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Jul 2020

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Identification of Intrinsic RNA Binding Specificity of Purified Proteins by in vitro RNA Immunoprecipitation (vitRIP)
体外RNA免疫沉淀(vitRIP)鉴定纯化蛋白的RNA结合特异性   

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Abstract

RNA-protein interactions are often mediated by dedicated canonical RNA binding domains. However, interactions through non-canonical domains with unknown specificity are increasingly observed, raising the question how RNA targets are recognized. Knowledge of the intrinsic RNA binding specificity contributes to the understanding of target selectivity and function of an individual protein.


The presented in vitro RNA immunoprecipitation assay (vitRIP) uncovers intrinsic RNA binding specificities of isolated proteins using the total cellular RNA pool as a library. Total RNA extracted from cells or tissues is incubated with purified recombinant proteins, RNA-protein complexes are immunoprecipitated and bound transcripts are identified by deep sequencing or quantitative RT-PCR. Enriched RNA classes and the nucleotide frequency in these RNAs inform on the intrinsic specificity of the recombinant protein. The simple and versatile protocol can be adapted to other RNA binding proteins and total RNA libraries from any cell type or tissue.

Graphic abstract:



Figure 1. Schematic of the in vitro RNA immunoprecipitation (vitRIP) protocol

Keywords: RNA immunoprecipitation (RNA免疫沉淀反应), In vitro (体外), RNA binding specificity (RNA结合特异性), Intrinsic specificity (内在的特异性), Recombinant protein (重组蛋白), RNA-protein interaction (RNA蛋白质相互作用), RNA sequencing (RNA测序)

Background

Eukaryotic cells contain a number of different RNA classes with thousands of RNA species and a highly diverse set of proteins interacting with those. RNA-protein interactions can be classified as specific and nonspecific, depending on the definition of the bound RNA sequence or structure and on the protein domains involved in the interaction (Jankowsky and Harris, 2015). RNA interactions through non-canonical RNA binding domains with unknown specificity are increasingly observed, which raises the question how dedicated RNA targets are recognized.


A better understanding of the function of a particular RNA binding protein would thus be facilitated by the identification of its intrinsic RNA binding specificity. Since RNA binding in vivo is often modulated by cooperating factors, intrinsic specificity can only be determined in an in vitro approach (Sloan and Bohnsack, 2018). Several high-throughput techniques, such as RNAcompete (Ray et al., 2013), RNA Bind-n-Seq (Lambert et al., 2014), and in vitro iCLIP (Sutandy et al., 2018) are able to determine the RNA binding profiles of individual proteins in vitro. These methodologies use RNA oligonucleotide libraries or pools of in vitro-transcribed RNA as substrates. Such artificial RNA libraries may be highly complex, but do not represent the cellular RNA pool and may not contain secondary structures.


We developed an in vitro RNA immunoprecipitation assay (vitRIP) that uncovers intrinsic RNA-binding specificities of isolated proteins in the context of the total cellular RNA pool (Figure 1) (Müller et al., 2020). Applying vitRIP to the DExH helicase MLE (maleless) and the male-specific lethal dosage compensation complex (MSL-DCC) from Drosophila melanogaster revealed the mechanism of specific incorporation of the long non-coding roX (RNA on the X) RNAs into the MSL-DCC (Müller et al., 2020). The simple vitRIP methodology identifies transcripts bound to a recombinant protein in native conditions (no crosslinking involved) by deep sequencing or quantitative RT-PCR. vitRIP uses the complex cellular transcriptome as substrate and informs on the intrinsic binding specificity of a given protein outside of its physiological context. Furthermore, the experimental setting can be easily controlled and adapted to the research question. The following protocol details the vitRIP procedure using the RNA helicase MLE and total RNA extracted from male Drosophila S2 cells as an example (Müller et al., 2020). However, we propose that vitRIP can be applied to any RNA binding protein, which can be produced recombinantly and purified to near-homogeneity. Moreover, total RNA extracted from any cell type or tissue can serve as an RNA library.


Materials and Reagents

Note: Please ensure that all reagents and materials are RNase-free.

  1. 1.5-ml low-binding tubes (Sarstedt, catalog number: 72.706.700)

  2. Drosophila melanogaster S2 cells (Drosophila Genomics Resource Center, https://dgrc.bio.indiana.edu/Home) or any cell line/tissue

  3. RNeasy Mini Kit (Qiagen, catalog number: 74104)

    Note: We did not test other RNA extraction kits, but would assume the manufacturer is not critical. Nevertheless, the extractability of various RNA species might differ between different kits.

  4. Anti-FLAG M2 Affinity Gel (Sigma, catalog number: A2220) or other appropriate affinity resin, depending on the tag of the recombinant protein

  5. Yeast tRNA solution (Sigma, catalog number: R5636)

  6. BSA fraction V powder (for blocking) (Sigma, catalog number: 05482)

  7. Nuclease-free water (Thermo Fisher Scientific, catalog number: AM9937)

  8. DNase I recombinant, RNase-free (Sigma, catalog number:04716728001)

  9. BSA 20 mg/ml solution (for vitRIP) (New England Biolabs, catalog number: B9000S)

  10. Recombinant RNasin RNase Inhibitor (40 U/μl) (Promega, catalog number: N2515)

  11. Adenosine 5’-triphosphate disodium salt (ATP) (Sigma, catalog number: 10127523001)

  12. Proteinase K (BioCat, catalog number: BIO-37039-BL)

  13. 10% SDS solution

  14. Phenol:chloroform:phenylalcohol ROTI Aqua-P/C/I (Carl Roth, catalog number: X985.1)

  15. 3 M sodium acetate pH 5.2

  16. Glycogen (20 mg/ml) (Sigma, catalog number:10901393001)

  17. Ethanol absolute for analysis (Sigma, catalog number: 1009832500)

  18. SuperScript III First-Strand Synthesis System (Thermo Fisher Scientific, catalog number: 18080051)

  19. Fast SYBR Green Mastermix (Thermo Fisher Scientific, catalog number: 4385610)

  20. Quant-iT Qubit RNA HS Assay kit (Thermo Fisher Scientific, catalog number: Q32855)

  21. Agilent RNA 6000 Pico Kit (Agilent, catalog number: 5067-1513)

  22. Agilent DNA 1000 Kit (Agilent, catalog number: 5067-1504) or Agilent High Sensitivity DNA Kit (Agilent, catalog number: 5067-4626)

  23. NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (New England Biolabs, catalog number: E6310)

  24. NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs, catalog number: E7760)

  25. KCl

  26. NaCl

  27. Na2HPO4

  28. KH2PO4

  29. HEPES

  30. MgCl2

  31. NP40 (IGEPAL CA-630, Sigma, catalog number: I3021)

  32. Anti-FLAG M2 Monoclonal Antibody (Sigma, catalog number: F3165)

  33. 2× SDS PAGE sample buffer (125 mM Tris HCl pH 6.8, 4% SDS, 20% glycerol, 0.01% bromophenol blue, 200 mM DTT)

  34. 1× PBS (see Recipes)

  35. vitRIP-100 buffer (see Recipes)

  36. vitRIP-250 buffer (see Recipes)

Equipment

  1. -80 °C freezer

  2. DeNovix DS-11 spectrophotometer (Biozym, catalog number: 31DS-11) or other spectrophotometer with 254 nm and 280 nm wavelength

  3. Refrigerated centrifuge (Eppendorf, model: 5424R)

  4. Eppendorf ThermoMixer C with heated lid (Eppendorf, catalog numbers: 5382000015, 5308000003)

  5. Rotator (Neolab, catalog number: 2-1175)

  6. Qubit fluorometer (Invitrogen)

  7. Bioanalyzer 2100 Instrument (Agilent)

  8. Lightcycler 480 Instrument II, 384-well (Roche, catalog number: 05015243001) or another Real-Time PCR System

  9. (Access to) Illumina HiSeq 1500 Sequencing Platform

Software

  1. STAR – Spliced Transcript Alignment to a Reference (Alexander Dobin, Cold Spring Harbor Laboratory, https://github.com/alexdobin/STAR) (Dobin et al., 2013)

  2. Samtools (Genome Research Limited, Sanger Institute, http://www.htslib.org) (Li et al., 2009)

  3. Bedtools (Quinlan laboratory, University of Utah, https://bedtools.readthedocs.io/en/latest/)

  4. IGV – Integrative Genomics Viewer and igvtools (IGV Team, Broad Institute and UC San Diego, http://software.broadinstitute.org/software/igv/home) (Robinson et al., 2011)

  5. R (R Core Team, https://www.r-project.org)

  6. RStudio (RStudio, https://rstudio.com)

  7. Bioconductor (Bioconductor Core Team, http://bioconductor.org) (Huber et al., 2015)

Procedure

Notes:

  1. It is essential to perform the experiment in ≥3 biological replicates using different batches of purified protein and total RNA.

  2. Conditions need to be optimized for each experimental system. Protein and/or RNA titration might be required to obtain an optimal signal-to-noise ratio.

  3. Include a vitRIP reaction with experimental RNA, but lacking the recombinant protein, to score RNA background on beads.

