016) can be downloaded from GeneExpressionOmnibus (GEO: http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE87751.
  2. Considerations concerning sequencing depth of samples from pathogen infected host tissue: The required sequencing depth will depend on both, the proportion of pathogen RNA compared to host RNA and the expected expression level of genes of interest. These properties may vary, depending on the time point during the infection cycle and the tissue studied. A quantitative real time (qRT)-PCR pre-assay to compare host against pathogen genomic marker can help to evaluate the ratio between host versus pathogen cells.
    Below we give some examples from our dataset that may help to estimate relevant properties and asses the reliability of expression differences observed for individual genes:
    Despite obtaining on average in total approximately 103,500,000 map-able reads in the in planta samples and only 13,800,000 in the axenic culture samples, i.e., ~7.5x more total reads, the in planta samples contain only an average of ~175,000 U. bromivora reads, compared to ~14,000,000 fungal reads in the samples of fungus grown in axenic culture. We thus obtained an approximately 80x better coverage of the axenic stage. This corresponds to having ~600x more plant reads compared to fungal reads in the mixed samples, a similar ratio to what we get when directly comparing the plant and fungal reads in the mixed samples. Having 80x lower coverage means that a transcript expressed equally in axenic culture and in planta that obtains 80 reads in axenic culture would typically obtain only one read in planta. Therefore, to reliably assess whether a transcript for which no reads are found in planta is downregulated compared to axenic culture, sufficient reads are required in the axenic culture sample. We defined a threshold of at least 150 reads (roughly 2x the difference in coverage) in axenic culture, additional to a significant P-value for the DESeq2 analysis, to consider a transcript for which no reads were obtained in planta as significantly downregulated in planta. Applying these filters, we identified 888 U. bromivora transcripts for which downregulation in planta could not be reliably assessed due to insufficient coverage.
  3. Suppliers and order numbers given for chemicals, consumables and instruments refer to the equipment used by us. In general, it can be exchanged for equivalent equipment from other suppliers.

Recipes

  1. Potato dextrose liquid medium (PD medium)
    24 g/L potato dextrose broth in deionized water
    Autoclaved
  2. Soil mix
    3:1:1:1 standard potting soil:perlite:silica sand:germination soil

Acknowledgments

This protocol is adapted from Rabe et al. (2016). We would like to thank all the people involved in the works for this protocol as well as the original publication that it is based upon. The research leading to these results received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. [EUP0012 ‘Effectomics’], the Austrian Science Fund (FWF): [P27429-B22, P27818-B22, I3033-B22], and the Austrian Academy of Science (OEAW).

References

  1. Bosch, J. and Djamei, A. (2017). Isolation of Ustilago bromivora strains from infected spikelets through spore recovery and germination. Bio Protoc 7(14): e2392.
  2. Bray, N. L., Pimentel, H., Melsted, P. and Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification.v Nat Biotechnol 34: 525-527.
  3. Chomczynski, P and Sacchi, N. (2006). The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nat Protoc 1(2): 581-585.
  4. Hochberg, Y and Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Stat Med 9(7): 811-818.
  5. Lohse, M., Nagel, A., Herter, T., May, P., Schroda, M., Zrenner, R., Tohge, T., Fernie, A. R., Stitt, M. and Usadel, B. (2014). Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data. Plant Cell Environ 37(5): 1250-1258.
  6. Love, M. I., Huber, W. and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12): 550.
  7. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet 17: 10-12.
  8. Rabe, F., Bosch, J., Stirnberg, A., Guse, T., Bauer, L., Seitner, D., Rabanal, F. A., Czedik-Eysenberg, A., Uhse, S., Bindics, J., Genenncher, B., Navarrete, F., Kellner, R., Ekker, H., Kumlehn, J., Vogel, J. P., Gordon, S. P., Marcel, T. C., Münsterkötter, M., Walter, M. C., Sieber, C. M., Mannhaupt, G., Güldener, U., Kahmann, R. and Djamei, A. (2016). A complete toolset for the study of Ustilago bromivora and Brachypodium sp. as a fungal-temperate grass pathosystem. eLife 5: 179-188.
  9. R Development Core Team. (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
  10. Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M. and Mewes, H. W. (2004). The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res 32(18): 5539-5545.
  11. Usadel, B., Nagel, A., Steinhauser, D., Gibon, Y., Bläsing, O. E., Redestig, H., Sreenivasulu, N., Krall, L., Hannah, M. A., Poree, F., Fernie, A. R and Stitt, M. (2006). PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments. BMC Bioinformatics 7: 535.