  4. Additional control reactions (e.g., total RNA extracted from a different species or protein mutated in its RNA binding domain (if known)) might help to further characterize the specificity of identified protein-RNA interactions.

  5. Work in RNase-free conditions.

  1. Preparation of total RNA

    1. Cultivate Drosophila melanogaster S2 cells under standard conditions. For details, see Drosophila Genomics Resource Center (https://dgrc.bio.indiana.edu/Home).

    2. Collect 5 × 106 exponentially growing cells by centrifugation (500 × g, 5 min, 25 °C) and discard the supernatant.

    3. Wash the cell pellet with 500 µl PBS, collect by centrifugation (500 × g, 5 min, 25 °C) and discard the supernatant.

    4. Proceed directly with RNA extraction or store the cell pellet at -80 °C for a maximum duration of 3 months.

    5. Extract total RNA using the Qiagen RNeasy Mini Kit according to the manufacturer’s instructions. Elute the RNA in 50 µl RNase-free water and determine the concentration spectrophotometrically. The typical yield from 5 × 106 S2 cells ranges between 25-50 µg.

    6. Store extracted RNA at -80 °C until further use (for a maximum duration of 1 year). Avoid frequent freezing/thawing of the RNA sample to prevent degradation.

      Note: Any other cell line or animal tissue can be used. We successfully tested several male and female Drosophila cell lines as well as head tissue from adult Drosophila. The methodology for RNA extraction from tissues might require adjustment.

  2. Preparation of highly purified protein

    Note: The vitRIP protocol presented here uses the well-characterized RNA helicase MLE as an example. However, vitRIP can be applied to any protein, which can be purified to near-homogeneity. Protein purification strategies have to be determined for each protein individually and cannot be generalized.

    1. Express the protein of interest in an appropriate heterologous expression system (e.g., E. coli or SF21 insect cells) and purify to near-homogeneity. Analyze the purity of the protein sample by SDS-PAGE and Coomassie staining.

    2. Determine the protein concentration by an appropriate method (absorption at 280 nm or Bradford protein assay). If necessary, use spin concentrators to increase the protein concentration. Flash freeze small aliquots (10-20 µl) and store at -80 °C. The required protein concentration for vitRIP strongly depends on the affinity to RNA substrates and needs to be determined empirically by applying protein titration series, as described in Müller et al. (2020). See also Step D, Note 3.

    3. Before use, thaw protein prep on ice, spin down to remove potential aggregates (20,000 × g, 10 min, 4 °C) and use the supernatant for vitRIP.

    4. The purification of full-length FLAG-tagged Drosophila melanogaster MLE after baculovirus-driven expression in SF21 cells is described in detail in Izzo et al. (2008) and Maenner et al. (2013), differing only in the elution buffer for MLE (20 mM HEPES-KOH pH 7.6, 200 mM KCl, 1 mM DTT, 2 mM MgCl2, 10% glycerol). Purified MLE has a typical concentration of 0.5-1 µg/µl and can be used directly for vitRIP.


  3. Preparation of Anti-FLAG M2 Affinity Gel

    Note: Prepare the resin always fresh. Any other resin matching the affinity tag can be used. Alternatively, protein-specific antibodies coated to Protein A or Protein G Sepharose may be used.

    1. Use 20 µl Anti-FLAG M2 Affinity Gel (“beads”) for each vitRIP reaction.

    2. Wash the beads: Add 1 ml PBS, invert the tube 10 times, centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant. Repeat this step one more time.

    3. Block the beads: Add 2% BSA/PBS/0.1 mg/ml yeast tRNA to the bead and incubate for 1 h at 4 °C with rotation. Centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant. This step intends to prevent non-specific binding of protein and/or total RNA to the FLAG affinity gel.

    4. Wash the beads: Add 1 ml vitRIP-100 buffer, invert the tube 10 times, centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant. Repeat this step one more time.

    5. With the last washing step, discard as much of the supernatant as possible and keep beads on ice.


  4. Pull down of RNA-protein complexes

    Notes:

    1. Pipet all steps on ice and centrifuge at 4 °C. Use low-binding tubes.

    2. The RNA amount for vitRIP depends on the abundance of individual transcripts and on the affinity of the studied protein to RNA. The amount might be determined empirically by applying an RNA titration series. In our hands, 2 µg total RNA per vitRIP reaction (100 µl volume) worked well.

    3. The protein concentration for vitRIP depends on the affinity to RNA and needs to be determined empirically by titration. We recommend to start with 10-250 nM of recombinant protein and 2 µg of total RNA. If no binding is observed, protein amount may be increased. MLE vitRIP was performed with 5-50 nM protein.


    1. Per reaction, dilute 2 µg of total RNA with vitRIP-100 buffer to a final volume of 10 µl. 10% of this mix (1 µl) serves as an RNA input sample and is kept on ice.

    2. Mix the remaining 90% of the RNA mix (9 µl) with the purified protein (MLE-FLAG: 5 nM and 50 nM, respectively) in a total volume of 100 µl in vitRIP-100 buffer including 10 µg BSA, 0.1 U/µl RNase-free recombinant DNase I, 0.8 U/µl RNasin RNase inhibitor and 1 mM ATP.

    3. Incubate 30 min at 25 °C (thermoblock with heated lid).

    4. Spin down reaction at 20,000 × g, 1 min, 4 °C and transfer the supernatant to blocked and equilibrated FLAG beads (from Step C5) in a 1.5 ml tube.

    5. Incubate for 30 min at RT (22 °C) with rotation.

    6. Centrifuge at 500 × g, 1 min, 4 °C and discard the supernatant (= unbound fraction).

    7. Wash beads with 1 ml vitRIP-100 buffer: Add 1 ml buffer, rotate 1 min at room temperature, centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant.

    8. Wash beads with 1 ml vitRIP-250 buffer: Add 1 ml buffer, rotate 1 min at room temperature, centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant.

    9. Wash beads with 1 ml vitRIP-100 buffer: Add 1 ml buffer, rotate 1 min at room temperature and distribute the beads in two tubes for protein and RNA extraction: 75% (750 µl) for RNA extraction and 25% (250 µl) for protein extraction. Centrifuge for 1 min at 500 × g, 4 °C and discard the supernatant.


  5. Protein extraction for Western Blot analysis

    1. To each sample (25% bead material, from Step D10), add 20 µl of 2× SDS sample buffer. Incubate at 95 °C, 5 min, with agitation.

    2. Centrifuge for 1 min at 500 × g to pellet beads and analyze the supernatant by SDS-PAGE and Western Blot for successful protein pull down.

    Note: vitRIP of FLAG-tagged MLE is analyzed by Western blot analysis using anti-FLAG antibody at 1:5,000 (v/v) dilution. MLE is visible as a single band corresponding to a molecular weight of 145 kDa.

  6. RNA extraction for RNA identification

    1. Input samples: Mix 10% RNA input (1 µl), 5 µl 10% SDS, 10 µl Proteinase K (10 mg/ml) and 84 µl vitRIP-100 buffer. Incubate in a thermoblock (with heated lid) at 55 °C, 45 min with agitation at 1,000 rpm.

    2. IP samples (75% bead material, from step D10): Add 5 µl 10% SDS, 10 µl Proteinase K (10 mg/ml) and 85 µl vitRIP-100 buffer. Incubate in a thermoblock (with heated lid) at 55 °C, 45 min with agitation (1,000 rpm).

    3. Centrifuge tubes for 1 min at 500 × g at room temperature to pellet beads of IP samples.

    4. Transfer the supernatant (contains eluted RNA) and transfer to a fresh 1.5 ml tube.

    5. To each sample (Input and IP), add 1 volume (around 100 µl) of acidic phenol:chloroform:isoamylalcohol. Vortex each tube thoroughly for 10 sec and centrifuge 5 min at 20,000 × g at room temperature.

      Safety note: Always use a fume hood when handling phenol:chloroform:isoamylalcohol and appropriately dispose of phenol-containing waste.

    6. Transfer the upper, aqueous phase (contains RNA; around 100 µl) to a fresh 1.5-ml tube and add 1/10 volume 3 M sodium acetate pH 5.2, 20 µg RNase-free glycogen and 2.5 volumes of ice-cold 100% EtOH.

    7. Incubate at -20 °C for > 12 h to precipitate the RNA.

    8. Centrifuge at 20,000 × g (full speed), 4 °C for 30 min. Carefully remove and discard the supernatant with a pipet tip without disturbing the RNA pellet, which is small and might be invisible.

    9. Wash pellet by adding 1 ml of ice-cold 70% EtOH.

    10. Centrifuge at 20,000 × g (full speed), 4 °C for 15 min. Carefully remove and discard the supernatant.

    11. Remove as much liquid as possible with a 20 µl pipet and air-dry the pellet (10-15 min).

    12. Resuspend the RNA pellet in 20 µl RNase-free water. Pipet up and down 10 times, followed by a quick spin. For complete solubilization, incubate the samples for 15 min at 25 °C with gentle agitation (500 rpm).

    13. Quantify 2 µl of the purified RNA using a Qubit fluorometer and the Qubit RNA High Sensitivity (HS) Assay kit.

      Note: The RNA concentration of IP samples might very low and not in the range of the Qubit RNA HS Assay kit standard. Our experience showed that RNA sequencing libraries could still be prepared with the NEB Ultra II Directional RNA Library Prep Kit. Other RNA-Seq library prep kits dealing with low-input samples may be employed.