简介

我们已经建立了Ustilago bromivora - Brachypodium spp。 作为一种用于生物营养真菌植物感染头虱类型的新模型病理学的相互作用(Rabe等人,2016)。 在该方案中,描述了用于比较真菌的植物生长中的腐生菌和萌发之间的基因表达的方法。 给出了实验和分析流程,如何使用下一代RNA测序(Illumina RNA-Seq)分析来获得与无性培养相比在植物中显着上调或下调的基因的列表。 此外,提出了识别在特定类型的基因中过度或低于代表的功能类别的不同方法。
【背景】RNA深度测序(RNA-Seq)是一种功能强大和通用的工具,可以了解细胞和生物体对环境变化的反应及其对新发育阶段的适应性。生活状况的显着变化是从酵母样生长转向非特异性病原体的丝状,致病性相关生长模式。我们研究了生物营养型真菌植物病原体(Ustilago bromivora)(Rabe等人,2016)的这一转变。来自感染组织的RNA-Seq是一个特殊的情况,因为来自宿主和病原体的读数将被鉴定。这里描述必要的考虑。这些包括充分覆盖宿主组织中的病原体所需的测序深度,以及用于对准和量化所得到的混合读数池的方法。过度/不足表达分析(ORA)是一种通过查看某些类别的誊本是否以一致的方式进行响应来将表达变化与潜在的生物反应相关联的方法。描述了可用于统计学检验转录物类别的过度表达或代表性不足的三种方法:第一种方法使用Fisher精确检验(给出R实施例的示例),针对确定的感兴趣类别单独测试ORA,例如预测的分泌蛋白质。 。另外两种方法是“探索性”分析,用于在给定的功能注释框架(FunCat或Mapman注释)中定义的所有功能类之间进行测试/表示不足。