    14. Store RNA at -80 °C until further use.


  7. RT-PCR

    Notes:

    1. If one or several RNA targets of the protein of interest are known, vitRIP can be analyzed by quantitative RT-PCR using gene-specific primers. It is important to include a non-bound transcript (e.g., housekeeping genes) as the negative control.

    2. In MLE vitRIP, the enrichment of the known MLE target roX2 RNA is quantified using roX2-specific primers and represented as the bound fraction relative to the input. The abundant 7SK RNA serves as the negative control. For details on primer sequences, see Müller et al. (2020). An example is given in Figure 2A.


    1. DNase I digest

      1. Mix 8 µl RNA, 1 µl 10× DNase I reaction buffer, 0.5 µl DNase I and 0.5 µl RNasin.

      2. Incubate for 1 h at 37 °C. Store on ice.

    2. cDNA synthesis using the SuperScript III First-Strand Synthesis System

      1. Use 10 µl DNase I-treated RNA and follow the manufacturer’s instructions for cDNA synthesis using random hexamer priming.

      2. Dilute the cDNA 1:5 to 1:50 (depending on the transcript abundance) with RNase-free water and analyze by qRT-PCR using transcript-specific primers and FAST SYBR Green Mastermix. Follow the recommendations specific for your Real-Time PCR machine.


  8. RNA-Seq Library preparation

    Note: The library preparation protocol depends on the experimental strategy. The high content of rRNA in total RNA will interfere with the analysis and should be removed before the library prep. In MLE vitRIP samples, rRNA is depleted using rRNA probes specific for human/mouse/rat rRNA, which also work reasonably well for Drosophila rRNA. This strategy allows for the identification of various RNA species in vitRIP samples (coding and non-coding). Alternatively, polyadenylated RNAs can be enriched using oligo d(T) beads if the experimental strategy aims at identifying exclusively enriched polyadenylated RNAs.


    1. Starting material for library preparation

      1. Quality control: Analyze 1 µl of purified input and immunoprecipitated RNA on a Bioanalyzer using the RNA 6000 Pico Kit.

      2. Choose an appropriate RNA quantity according to the recommendations of the RNA-Seq library prep kit. For MLE vitRIP library preparation with the NEB Ultra II Directional RNA Library Prep Kit, 30-40 ng RNA served as starting material.

    2. rRNA depletion

      1. Use 30-40 ng RNA as quantified by Qubit fluorometer for rRNA depletion using the NEB rRNA Depletion Kit.

      2. Follow the manufacturer’s protocol for rRNA depletion (NEB #E6310).

      3. Analyze 1 µl of rRNA-depleted sample on a Bioanalyzer using the RNA 6000 Pico Kit. The major 18S and 28S rRNA peaks (Drosophila) should be largely eliminated. It is not possible to obtain an RNA Integrity Number (RIN) for Drosophila RNA samples.

    3. Library preparation

      1. Use the rRNA-depleted sample for library preparation following the instructions of the NEB Ultra II Directional RNA Library Prep Kit (NEB #E7760).

      2. Assess quality and quantity of 1 µl of each library on a Bioanalyzer using the DNA 1000 or DNA High Sensitivity Kit. A single peak with a maximum of approximately 300 bp should be observed (Figure 2B).

      3. Sequence on an Illumina HiSeq1500 instrument in 50 bp paired-end mode. Sequencing run settings were 50bp-8bp-8bp-50bp (read1-index1-index2-read2), where the second index file was not used.



    Figure 2. Representative examples of vitRIP analysis. For details, see the original publication (Müller et al., 2020). A. MLE vitRIP with S2 RNA in absence or presence of ATP. Enrichment of roX2 and 7SK was analyzed by qRT-PCR and is displayed relative to the input. Ctrl indicates RNA-only vitRIP samples lacking recombinant MLE. B. Representative Bioanalyzer profiles of RNA sequencing libraries prepared of input and MLE vitRIP samples. Libraries were analyzed on a DNA 1000 chip and show a single peak with an expected maximum at approximately 300 bp. C. Genome browser views of representative vitRIP profiles of MLE on roX2 and 7SK in comparison to S2 input. Genomic coordinates are given above the graph. D. MA plot representing differential expression analysis of MLE vitRIP. E. Principal Component Analysis (PCA) of MLE vitRIP-seq data at different conditions. X-axis shows the first (PC1) and y-axis the second (PC2) principal component. F. Comparison of U-/A-tetranucleotide frequencies of RNA in MLE vitRIP relative to the input. RNA classes of interest (roX2, mitochondrial RNA) are highlighted. Spearman´s correlation (r) is indicated.

  9. Data analysis

    Notes:

    1. This section describes the RNA-Seq analysis of MLE vitRIP as published in Müller et al., 2020. Adjustment of the procedure to the chosen model system might be required.

    2. Analyze ≥3 biological replicates to ensure valid interpretation of the data.

    3. Simplified example code chunks are shown, see details:https://github.com/tschauer/vitRIP_2020.


    1. Processing of Illumina sequencing reads

      1. Map paired-end reads (50 bp) to the reference genome (Drosophila melanogaster version dm6) and count reads per gene using the STAR aligner (version 2.5.3a) and the Flybase annotation (ftp://ftp.flybase.net/releases/FB2017_04/dmel_r6.17/gtf/dmel-all-r6.17.gtf.gz) while filtering out reads with multiple alignments.


        STAR --runThreadN 8

        --readFilesCommand gunzip -c

        --quantMode GeneCounts

        --genomeDir ${path_to_star_index}

        --sjdbGTFfile ${gtf_file}

        --readFilesIn ${fastq_file}_1.txt.gz ${fastq_file}_2.txt.gz

        --outFileNamePrefix ${fastq_file}.

        --outSAMtype BAM SortedByCoordinate

        --limitBAMsortRAM 5000000000

        --outFilterMultimapNmax 1


      2. Count the total number of aligned reads using samtools (version 1.7) and generate normalized (reads per million) bedgraph files using genomeCoverageBed (bedtools version 2.27.1).


        total=`samtools view -c ${bam_file}`

        scaler=`echo "scale=10; 1/(${total}/1000000)" | bc`

        genomeCoverageBed -ibam ${bam_file} -bg -split -scale ${scaler} > ${bedgraph_file}


      3. Convert bedgraph to tdf files using igvtools (version 2.3.98).


        igvtools toTDF -z 5 ${bedgraph_file} ${tdf_file} ${path_to_fasta}


      4. Visualize tdf files in the IGV browser (Figure 2C).


    2. Differential expression analysis

      1. Load and merge count tables (*ReadsPerGene.out.tab files generated by STAR) into R (version 3.6.1) and keep genes with at least 1 read count present in 75% of the samples.


        filter <- apply(count_table, 1, function(x) length(x[x>1]) >= ncol(count_table)/1.333)

        count_table_filtered <- count_table[filter,]


      2. Use the DESeq2 package (version 1.26.0) for differential expression analysis while adding replicate information as batch variable.


        dds <- DESeqDataSetFromMatrix(countData = count_table_filtered,

                                     colData = column_data,

                                     design = ~batch+sample)

        dds <- DESeq(dds)


        Note: Samples that should be directly compared to each other have to be fitted in the same DESeq2 model.

      3. Obtain log2FoldChange estimates and adjusted p-values using the results function (DESeq2). Set the adjusted p-value cutoff to 0.01.


        res <- results(dds,

                           contrast = c("sample", "IP", "Input"),

                           independentFiltering = FALSE)

        res.sign <- subset(res, padj < 0.01)


      4. Visualize results as MA plot, label significant genes in red and example genes in orange (Figure 2D).


        plot(res$baseMean, res$log2FoldChange,

             log="x", xlim = c(1, 1e6), ylim = c(-10,10),

             xlab = "log mean counts", ylab = "log2 fold change",

             col = rgb(0,0,0,0.1), pch = 19, cex = 0.25)


        points(res.sign$baseMean, res.sign$log2FoldChange,

              col = rgb(0.8,0,0,0.5), pch = 19, cex = 0.25)


        favorite_gene <- rownames(res) %in% c("FBgn0019660","FBgn0019661")

        points(res$baseMean[favorite_gene],res$log2FoldChange[favorite_gene],

               col = rgb(0.9,0.6,0,1), pch = 19, cex = 1)


    3. Principal Component Analysis (PCA)

      1. Correct for batch effect using the ComBat function (sva package version 3.32) on the normalized read counts.


        batch_var <- colData(dds)$batch

        modcombat <- model.matrix(~sample, data = colData(dds))

        log2_corrected_counts <- ComBat(dat = log2(counts(dds, normalized = TRUE)+1),

                                       batch = batch_var, mod = modcombat,

                                       par.prior = TRUE, prior.plots = FALSE)