关键字:植物感染, 活体营养植物病原体, 真菌病原体, 黑粉病, 麦雀生黑粉菌, RNA-seq, 差异表达, 过度/不足代表分析

材料和试剂

  1. Pipetman钻石提示,D200(Gilson,目录号:F161931)
  2. 皮皮特钻石提示,D1000(Gilson,目录号:F161671)
  3. 50ml无菌一次性小瓶(SARSTEDT,目录号:62.547.254)
  4. 1.5ml微量离心管(SARSTEDT,目录号:72.690.001)
  5. 微均化器(Carl Roth,目录号:K994.1)
  6. 无水甘油(Applichem,目录号:A1123,1000)
  7. 液氮
  8. 次氯酸钠〜10%(Honeywell International,目录号:71696-2.5L)
  9. 盐酸(HCl)37%(申请,目录号:131020.1211)
  10. TRIzol试剂(Thermo Fisher Scientific,Invitrogen TM,目录号:15596026)
  11. TURBO DNA-免费试剂盒(Thermo Fisher Scientific,Invitrogen TM,目录号:AM1907)
  12. Ribo-Zero rRNA去除试剂盒(植物)(Illumina,目录号:MRZPL1224)
  13. NEB Next Ultra RNA文库准备工具(New England Biolabs,目录号:E7530S)
  14. Ampure XP珠(Beckman Coulter,目录号:A63882)
  15. 用于生物分析仪的Agilent RNA 6000纳米试剂盒(Agilent Technologies,目录号:5067-1511)或标准灵敏度RNA分析试剂盒(15 nt)(高级分析技术,目录号:DNF-471)
  16. Illumina PE集群套件(Illumina,目录号:FC-401-4003)
  17. Illumina 250周期SBS试剂(Illumina,目录号:PE-401-4001)
  18. 马铃薯葡萄糖肉汤(BD,目录号:254920)
  19. 标准灌封土(Topfsubstrat ED63,Einheitserde,目录号:SP ED63 T,从GBC-Gartenbauzentrum Schwechat获得,目录号:013224)
  20. 珍珠岩(珍珠岩Premium 2-6mm,Gramoflor,从GBC-Gartenbauzentrum Schwechat获得,目录号:079568)
  21. 二氧化硅砂(Quarzsand 0.5-2mm,min2C,从GBC-Gartenbauzentrum Schwechat获得,目录号:005989)
  22. 萌发土(Aussaaterde,Huminsubstrat N3,Neuhaus,Klasmann-Deilmann,从GBC-Gartenbauzentrum Schwechat获得,目录号:001318)
  23. 马铃薯葡萄糖液介质(PD培养基)(参见食谱)
  24. 土壤混合(见食谱)

设备

  1. Pipetman P1000(Gilson,目录号:F123602)
  2. Pipetman P200(Gilson,目录号:F123601)
  3. 离心机用于50ml一次性小瓶(例如,Eppendorf,型号:5810 R)
  4. 光谱光度计,能够测量600 nm的OD
  5. ND-1000纳米分光光度计(Thermo Fisher Scientific,型号:NanoDrop 1000)
  6. 2100 Bioanalyzer(Agilent Technologies,型号:2100 Bioanalyzer)或Fragment Analyzer 12(Advanced Analytical Technologies,型号:Fragment Analyzer 12)
  7. Illumina HiSeq2500仪器(Illumina,型号:HiSeq ® 2500)
  8. 通风柜

软件

  1. FastQC( http://www.bioinformatics.babraham.ac。 uk / projects / fastqc )(免费提供)
  2. Cutadapt(Martin,2011)(免费提供)
  3. Kallisto(Bray 等,,2016)(免费提供)
  4. R统计环境(R Development Core Team,2012)(免费提供)
    对于不熟悉R的用户,以下资源可能会有所帮助:
    https:// cran。 r-project.org/doc/manuals/r-release/R-intro.pdf
    https://onlinecourses.science.psu.edu/statprogram/r < / a>
    https://www.youtube.com/watch?v=7cGwYMhPDUY < / a>
  5. DESeq2 R软件包(Love et al。,2014)(免费提供)
  6. FunCat工作流程(Ruepp 等人,2004)在Pedant主页上实现( http://mips.helmholtz-muenchen.de/funcatDB/index_update.html )(免费提供给公开发布的物种)
  7. 墨卡托在线工具( http:// www。 plabipd.de/portal/web/guest/mercator-sequence-annotation )(免费提供)
  8. Pageman工具(Usadel 等人,2006),Mapman软件套件的一部分:&lt; a class =“ke-insertfile”href =“http://mapman.gabipd.org/web / guest / mapman“target =”_blank“> http://mapman.gabipd.org/web/guest/mapman (免费提供)

程序

  1. 获得腐生菌生长的样品。 bromivora
    1. 获得腐生菌生长的样品。溴霉素,PD培养基(参见食谱)从保持在-80℃的交配1型菌株(UB1)甘油储备液接种。通过将1ml UB1过夜培养物与1ml 50%甘油混合来制备甘油储备液。对于原来的隔离U。 bromivora 孢子线请参考(Bosch and Djamei,2017)。
    2. 在三个独立的生物重复中,细胞在21℃和200rpm下生长至OD 600nm±0.8。通过在50ml小瓶中以1,200×g离心5分钟来对真菌材料进行取样。小心地倒出上清液,将沉淀物在液氮中冲击冷冻。如果其他种类生长,则可能是沉淀较不稳定,而且应该吸取上清液。
  2. 获取在植物中生长的真菌的样品
    1. 为了获得植物中生长的真菌的样品,通过将杂交的Brachypodium hybridum Bd28的晶体与两个50ml的管子一起放入封闭的灭菌器中进行气体灭菌,每个管子分别具有25ml / %次氯酸钠溶液+ 0.75ml 37%HCl 2小时。
    2. 在气体灭菌后,将种子留在通风橱中的50ml管中,盖上1小时,以便任何残留的氯气可以蒸发。
    3. 然后将种子在无菌过滤的自来水中浸泡1-2小时,并在4℃下在黑暗中发芽2-3周。
    4. 幼苗随后用U感染。溴孢菌孢子溶液。通过用具有V形底部的一次性小瓶中的微量均化器将少量(例如,1ml)的过滤的自来水的感染植物的孢子吸收填充的小穗研磨得到孢子溶液,例如,,1.5ml微量离心管)。将幼苗在4℃下用孢子溶液保湿(但不浸没)一周,然后种植在土壤中(参见配方部分的土壤混合物)。
    5. 种植后12天,收获植物茎,并在液氮中冲击冷冻
  3. RNA提取
    一个。根据制造商的方案,使用TRIzol(Chomczynski和Sacchi,2006)提取RNA。
    湾根据制造商的说明书,使用TURBO DNA-免费 Kit除去残留的DNA。
  4. RNA质量和数量的测定
    1. 应检查RNA质量:为了确定RNA浓度和纯度,应使用NanoDrop分光光度计测量RNA。接近2的260:280比例和大约2-2.2的260:230的比率是理想的。对于随后的文库制备,需要最少量的100-200ng RNA。为了确定分离的RNA没有显着降解,可以使用具有适当试剂盒的Bioanalyzer或Fragment Analyzer(参见设备)。测量根据制造商的说明进行。由Bioanalyzer软件提供的RNA完整性编号(RIN)不适用于植物样品,因为它们含有植物和真菌rRNA(例如:参见图1)。但是,生物分析仪或片段分析仪图应按以下标准进行手动评估:所有rRNA峰值应窄且近对称成型,峰值之间的基线应平坦且接近0.
    2. 或者,RNA凝胶(TBE中的1%琼脂糖凝胶)可用于评估RNA完整性,即使该方法比使用Bioanalyzer / Fragment Analyzer更不精确。对于来自腐生菌生长的高质量RNA样品,应该看到四个条带,两个清晰带(rRNA)和植物样本中的四个条带,几乎没有额外的涂片。


      图1.从 U.olivora 感染 感染 > Brachypodium 组织

  5. 图书馆建设
    1. 在文库制备之前,按照制造商的说明书,使用Ribo-Zero rRNA去除试剂盒从样品中除去核糖体RNA。
    2. 使用NEB Next Ultra RNA文库准备工具制备文库。尺寸选择使用Ampure XP珠进行。生物分析仪/片段分析仪用于测试文库的大小分布,然后使用qPCR来确定簇生成所需的正确浓度。
  6. 使用Illumina HiSeq2500仪器,以配对终端模式(PE125)对图书馆进行排序。
    注意:
    1. 在决定测序深度时,重要的考虑因素是来自感染宿主组织的文库中病原体转录物的足够覆盖(见注释)。
    2. 我们对植物样本进行了测序,平均深度为130,000,000个原始读数和无菌培养样本,平均深度为23,000,000个原始读数。
  7. 使用FastQC评估产生的读数的质量:检查的属性是排除测序问题的基准质量,如终止或失败循环中的质量下降,kmer分布和超表示序列,以识别适配器二聚体和短插入和重复率排除过度放大或其他图书馆问题。除了在3'端显示Illumina适配器序列的少量读数,我们没有遇到任何明显的问题。使用cutadapt v1.4.2从读取中删除适配器序列。