      2. Perform and plot PCA on the batch-corrected counts (Figure 2E).


        pca <- prcomp(t(log2_corrected_counts), scale. = TRUE)

        percentVar <- round(pca$sdev^2/sum(pca$sdev^2)*100,1)[1:10]


        plot(pca$x[,1], pca$x[,2],

            col = color_palette[colData(dds)$sample], pch = 16, cex = 0.7,

            xlab = paste("PC1 (", percentVar[1], "%)", sep=""),

            ylab = paste("PC2 (", percentVar[2], "%)", sep=""))


    4. Nucleotide frequency analysis

      1. Set up annotation by converting FlyBase annotation to TxDb object (GenomicFeatures package version 1.36.4) and extract genic sequences using the BSgenome package (version 1.52).


        txdb <- makeTxDbFromGFF("dmel-all-r6.17.gtf", format="gtf")

        seqlevelsStyle(txdb) <- "UCSC"

        seqlevels(txdb) <- gsub("mitochondrion_genome","chrM", seqlevels(txdb))


        chromosomes <- c("chrX","chr2L","chr2R","chr3L","chr3R","chr4","chrM","chrY")

        ranges_genes <- genes(txdb)

        ranges_genes <- keepSeqlevels(ranges_genes, chromosomes, pruning.mode = "coarse")


        genic_seq <- BSgenome::getSeq(BSgenome.Dmelanogaster.UCSC.dm6, ranges_genes)


      2. Calculate genic nucleotide frequencies using the oligonucleotideFrequency function (Biostrings package version 2.52). Set oligonucleotide width to either 4, 5, or 6, sliding window step to 1 and as.prob to TRUE.


        genic_4merfreq <- oligonucleotideFrequency(genic_seq, width = 4, step = 1, as.prob = T)

        rownames(genic_4merfreq) <- names(genic_seq)


      3. Select genes, which are significantly enriched (adjusted p-value < 0.01 and log2[IP/Input] > 0).


        res.sign_enriched <- subset(res, padj < 0.01 & res$log2FoldChange > 0)


        select_enriched <- rownames(genic_4merfreq) %in% rownames(res.sign_enriched)

        select_notenriched <- !(rownames(genic_4merfreq) %in% rownames(res.sign_enriched))


        freqs_enriched <- genic_4merfreq[select_enriched,]

        freqs_notenriched <- genic_4merfreq[select_notenriched,]


      4. Visualize nucleotide frequencies for genes enriched or not enriched as boxplots. Sort sequences by their median frequency.


        freq_order <- order(apply(freqs_enriched, 2, median), decreasing = T)

        freqs_enriched <- freqs_enriched[,freq_order]

        freqs_notenriched <- freqs_notenriched[,freq_order]


        freqs_merged <- c(as.list(data.frame(freqs_enriched)),

                          as.list(data.frame(freqs_notenriched)))


        boxplot(freqs_merged[rep(1:25, each=2)+c(0,ncol(freqs_enriched))],

                col =c("darkred","darkgrey"), las=2, outline=F,

                ylab = "Frequency")


      5. Visualize association between vitRIP enrichment (i.e., log2[IP/Input]) and nucleotide frequencies as scatterplot. Calculate Spearman´s correlation to assess the relationship (Figure 2F).


        res_freq <- merge(res, genic_4merfreq, by="row.names")


        plot(res_freq$log2FoldChange, res_freq[,"TTTT"],

             main = "", xlab = "log2 Fold Change", ylab = "Frequency UUUU",

             xlim = c(-8,8), col = rgb(0,0,0,0.1), pch = 19, cex = 0.25)

        abline(v=0, col="grey32")


        corr <- cor(res_freq$log2FoldChange, res_freq[,"TTTT"], method = "spearman")

        title(paste("cor =", round(corr,2)), line = 0.5, cex.main=1)


    5. Evaluation of enriched/not-enriched RNA classes

      1. Extract annotations (e.g., exons, introns, 5’ and 3’ UTRs as well as for snRNAs, snoRNAs, and tRNAs) from the GTF annotation (i.e., dmel-all-r6.17.gtf).


        anno_gtf <- import.gff("dmel-all-r6.17.gtf")


        genes_3UTR <- anno_gtf[anno_gtf$type == "3UTR",]

        genes_3UTR_length <- aggregate(width(genes_3UTR),

                                   by = list(genes_3UTR$gene_id), FUN = max)


        select_q95 <- genes_3UTR_length[,2] > quantile(genes_3UTR_length[,2], 0.95)

        class.long3UTR <- genes_3UTR_length[,1][select_q95]


        Note: long 3’ UTRs transcripts were selected as transcripts with the longest 5% of 3’ UTRs. In cases, when a gene was annotated with 3’ UTR isoforms, the isoform with the longest 3’ UTR was taken.

      2. Extract annotation of Drosophila snoRNA classes from the SnOPY database (snoRNA orthological gene database; http://snoopy.med.miyazaki-u.ac.jp) (Yoshihama et al., 2013). Convert gene names to gene ids using org.Dm.eg.db package (version 3.8.2)


        snoRNA_snopy <- read.table("snoRNA_snopy.txt", header = T, sep = "\t", stringsAsFactors = F)


        snoRNA_HACA <- snoRNA_snopy$snoRNA.name[snoRNA_snopy$Box == "H/ACA"]

        class.snoRNA_HACA <- mapIds(org.Dm.eg.db, keys = snoRNA_HACA,

                                   keytype = "SYMBOL", column = "FLYBASE")


      3. Extract information on adenosine-to-inosine edited RNAs from published data (St Laurent et al., 2013). Read the excel sheet to R (gdata package version 2.18.0) and select unique gene ids from the GTF annotation.


        editedRNAs_symbols <- read.xls("nsmb.2675-S2.xlsx", sheet = 4, stringsAsFactor = FALSE)

        editedRNAs_symbols <- editedRNAs_symbols[,1]

        editedRNAs_symbols <- gsub("-R.*", "", editedRNAs_symbols[-1:-2])


        class.editedRNAs <- anno_gtf$gene_id[anno_gtf$gene_symbol %in% editedRNAs_symbols]

        class.editedRNAs <- unique(class.editedRNAs)


      4. Use Fisher exact test to evaluate the enrichment of selected classes of RNAs.


        class.mito <- ranges_genes[seqnames(ranges_genes) == "chrM"]$gene_id


        Sign.inClass <- sum((rownames(res) %in% class.mito) &

        res$padj < 0.01 & res$log2FoldChange > 0)


        NS.inClass <- sum((rownames(res) %in% class.mito) &

        res$padj >= 0.01 & res$log2FoldChange > 0)


        Sign.notClass <- sum(!(rownames(res) %in% class.mito) &

        res$padj < 0.01 & res$log2FoldChange > 0)


        NS.notClass <- sum(!(rownames(res) %in% class.mito) &

        res$padj >= 0.01 & res$log2FoldChange > 0)


        fisher.test(matrix(c(Sign.inClass,

                             Sign.notClass,

                             NS.inClass,

                             NS.notClass), nrow = 2, byrow = T))

    Recipes

    Note: Prepare all buffers in RNase-free conditions.

    1. 1× PBS

      140 mM NaCl

      2.7 mM KCl

      10 mM Na2HPO4

      1.8 mM KH2PO4

    2. vitRIP-100 buffer

      25 mM HEPES-NaOH pH 7.6

      100 mM NaCl

      0.05% (v/v) NP40

      3 mM MgCl2

    3. vitRIP-250 buffer

      25 mM HEPES-NaOH pH 7.6

      250 mM NaCl

      0.05% (v/v) NP40

      3 mM MgCl2

    Acknowledgments

    This work was supported by the German Research Foundation through grant Be1140/9-1. This protocol was originally published in Müller et al. (2020).

    Competing interests

    No competing interests to declare.

    References

  1. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. and Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1): 15-21.
  2. Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B. S., Bravo, H. C., Davis, S., Gatto, L., Girke, T., Gottardo, R., Hahne, F., Hansen, K. D., Irizarry, R. A., Lawrence, M., Love, M. I., MacDonald, J., Obenchain, V., Oles, A. K., Pages, H., Reyes, A., Shannon, P., Smyth, G. K., Tenenbaum, D., Waldron, L. and Morgan, M. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12(2): 115-121.
  3. Izzo, A., Regnard, C., Morales, V., Kremmer, E. and Becker, P. B. (2008). Structure-function analysis of the RNA helicase maleless. Nucleic Acids Res 36(3): 950-962.
  4. Jankowsky, E. and Harris, M. E. (2015). Specificity and nonspecificity in RNA-protein interactions. Nat Rev Mol Cell Biol 16(9): 533-544.
  5. Lambert, N., Robertson, A., Jangi, M., McGeary, S., Sharp, P. A. and Burge, C. B. (2014). RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol Cell 54(5): 887-900.
  6. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R. and Genome Project Data Processing, S. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16): 2078-2079.
  7. Maenner, S., Muller, M., Frohlich, J., Langer, D. and Becker, P. B. (2013). ATP-dependent roX RNA remodeling by the helicase maleless enables specific association of MSL proteins. Mol Cell 51(2): 174-184.
  8. Müller, M., Schauer, T., Krause, S., Villa, R., Thomae, A. W. and Becker, P. B. (2020). Two-step mechanism for selective incorporation of lncRNA into a chromatin modifier. Nucleic Acids Res 48(13): 7483-7501.
  9. Ray, D., Kazan, H., Cook, K. B., Weirauch, M. T., Najafabadi, H. S., Li, X., Gueroussov, S., Albu, M., Zheng, H., Yang, A., Na, H., Irimia, M., Matzat, L. H., Dale, R. K., Smith, S. A., Yarosh, C. A., Kelly, S. M., Nabet, B., Mecenas, D., Li, W., Laishram, R. S., Qiao, M., Lipshitz, H. D., Piano, F., Corbett, A. H., Carstens, R. P., Frey, B. J., Anderson, R. A., Lynch, K. W., Penalva, L. O., Lei, E. P., Fraser, A. G., Blencowe, B. J., Morris, Q. D. and Hughes, T. R. (2013). A compendium of RNA-binding motifs for decoding gene regulation. Nature 499(7457): 172-177.
  10. Robinson, J. T., Thorvaldsdottir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G. and Mesirov, J. P. (2011). Integrative genomics viewer. Nat Biotechnol 29(1): 24-26.
  11. Sloan, K. E. and Bohnsack, M. T. (2018). Unravelling the Mechanisms of RNA Helicase Regulation. Trends Biochem Sci 43(4): 237-250.
  12. St Laurent, G., Tackett, M. R., Nechkin, S., Shtokalo, D., Antonets, D., Savva, Y. A., Maloney, R., Kapranov, P., Lawrence, C. E. and Reenan, R. A. (2013). Genome-wide analysis of A-to-I RNA editing by single-molecule sequencing in Drosophila. Nat Struct Mol Biol 20(11): 1333-1339.
  13. Sutandy, F. X. R., Ebersberger, S., Huang, L., Busch, A., Bach, M., Kang, H. S., Fallmann, J., Maticzka, D., Backofen, R., Stadler, P. F., Zarnack, K., Sattler, M., Legewie, S. and Konig, J. (2018). In vitro iCLIP-based modeling uncovers how the splicing factor U2AF2 relies on regulation by cofactors. Genome Res 28(5): 699-713.
  14. Yoshihama, M., Nakao, A. and Kenmochi, N. (2013). snOPY: a small nucleolar RNA orthological gene database. BMC Res Notes 6: 426.

简介

[摘要] RNA-蛋白质相互作用通常由专门的规范RNA结合域介导。然而,越来越多地观察到通过具有未知特异性的非经典结构域的相互作用,这提出了如何识别RNA靶标的问题。内在的RNA结合特异性的知识有助于理解单个蛋白质的靶标选择性和功能。

所呈现的体外RNA免疫沉淀测定法(vitRIP )揭示固有RNA使用总细胞RNA池作为分离的蛋白质的结合特异性一个库。从细胞或组织中提取的总RNA与纯化的重组蛋白孵育,免疫沉淀RNA-蛋白复合物,并通过深度测序或定量RT-PCR鉴定结合的转录物。这些RNA中丰富的RNA类和核苷酸频率决定了重组蛋白的固有特异性。该简单而通用的方案可适用于任何细胞类型或组织的其他RNA结合蛋白和总RNA文库。



图形摘要:


图1.体外RNA免疫沉淀(vitRIP )方案示意图

[背景]真核细胞包含许多不同的RNA类,具有成千上万的RNA种类以及与之相互作用的高度多样化的蛋白质。根据结合的RNA序列或结构的定义以及相互作用中涉及的蛋白质结构域的不同,RNA-蛋白质相互作用可分为特异性和非特异性(Jankowsky和Harris,2015)。越来越多地观察到通过未知特异性的非经典RNA结合结构域进行的RNA相互作用,这提出了如何识别专用RNA靶标的问题。

因此,通过鉴定其固有的RNA结合特异性将有助于更好地理解特定RNA结合蛋白的功能。由于体内RNA结合通常受合作因子调节,因此固有特异性只能通过体外方法确定(Sloan和Bohnsack,2018)。几个高通量技术,如RNAcompete (雷等人,2013),RNA绑定正SEQ (兰伯特等人,2014) ,并且在体外iCLIP (Sutandy等人,2018)是能够确定RNA结合概况个别蛋白质的体外。这些方法使用RNA寡核苷酸文库或体外转录的RNA库作为底物。此类人工RNA库可能非常复杂,但不代表细胞RNA库并且可能不包含二级结构。

我们开发了一种体外RNA免疫沉淀测定法(vitRIP ),可在总细胞RNA池的背景下揭示分离蛋白的固有RNA结合特异性(图1)(Müller等。(2020年)。施加vitRIP到DEXH解旋酶MLE(maleless )和从阳特异性致死剂量补偿复合物(MSL-DCC)DROS ophila果蝇揭示了长的非编码的特异性并入的机构ROX (在X RNA)的RNA进MSL-DCC (Müller等,2020)。简单的vitRIP方法可通过深度测序或定量RT-PCR在天然条件下(不涉及交联)鉴定与重组蛋白结合的转录物。vitRIP使用复杂的细胞转录组作为底物,并在给定蛋白质的生理背景之外告知其固有的结合特异性。此外,可以容易地控制实验设置并使其适应研究问题。以下协议详细介绍了vitRIP程序,以RNA解旋酶MLE和从果蝇S2雄性细胞中提取的总RNA为例(Müller等,2020)。但是,我们建议vitRIP可以应用于任何RNA结合蛋白,可以重组生产并纯化至接近均质。而且,从任何细胞类型或组织中提取的总RNA可作为一个RNA文库。

关键字:RNA免疫沉淀反应, 体外, RNA结合特异性, 内在的特异性, 重组蛋白, RNA蛋白质相互作用, RNA测序

材料和试剂

注意:请确保所有试剂和材料均不含RNase。

1. 1.5毫升低结合力试管(Sarstedt ,目录号:72.706.700)     

2.果蝇果蝇r S2细胞(果蝇基因组资源中心,https://dgrc.bio.indiana.edu/Home)或任何细胞系/组织     

3. RNeasy迷你套件(Qiagen,目录号:74104)     

注意:我们没有测试其他RNA提取试剂盒,但可以认为制造商并不重要。但是,不同试剂盒中各种RNA种类的可提取性可能有所不同。


4.根据重组蛋白的标签,使用抗FLAG M2亲和凝胶(Sigma,目录号:A2220)或其他合适的亲和树脂     

5.酵母tRNA溶液(Sigma,目录号:R5636)     

6. BSA馏分V粉末(用于封闭)(Sigma,目录号:05482)     

7.无核酸酶的水(Thermo Fisher Scientific,目录号:AM9937)     

8. DNase I重组,无RNase(Sigma,目录号:04716728001)     

9. BSA 20 mg / ml溶液(用于vitRIP )(新英格兰生物实验室,目录号:B9000S)     

10.重组RNasin RNase抑制剂(40 U / μl )(Promega ,目录号:N2515) 

11.腺苷5'-三磷酸二钠盐(ATP)(Sigma,目录号:10127523001) 

12.蛋白酶K(BioCat ,目录号:BIO-37039-BL) 

13. 10%SDS解决方案 

14.苯酚:氯仿:苯醇ROTI Aqua-P / C / I(Carl Roth,目录号:X985.1) 

15. 3 M乙酸钠pH 5.2 

16.糖原(20 mg / ml)(Sigma,目录号:10901393001) 

17.分析用乙醇绝对盐(西格玛,目录号:1009832500) 

18. SuperScript III第一链合成系统(Thermo Fisher Scientific,目录号:18080051) 

19. Fast SYBR Green Mastermix (Thermo Fisher Scientific,目录号:4385610) 

20. Quant- iT Qubit RNA HS检测试剂盒(Thermo Fisher Scientific,目录号:Q32855) 

21.安捷伦RNA 6000 Pico试剂盒(安捷伦,目录号:5067-1513) 

22.安捷伦DNA 1000试剂盒(安捷伦,目录号:5067-1504)或安捷伦高灵敏度DNA试剂盒(安捷伦,目录号:5067-4626) 

23. NEBNext rRNA耗竭试剂盒(人/小鼠/大鼠)(新英格兰生物实验室,目录号:E6310) 

24.用于Illumina的NEBNext Ultra II定向RNA文库制备试剂盒(新英格兰生物实验室,目录号:E7760) 

25.氯化钾 

26.氯化钠 

27. Na 2 HPO 4 

28. KH 2 PO 4 

29. HEPES 

30.氯化镁2 

31. NP40(IGEPAL CA-630,西格玛,目录号:I3021) 

32.抗FLAG M2单克隆抗体(西格玛,目录号:F3165) 

33. 2 × SDS PAGE样品缓冲液(125 mM Tris HCl pH 6.8,4%SDS,20%甘油,0.01%溴酚蓝,200 mM DTT) 

34. 1 × PBS(请参阅食谱) 

35. vitRIP-100缓冲区(请参阅食谱) 

36. vitRIP-250缓冲区(请参阅食谱) 



设备


-80°C冷冻室
DeNovix DS-11分光光度计(Biozym ,目录号:31DS-11)或其他具有254 nm和280 nm波长的分光光度计
冷冻离心机(Eppendorf,型号:5424R)
带加热盖的Eppendorf ThermoMixer C(Eppendorf,目录号:5382000015,5308000003)
旋转器(Neolab ,目录号2-1175)
量子位荧光计(Invitrogen)
Bioanalyzer 2100仪器(安捷伦)
Lightcycler 480仪器II,384孔(Roche,目录号:05015243001)或其他实时PCR系统
(访问)Illumina HiSeq 1500测序平台