数据分析

图2显示了RNA-Seq数据分析中涉及的不同步骤的流程图,表明使用的软件工具

图2. RNA-Seq数据分析的工作流程。深蓝色背景:来自lllumina RNA测序的输入;浅蓝色:排序质量控制和阅读量化;绿色:识别差异表达基因;橙色:三种用于过度/不足表示分析的方法。

  1. 鉴定差异基因
    1. 然后将经修剪的RNA-Seq读数与使用Kallisto软件,使用默认值从提取自Twachypodium distachyon Bd21(Bdistachyon_283_v2.1)和Ustilago bromivora UB1注释的组合转录组进行定量参数。这构成了针对转录组的伪对齐。在同一基因编码的多个剪接变体被鉴定的情况下(在我们的数据集20例中),剪接变体可以作为所有后续分析中的单个基因进行处理和计数。
    2. 数据集的进一步分析可以用统计环境R.进行。为了比较。溴霉素在植物生长条件下的腐生菌和萌发之间的基因表达,只有U。 bromivora 转录本(即,,具有以“UBRO”开始的基因标识符的那些)保留。差分表达式统计使用DESeq2 R包计算。为了估计样本之间的大小因子,使用所有样本之间的总体真菌表达水平相似的默认假设(估计大小为:type ='ratio')。
    3. 如果与无菌培养物相比log2倍变化>,则植株中的誊本被认为在植物中显着上调或下调。 2 /&LT; -2和Benjamini-Hochberg(Hochberg和Benjamini,1990)校正了 0.1。
      此外,为了可靠地评估植物中的下调,尽管植物样本中真菌读数的覆盖率较低,我们需要平均至少150次阅读,样本在植物中被认为下调。 (见“备注”部分的更多细节)。

  2. 过/不足表示分析
    1. 为了测试植物中上下文转录中感兴趣的转录物类型的过/不足表达(ORA),可以根据情况使用以下策略之一:
      1. 对于用户定义的成绩单列表(例如,在我们的例子中,编码预测的分泌蛋白质的转录本的列表),在显着响应的转录物的列表中与给定类别的过度/不足的表示相比,所有预测或表达转录本中的分类可以用R统计环境中的Fisher精确检验来计算。可以使用以下代码:

        输入&lt; - cbind(total = c(Nr> total_secreted ,Nr total non-secreted ),in_planta_up = c(Nr in-planta up secreted ,Nr in-planta up non-secreted ))
        fisher.test(输入)

        在fisher.test()函数中,可以添加参数 alternative =“greater”或 alternative =“less”,以便仅测试过度表示或仅在代表不足的情况下测试可以排除这两个选项。
        Nr total_secreted ..在所有预测或表达的成绩单中编码预测的分泌蛋白质的转录本数目
        在所有预测或表达的转录物中编码预测的非分泌蛋白质的转录本数目
        Nr 植物分泌物 ..编码预测的分泌蛋白质的转录本数量确定为与腐生菌生长显着上调的植物之间的转录本
        Nr 植物中未分泌的 ..编码预测的非分泌蛋白质的转录本数目确定为与腐生长生长显着上调植物中的转录本。

      2. 为了系统地测试在植物中显着上调或下调的成绩单中功能类别的过度表示,可用的FunCat分类和预测的U的可用的ORA工作流程。 bromivora 成绩单 http://mips.helmholtz -muenchen.de/funcatDB/index_update.html 可以使用。
      3. 或者,以下工作流可以用于所有物种,当FunCat注释不可用时:
        1. 使用墨卡托在线工具在Mapman格式中生成功能注释 - 预测成绩单必须以FASTA格式提交。
        2. 使用Pageman工具(集成在Mapman软件套装中 http:// mapman.gabipd.org/web/guest/mapman )进行ORA。我们使用Fisher测试统计,并通过Benjamini-Hochberg算法对p值进行了多次测试。