软件


STAR –与参考的剪接转录比对(Alexander Dobin ,冷泉港实验室,https://github.com/alexdobin/STAR)(Dobin等人,2013)
Samtools (基因组研究有限公司,桑格研究所,http: //www.htslib.org )(Li等,2009)
Bedtools (犹他大学奎兰实验室,https: //bedtools.readthedocs.io/en/latest/ )
IGV –集成基因组学查看器和igvtools (IGV团队,Broad Institute和圣地亚哥大学UC,http: //software.broadinstitute.org/software/igv/home )(Robinson等人,2011年)
R(R核心团队,https://www.r-project.org)
RStudio(RStudio,https ://rstudio.com )
生物导体(生物导体核心团队,http ://bioconductor.org )(Huber et al。,2015)


程序


笔记:


使用不同批次的纯化蛋白质和总RNA,必须进行≥3个生物学重复实验。
需要为每个实验系统优化条件。蛋白质和/或RNA滴定可能需要获得一个最佳的信噪比。
包括与实验RNA进行的vitRIP反应,但缺少重组蛋白,以对珠子上的RNA背景进行评分。
额外的对照反应(例如,从不同物种中提取的总RNA或在其RNA结合结构域中突变的蛋白质(如果已知))可能有助于进一步表征已鉴定的蛋白质-RNA相互作用的特异性。
在无RNase的条件下工作。


总RNA的制备
在标准条件下培养果蝇S2细胞。有关详细信息,请参阅果蝇基因组资源中心(https://dgrc.bio.indiana.edu/Home)。
通过离心(500 × g ,5分钟,25°C)收集5 × 10 6指数增长的细胞,并弃去上清液。
用500 µl PBS洗涤细胞沉淀,离心收集(500 × g ,5分钟,25°C ),弃去上清液。
直接进行RNA提取或将细胞沉淀物在-80°C下保存,最长持续3个月。
根据制造商的说明,使用Qiagen RNeasy Mini Kit提取总RNA。用50 µl不含RNase的水中洗脱RNA,并用分光光度法测定浓度。5 × 10 6 S2细胞的典型产量在25-50 µg之间。
将提取的RNA储存在-80°C下直至进一步使用(最长持续1年)。避免频繁冷冻/解冻RNA样品以防止降解。
注意:可以使用任何其他细胞系或动物组织。我们成功地测试了成年果蝇的几种雄性和雌性果蝇细胞系以及头部组织。从组织中提取RNA的方法可能需要调整。


高纯度蛋白质的制备
注意:此处介绍的vitRIP协议以特征明确的RNA解旋酶MLE为例。但是,vitRIP可以应用于可以纯化至近乎均质的任何蛋白质。必须针对每种蛋白质分别确定蛋白质纯化策略,并且不能一概而论。


表达感兴趣的蛋白质在适当的异源表达系统(例如,大肠杆菌或昆虫SF21细胞)并纯化至接近均一。通过SDS-PAGE和考马斯亮蓝染色分析蛋白质样品的纯度。
通过适当的方法(在280 nm处吸收或Bradford蛋白测定)确定蛋白浓度。如有必要,请使用旋转浓缩器以增加蛋白质浓度。速冻小等分试样(10-20 µl),并储存在-80°C下。vitRIP所需的蛋白质浓度在很大程度上取决于对RNA底物的亲和力,需要通过应用蛋白质滴定系列凭经验确定,如Müller等人(2003年)中所述。(2020年)。另请参阅步骤D,注释3。
使用前,将蛋白解冻于冰上,旋转以除去潜在的聚集体(20,000 × g ,10分钟,4°C),并将上清液用于vitRIP 。
杆状病毒驱动的SF21细胞表达后,全长FLAG标记的果蝇果蝇MLE的纯化在Izzo等人的文章中有详细描述。(2008 )和Maenner等。(2013) ,仅在为MLE洗脱缓冲液(20mM不同HEPES -KOH pH 7.6的,200毫米的KCl ,1 mM的DTT,2毫摩尔MgCl 2 ,10%甘油)。纯化的MLE的典型浓度为0.5-1 µg / µl,可直接用于vitRIP 。


抗FLAG M2亲和凝胶的制备
注意:始终准备新鲜的树脂。可以使用与亲和标签匹配的任何其他树脂。或者,可以使用包被至蛋白A或蛋白G Sepharose的蛋白特异性抗体。


每个vitRIP反应使用20 µl Anti-FLAG M2亲和凝胶(“珠子”)。
:洗净珠甲在500 DD 1ml PBS中,转化管10倍,离心1分钟×克,4℃,并丢弃上清液。再重复一次此步骤。
封闭磁珠:将dd 2%BSA / PBS / 0.1 mg / ml酵母tRNA固定在磁珠上,并在4°C旋转孵育1小时。在4 × 500 × g下离心1分钟,弃去上清液。该步骤旨在防止蛋白质和/或总RNA与FLAG亲和凝胶发生非特异性结合。
:洗净珠甲在500 DD1毫升vitRIP-100缓冲液中,倒置试管10次,离心1分钟×克,4℃,并丢弃上清液。再重复一次此步骤。
在最后的洗涤步骤中,尽可能多地丢弃上清液,并将珠子放在冰上。


拉下RNA-蛋白质复合物
笔记:


将所有步骤移至冰上并于4 °C离心。使用低约束力的管子。
vitRIP的RNA量取决于单个转录物的丰度以及所研究蛋白质与RNA的亲和力。该量可以通过应用RNA滴定系列凭经验确定。在我们手中,每个vitRIP反应(100 µl体积)总RNA 2 µg效果很好。
vitRIP的蛋白质浓度取决于与RNA的亲和力,需要通过滴定凭经验确定。我们建议从10-250 nM的重组蛋白和2 µg的总RNA开始。如果未观察到结合,则蛋白质量可能会增加。用5-50 nM蛋白进行MLE vitRIP 。


每个反应用vitRIP-100缓冲液稀释2 µg总RNA到最终体积为10 µl。此混合物(1μL)的10%作为一个RNA输入采样,并保持在冰上。
与纯化的蛋白混合剩余的90%的RNA混合物(9微升)的(MLE-FLAG:5纳米和50纳米,分别地)在100μl的在vitRIP-100缓冲液含10微克BSA,总体积0.1 U /不含µl RNase的重组DNase I,0.8 U / µl RNasin RNase抑制剂和1 mM ATP。
孵育30分钟,在25℃(热块与加热盖)。
在20,000 × g ,1分钟,4 °C下旋转反应,并将上清液转移到1.5 ml管中的封闭且平衡的FLAG珠粒(来自S tep C5)。
旋转在室温(22°C)下孵育30分钟。
在500 × g下离心1分钟,在4°C下离心并弃去上清液(=未结合的级分)。
洗珠用1ml vitRIP-100缓冲液:甲DD 1ml缓冲液,旋转在室温下1分钟,离心1分钟,在500 ×克,4℃,并丢弃上清液。
洗珠用1ml vitRIP-250缓冲液:甲DD 1ml缓冲液,旋转在室温下1分钟,离心1分钟,在500 ×克,4℃,并丢弃上清液。
洗珠用1ml vitRIP-100缓冲液:甲DD 1ml缓冲液,旋转1分钟,在室温下和分布在两个管的珠蛋白和RNA提取:75%(750微升)用于RNA提取和25%(250微升)用于蛋白质提取。在4 × 500 × g下离心1分钟,弃去上清液。


蛋白质提取用于蛋白质印迹分析
向每个样品(步骤D10中25%的珠子材料)中,加入20 µl 2 × SDS样品缓冲液。在搅拌下于95°C孵育5分钟。
以500 × g离心1分钟以沉淀出珠子,并通过SDS-PAGE和Western Blot分析上清液,以成功提取蛋白质。
注意:使用抗FLAG抗体以1:5,000(v / v)稀释度通过Western blot分析来分析FLAG标记的MLE的vitRIP 。MLE作为对应145 kDa分子量的单条带可见。


RNA提取用于RNA鉴定
输入样本:中号IX 10%RNA输入(1微升),5μl的10%SDS,10μl的蛋白酶K(10毫克/毫升)和84微升vitRIP-100缓冲液中。孵育在一个热块在55℃下,45分钟搅拌1(与加热盖),000转。
IP样品(75%的珠材料,来自步骤D10):加入5 µl 10%SDS,10 µl蛋白酶K(10 mg / ml)和85 µl vitRIP-100缓冲液。在55°C的恒温器(带加热盖)中孵育45分钟,同时搅拌(1,000 rpm)。
在室温下以500 × g的速度离心管1分钟,以沉淀IP样品的珠。
转移上清(含有洗脱RNA),并转移到一个新的1.5ml管中。
向每个样品(输入和IP)中,加入1体积(约100 µl)的酸性酚:氯仿:异戊醇。将每个试管彻底涡旋10秒钟,并在室温下以20,000 × g离心5分钟。
安全说明:处理苯酚:氯仿:异戊醇时,请始终使用通风橱,并妥善处理含酚废物。


将上层水相(包含RNA;约100 µl)转移至新的1.5 ml试管中,并加入1/10体积的3 M乙酸钠pH 5.2、20 µg不含RNase的糖原和2.5体积的冰冷的100%EtOH 。
在-20°C下孵育> 12小时以沉淀RNA。
在20,000 × g (全速),4°C下离心30分钟。小心地用移液器吸头除去并丢弃上清液,而不会干扰小且可能不可见的RNA沉淀。
加入1 ml冰冷的70%EtOH洗涤沉淀。
在20,000 × g (全速),4°C下离心15分钟。小心取出并丢弃上清液。
用20 µl移液器移走尽可能多的液体,然后风干沉淀(10-15分钟)。
将RNA沉淀重悬于20 µl无RNase的水中。上下吸移10次,然后快速旋转。为了完全溶解,将样品在25°C下温和搅拌(500 rpm)孵育15分钟。
使用Qubit荧光计和Qubit RNA高灵敏度(HS)分析试剂盒定量2 µl纯化的RNA。
注意:IP样品的RNA浓度可能很低,不在Qubit RNA HS分析试剂盒标准溶液的范围内。我们的经验表明,仍可以使用NEB Ultra II定向RNA文库制备试剂盒制备RNA测序文库。可以使用其他处理低投入样品的RNA-Seq文库制备试剂盒。


将RNA储存在-80°C直至进一步使用。


逆转录PCR
笔记:


如果已知目标蛋白质的一个或多个RNA靶标,则可以使用基因特异性引物通过定量RT-PCR分析vitRIP 。以包括未结合的转录物(例如管家基因),因为它是重要的阴性对照。
在MLE vitRIP ,已知的MLE靶roX2 RNA的富集使用roX2特异性引物进行定量并表示为相对于结合级分的输入。丰富的7SK RNA充当了阴性对照。有关引物序列的详细信息,请参见Müller等。(2020年)。在图2A中给出了一个例子。


DNase我消化
混合8 µl RNA,1 µl 10 × DNase I反应缓冲液,0.5 µl DNase I和0.5 µl RNasin 。
在37°C下孵育1小时。存放在冰上。
使用SuperScript III第一链合成系统进行cDNA合成
使用10 µl经DNase I处理的RNA,并按照制造商的说明使用随机六聚体引发进行cDNA合成。
用不含RNase的水稀释cDNA 1:5至1:50(取决于转录本的丰度),并使用转录本特异性引物和FAST SYBR Green Mastermix通过qRT -PCR进行分析。请遵循针对您的Real-Time PCR机的建议。


RNA-Seq文库制备
注意:文库制备方案取决于实验策略。总RNA中高含量的rRNA将干扰分析,应在文库制备之前将其除去。在MLE vitRIP样品,rRNA的使用rRNA基因的探针特异于人/小鼠/大鼠rRNA的,这也对果蝇的rRNA工作得相当好耗尽。这种策略可以识别vitRIP样品中的各种RNA (编码和非编码)。或者,如果实验策略旨在鉴定专门富集的聚腺苷酸化的RNA,则可以使用oligo d(T)珠来富集聚腺苷酸化的RNA。


图书馆准备的起始材料
质量控制:甲nalyze 1纯化输入的微升,并使用RNA 6000微微试剂盒免疫沉淀RNA上的生物分析仪。
根据RNA-Seq文库制备试剂盒的建议选择合适的RNA量。对于使用NEB Ultra II定向RNA文库制备试剂盒制备MLE vitRIP文库,将30-40 ng RNA用作起始材料。
rRNA耗竭
使用NEB rRNA耗竭试剂盒,使用Qubit荧光计定量的30-40 ng RNA进行rRNA耗竭。
遵循制造商关于rRNA消耗的规程(NEB#E6310)。
使用RNA 6000 Pico试剂盒在Bioanalyzer上分析1 µl耗尽rRNA的样品。应主要消除18S和28S rRNA的主要峰(果蝇)。无法获得果蝇RNA样品的RNA完整性编号(RIN)。
图书馆准备
按照NEB Ultra II定向RNA文库制备试剂盒(NEB#E7760)的说明,将消耗rRNA的样品用于文库制备。
使用DNA 1000或DNA高灵敏度试剂盒,在生物分析仪上评估每个库的1 µl样品的质量和数量。具有最大的单个峰的约300 bp的应观察到(图2B)。
序列上的Illumina公司在50个碱基配对末端模式HiSeq1500仪器。测序运行设置为50bp-8bp-8bp-50bp(read1-index1-index2-read2),其中未使用第二个索引文件。






图2. vitRIP分析的代表性示例。有关详细信息,请参阅该原始出版物(穆勒等人,2020年)。A.在不存在或存在ATP的情况下,带有S2 RNA的MLE vitRIP 。roX2和7SK的富集通过分析定量RT -PCR,并且相对于被显示的输入。Ctrl表示缺少重组MLE的纯RNA vitRIP样品。B.由输入和MLE vitRIP样品制备的RNA测序文库的代表性生物分析仪谱。在DNA 1000芯片上分析了文库,并显示了一个单峰,其预期最大值约为300 bp。C.与S2输入相比,roX2和7SK上MLE的代表性vitRIP配置文件的基因组浏览器视图。基因组坐标在图上方给出。D. MA图代表MLE vitRIP的差异表达分析。E.在不同条件下MLE vitRIP-seq数据的主成分分析(PCA)。X轴显示第一个(PC1),y轴显示第二个(PC2)主分量。F.在MLE RNA的U- / A-四核苷酸的频率的比较vitRIP相对于所述输入。感兴趣的RNA类别(roX2,线粒体RNA)突出显示。显示了Spearman的相关性(r)。






数据分析


笔记:


本部分描述了Müller等人于2020年发表的MLE vitRIP的RNA-Seq分析。可能需要根据所选模型系统调整程序。
分析≥3个生物学重复样本,以确保对数据进行有效的解释。
显示了简化的示例代码块,请参阅详细信息:https : //github.com/tschauer/vitRIP_2020。


A. Illumina测序的读取处理     

1.将配对末端的读段(50 bp)映射到参考基因组(果蝇Dm6版本),并使用STAR aligner(版本2.5.3a)和Flybase注释(ftp://ftp.flybase.net)对每个基因的读数进行计数/releases/FB2017_04/dmel_r6.17/gtf/dmel-all-r6.17.gtf.gz),同时过滤出具有多个比对的读取。     



STAR- runThreadN 8


- readFilesCommand gunzip解-C


- quantMode GeneCounts


-基因组目录$ { path_to_star_index }


- sjdbGTFfile $ { gtf_file }


- readFilesIn $ {fastq_file } _ 1.txt.gz $ {} fastq_file _2.txt.gz


- outFileNamePrefix $ { fastq_file }。


- outSAMtype BAM SortedByCoordinate


- limitBAMsortRAM 50亿


- outFilterMultimapNmax 1


2.计数的对齐使用读取总数samtools (1.7版本),并生成标准化(每百万读取)bedgraph文件使用genomeCoverageBed (bedtools版本2.27.1)。     



总计=` samtools视图-c $ { bam_file }`


scaler =`echo“ scale = 10; 1 /($ {total} / 1000000)” | BC `


genomeCoverageBed - IBAM $ { bam_file } - BG -split进制$ {缩放}> $ { bedgraph_file }


3.转换bedgraph到TDF文件使用igvtools (98年3月2日的版本)。     



igvtools toTDF -z 5 $ { bedgraph_file } $ { tdf_file } $ { path_to_fasta }


4.在IGV浏览器中可视化tdf文件(图2C)。     



B.差异表达分析     

将计数表(由STAR生成的* ReadsPerGene.out.tab文件)加载并合并到R (版本3.6.1)中,并在75%的样本中保留读取计数至少为1的基因。


过滤< -应用(count_table ,1,函数(x)的长度(X [X> 1])> = NcoI位(count_table )/1.333)


count_table_filtered < -count_table [filter,]


使用DESeq2软件包(1.26.0版)进行差异表达分析,同时将复制信息添加为批处理变量。


dds < -DESeqDataSetFromMatrix (countData = count_table_filtered ,


                                    colData = column_data ,


                                    设计=〜批+样本)


dds < -DESeq (dds )


注意:应该直接相互比较的样本必须安装在相同的DESeq2模型中。


使用结果函数(DESeq2)获得log2FoldChange估计值和调整后的p值。将调整后的p值截止值设置为0.01。


res < -results (dds ,


                  对比度= c(“样本”,“ IP”,“输入”),


                  IndependentFiltering = FALSE)


res.sign <-子集(res,padj <0.01)


将结果可视化为MA图,将红色显着的基因标记为橙色,将示例基因标记为橙色(图2D)。


plot(res $ baseMean ,res $ log2FoldChange,


      log =“ x”,xlim = c(1,1e6),ylim = c(-10,10),


      xlab =“对数均值”,ylab =“ log2倍数变化”,


      col = rgb (0,0,0,0.1),pch = 19,cex = 0.25)


points(res.sign $ baseMean ,res.sign $ log2FoldChange,


        col = rgb (0.8,0,0,0.5),pch = 19,cex = 0.25)


favorite_gene < - rownames (RES)%以%C( “FBgn0019660”, “FBgn0019661”)


points(res $ baseMean [favorite_gene],res $ log2FoldChange [favorite_gene],


        col = rgb (0.9,0.6,0,1),pch = 19,cex = 1)