笔记

  1. 所有排序原始数据(.bam文件)以及每个 U的计数。可以从GeneExpressionOmnibus(GEO: http://www.ncbi.nlm.nih.gov/geo/ ),登录号GSE87751。
  2. 关于来自病原体感染的宿主组织的样品的测序深度的考虑:所需的测序深度将取决于与宿主RNA相比的病原体RNA的比例和感兴趣的基因的预期表达水平。这些性质可能会有所不同,这取决于感染周期和研究组织的时间点。定量实时(qRT)-PCR预测定比较宿主与病原体基因组标记可以帮助评估宿主与病原体细胞之间的比例。
    下面我们给出一些我们的数据集中的例子,可以帮助估计相关属性,并评估个体基因观察到的表达差异的可靠性:
    尽管在植物样本中平均获得了大约10350万次可绘制的图像,而在无效文化样本中只有13,800,000个,即,总读数大约为7.5倍,在植物中 样本仅包含平均约175,000英尺。 bromivora 读取,与在无菌培养物中生长的真菌样品中的〜14,000,000个真菌读数相比。因此,我们获得了大约80倍的更好的覆盖阶段。这相当于与混合样品中的真菌读数相比,具有〜600倍的植物读数,与在直接比较混合样品中的植物和真菌读数时所得到的相似。具有80倍以下覆盖率意味着在无菌培养物中平均表达的转录物在植物中获得80个读数,通常在植物中只能获得一个读数。因此,与无菌培养物相比,为了可靠地评估在植物中没有发现读数的抄本是否被下调,在无菌培养样品中需要足够的读数。我们定义了在无菌培养中至少150次读数(大约为2倍的覆盖差异)的阈值,除了DESeq2分析的重要价值之外,考虑未获得读数的转录本在植物中在植物中显着下调。应用这些过滤器,我们确定了888。由于覆盖范围不足,无法可靠地评估植物中下调的bromivora 成绩单。
  3. 化学品,消耗品和仪器的供应商和订货号是指我们使用的设备。一般来说,可以从其他供应商交换等效设备。

食谱

  1. 马铃薯葡萄糖液介质(PD培养基)
    24 g / L土豆葡萄糖肉汤去离子水
    高压灭菌
  2. 土壤混合
    3:1:1:1标准灌封土:珍珠岩:硅砂:发芽土壤

致谢

该协议由Rabe等人(2016)改编。我们要感谢参与该协议的作品的所有人员以及它所基于的原始出版物。导致这些成果的研究从欧盟研究委员会根据欧盟第七框架计划(FP7 / 2007-2013)/ ERC拨款协议号获得资助。 [EUP0012'Effectomics'],奥地利科学基金(FWF):[P27429-B22,P27818-B22,I3033-B22]和奥地利科学院(OEAW)。