C.主成分分析(PCA)     

使用ComBat函数(sva软件包版本3.32)对归一化的读取计数进行批处理效果校正。


batch_var < -colData (dds )$ batch


modcombat < -model.matrix (〜sample ,data = colData (dds ))


log2_corrected_counts < -战斗(DAT = LOG2(计数(DDS ,归= TRUE)+1),


                                      批处理= batch_var ,mod = modcombat ,


                                      par.prior = TRUE,prior.plots = FALSE)


在批次校正后的计数s上执行PCA并将其绘制成图(图2E)。


pca < -prcomp (t(log2_corrected_counts),scale。= TRUE)


percentVar <-回合(pca $ sdev ^ 2 / sum(pca $ sdev ^ 2)* 100,1)[1:10]


情节(pca $ x [,1],pca $ x [,2],


     col = color_palette [ colData (dds )$ sample],pch = 16,cex = 0.7,


     xlab = paste(“ PC1(”,percentVar [1],“%)”,sep =“”),


     ylab = paste(“ PC2(”,percentVar [2],“%)”,sep =“”))


D.核苷酸频率分析     

通过将FlyBase注释转换为TxDb对象(GenomicFeatures软件包版本1.36.4)来设置注释,并使用BSgenome软件包(版本1.52)提取基因序列。


txdb < -makeTxDbFromGFF (“ dmel-all-r6.17.gtf”,format =“ gtf ”)


seqlevelsStyle (txdb )<-“ UCSC”


seqlevels (txdb )<- gsub (“线粒体基因组”,“ chrM ”,seqlevels (txdb ))


染色体<-c(“ chrX”,“ chr2L”,“ chr2R”,“ chr3L”,“ chr3R”,“ chr4”,“ chrM”,“ chrY”)


range_genes < -genes (txdb )


ranges_genes < -keepSeqlevels (ranges_genes ,染色体,pruning.mode =“粗略”)


genic_seq <-BSgenome :: getSeq(BSgenome.Dmelanogaster.UCSC.dm6,range_genes)


使用寡核苷酸频率函数(Biostrings软件包版本2.52)计算基因核苷酸频率。组寡核苷酸宽度要么4,5 ,或6,滑动窗口步骤1和as.prob为TRUE。


genic_4merfreq <-寡核苷酸频率(genic_seq ,width = 4,step = 1,as.prob = T)


行名(genic_4merfreq)<-名称(genic_seq )


选择显着富集的基因(调整后的p值<0.01,log2 [IP /输入]> 0)。


res.sign_enriched <-子集(res,padj <0.01&res $ log2FoldChange> 0)


select_enriched < - rownames (genic_4merfreq)%以%rownames (res.sign_enriched )


select_notenriched < - !(rownames以%(genic_4merfreq)%rownames (res.sign_enriched ))


freqs_enriched <-genic_4merfreq [ select_enriched ,]


freqs_notenriched <-genic_4merfreq [ select_notenriched ,]


可视化盒形图富集或不富集的基因的核苷酸频率。按序列的中位数频率对序列进行排序。


freq_order < -order (apply(freqs_enriched ,2,中位数),递减= T)


freqs_enriched < - freqs_富集[ ,freq_order ]


freqs_notenriched < -freqs_ notenriched [ ,freq_order ]


freqs_merged < -c(as.list (data.frame (freqs_enriched )),


                     as.list (data.frame (freqs_notenriched )))


boxplot (freqs_merged [rep(1:25,each = 2)+ c(0,ncol (freqs_enriched ))],


        col = c(“ darkred ”,“ darkgrey ”),las = 2,outline = F,


        ylab =“频率”)


之间的关联可视化vitRIP富集(即,LOG2 [IP /输入])和核苷酸频率为散点图。计算Spearman的相关性以评估该关系(图2F)。


res_freq < -merge (res,genic_4merfreq,by =“ row.names ”)


plot(res_freq $ log2FoldChange,res_freq [,“ TTTT”],


     main =“”,xlab =“ log2倍数变化”,ylab =“频率UUUU”,


     xlim = c(-8,8),col = rgb (0,0,0,0.1),pch = 19,cex = 0.25)


退位(v = 0,col =“ grey32”)


科尔< -  COR (res_freq $ log2FoldChange,res_freq [ “TTTT”],方法= “斯皮尔曼”)


title(paste(“ cor =”,round(corr,2)),line = 0.5,cex.main = 1)


E.评估富集/未富集的RNA类     

提取的注释(例如,外显子,内含子,5'和3'非翻译区,以及用于snRNAs,snoRNAs ,酶和tRNA)从GTF注释(即,DMEL-全r6.17.gtf)。


anno_gtf < -import.gff (“ dmel-all-r6.17.gtf”)


genes_3UTR < -anno_ gtf [ anno_gtf $ type ==“ 3UTR”,]


genes_3UTR_length <-聚合(宽度(genes_3UTR),


                                     由=列表(genes_3UTR $ gene_id),FUN =最大值)


select_q95 <-genes_3UTR_length [ ,2]>分位数(genes_3UTR_length [,2],0.95)


class.long3UTR < -基因_3UTR_length [,1] [select_q95]


注意:选择长3'UTR转录本作为3'UTR最长5%的转录本。在某些情况下,当一个基因带有3'UTR同工型时,便会选择具有最长3'UTR的同工型。


的提取物注释果蝇从的snoRNA类SnOPY数据库(的snoRNA orthological基因数据库; http://snoopy.med.miyazaki-u.ac.jp)(吉滨。等人,2013年)。使用org.Dm.eg.db软件包(版本3.8.2)将基因名称转换为基因ID。


snoRNA_snopy < -函数read.table (“snoRNA_snopy.txt”,首标= T,九月= “\ t”的,stringsAsFactors = F)


snoRNA_HACA <-snoRNA_snopy $ snoRNA.name [ snoRNA_snopy $ Box ==“ H / ACA”]


class.snoRNA_HACA < -mapIds (org.Dm.eg.db ,keys = snoRNA_HACA ,


                                  键类型=“ SYMBOL”,列=“ FLYBASE”)


从已发表的数据中提取关于腺苷到肌苷编辑的RNA的信息(St Laurent et al。,2013)。将excel表读到R(gdata软件包版本2.18.0),然后从GTF注释中选择唯一的基因ID。


editedRNAs_symbols < -read.xls(“ nsmb.2675-S2.xlsx”,工作表= 4,stringsAsFactor = FALSE)


editedRNAs_symbols < -editedRNAs_符号[ ,1]


editedRNAs_symbols < - GSUB (“-R *”。 “”,editedRNAs_symbols [-1:-2])


class.editedRNAs < -anno_gtf $ gene_ id [ anno_gtf $ gene_symbol %in%editedRNAs_symbols ]


class.editedRNAs < -unique (class.editedRNAs )


使用Fisher精确检验评估选定类别的RNA的富集。


class.mito < - ranges_基因[ seqnames (ranges_genes )== “ CHRM ”] $ gene_id


Sign.inClass < -总和((rownames (RES)%以%class.mito )&


res $ padj <0.01和res $ log2FoldChange> 0)


NS.inClass < -    总和((rownames (RES)%以%class.mito )&


res $ padj > = 0.01&res $ log2FoldChange> 0)


Sign.notClass < -总和(!(rownames (RES)%的%class.mito )


res $ padj <0.01和res $ log2FoldChange> 0)


NS.notClass < -   总和(!(rownames (RES)%的%class.mito )


res $ padj > = 0.01&res $ log2FoldChange> 0)


fisher.test (matrix(c(Sign.inClass ,


                         Sign.notClass ,


                         NS.inClass ,


                         NS.notClass ),nrow = 2,byrow = T))


菜谱


注意:在无RNase的条件下准备所有缓冲液。


1 × PBS
140毫米氯化钠


2.7毫米氯化钾


10毫米Na 2 HPO 4


1.8毫米KH 2 PO 4


vitRIP-100缓冲区
25 mM HEPES-NaOH pH 7.6


100毫米氯化钠


0.05%(v / v)NP40


3毫米MgCl 2


vitRIP-250缓冲区
25 mM HEPES-NaOH pH 7.6


250毫米氯化钠


0.05%(v / v)NP40


3毫米MgCl 2


致谢


这项工作得到了德国研究基金会的资助(Be1140 / 9-1)。该协议最初发表在Müller等人的文章中。(2020年)。


利益争夺


无利益冲突可宣布。


参考


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Copyright: © 2021 The Authors; exclusive licensee Bio-protocol LLC.
引用:Müller, M., Schauer, T. and Becker, P. B. (2021). Identification of Intrinsic RNA Binding Specificity of Purified Proteins by in vitro RNA Immunoprecipitation (vitRIP). Bio-protocol 11(5): e3946. DOI: 10.21769/BioProtoc.3946.
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