参考

  1. Bosch J.和Djamei A.(2017)。 Ustilago的隔离通过孢子恢复和发芽从感染的小穗中提取溴菌丝菌株。 Bio Protoc 7(14):e2392。
  2. Bray,NL,Pimentel,H.,Melsted,P.和Pachter,L.(2016)。近似最佳概率RNA-seq quantification.v Nat Biotechnol 34:525-527。
  3. Chomczynski,P和Sacchi,N。(2006)。通过酸性硫氰酸胍 - 苯酚 - 氯仿提取的RNA分离的单步方法:二十多年。 Nat Protoc 1(2):581-585。
  4. Hochberg,Y和Benjamini,Y。(1990)。&nbsp; 更强大的多重意义测试程序。 Stat Med 9(7):811-818。
  5. Lohse,M.,Nagel,A.,Herter,T.,May,P.,Schroda,M.,Zrenner,R.,Tohge,T.,Fernie,AR,Stitt,M.and Usadel,B.(2014 )。墨卡托:基因组快速简单的网络服务器植物序列数据的大规模功能注释。植物细胞环境37(5):1250-1258。
  6. Love,MI,Huber,W. and Anders,S。(2014)。&nbsp; 使用DESeq2的RNA-seq数据的折叠变化和色散的中度估计。 Genome Biol 15(12):550.
  7. Martin,M.(2011)。&nbsp; Cutadapt从高通量排序读取中删除适配器序列。 EMBnet 17:10-12。
  8. Rabe,F.,Bosch,J.,Stirnberg,A.,Guse,T.,Bauer,L.,Seitner,D.,Rabanal,FA,Czedik-Eysenberg,A.,Uhse,S.,Bindics, ,Genenncher,B.,Navarrete,F.,Kellner,R.,Ekker,H.,Kumlehn,J.,Vogel,JP,Gordon,SP,Marcel,TC,Münsterkötter,M.,Walter,MC,Sieber,CM ,Mannhaupt,G.,Güldener,U.,Kahmann,R.and Djamei,A。(2016)。&lt; a class =“ke-insertfile”href =“https://www.ncbi.nlm.nih。 gov / pubmed / 27835569“target =”_ blank“>用于研究Ustilago bromivora 和 Brachypodium sp的完整工具集。作为真菌温带草病系统。 eLife 5:179-188。
  9. R开发核心团队。 (2012)。 R:统计计算的语言和环境。统计计算基金会。
  10. Ruepp,A.,Zollner,A.,Maier,D.,Albermann,K.,Hani,J.,Mokrejs,M.,Tetko,I.,Güldener,U.,Mannhaupt,G.,Münsterkötter, Mewes,HW(2004)。&nbsp; FunCat,功能性用于从全基因组进行蛋白质系统分类的注释方案。 Nucleic Acids Res 32(18):5539-5545。
  11. Usadel,B.,Nagel,A.,Steinhauser,D.,Gibon,Y.,Bläsing,OE,Redestig,H.,Sreenivasulu,N.,Krall,L.,Hannah,MA,Poree,F.,Fernie, A.R和Stitt,M.(2006)。 PageMan:交互式本体工具,用于生成,显示和注释概要分析实验的概览图。 BMC生物信息学 7:535.
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright Czedik-Eysenberg et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Czedik-Eysenberg, A. B., Rabe, F., Ekker, H., Czepe, C. and Djamei, A. (2017). Overrepresentation Analyses of Differentially Expressed Genes in the Smut Fungus Ustilago bromivora during Saprophytic and in planta Growth. Bio-protocol 7(15): e2426. DOI: 10.21769/BioProtoc.2426.
  2. Rabe, F., Bosch, J., Stirnberg, A., Guse, T., Bauer, L., Seitner, D., Rabanal, F. A., Czedik-Eysenberg, A., Uhse, S., Bindics, J., Genenncher, B., Navarrete, F., Kellner, R., Ekker, H., Kumlehn, J., Vogel, J. P., Gordon, S. P., Marcel, T. C., Münsterkötter, M., Walter, M. C., Sieber, C. M., Mannhaupt, G., Güldener, U., Kahmann, R. and Djamei, A. (2016). A complete toolset for the study of Ustilago bromivora and Brachypodium sp. as a fungal-temperate grass pathosystem. eLife 5: 179-188.
提问与回复

(提问前,请先登录)bio-protocol作为媒介平台,会将您的问题转发给作者,并将作者的回复发送至您的邮箱(在bio-protocol注册时所用的邮箱)。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片或者视频的形式来说明遇到的问题。由于本平台用Youtube储存、播放视频,作者需要google 账户来上传视频。

当遇到任务问题时,强烈推荐您提交相关数据(如截屏或视频)。由于Bio-protocol使用Youtube存储、播放视频,如需上传视频,您可能需要一个谷歌账号。