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| Status |
Public on May 04, 2022 |
| Title |
RNAseq_TSB_r2_T0 |
| Sample type |
SRA |
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| Source name |
Staphylococcus aureus
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| Organism |
Staphylococcus aureus |
| Characteristics |
strain: USA300 rnc:HTF
media: TSB
treatment: Sample harvested at OD600 = 3 after growth in TSB
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| Treatment protocol |
Following the shift, cells were UV cross-linked through 254 nm UV (1000 mJ/cm2) using a Vari-X-Linker (UVO3 (McKellar et al, 2020; van Nues et al, 2017). Afterwards, cells were harvested onto filters by vaccuum filtration, flash-frozen in liquid nitrogen and stored at -80°C until their processing.
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| Growth protocol |
S. aureus cells expressing HTF-tagged RNase III (or an untagged, wild type control) were grown to saturation in TSB at 37 °C with shaking. Cells were then diluted into 500 mL of fresh TSB to an OD600 value of 0.05 and grown to an OD600 of 3. Subsequently, cells were harvested by vacuum filtration and shifted to an equivalent volume of stress media, either RPMI or LPM. As as control, cells were shifted back into their original TSB medium. For CLASH, samples were taken after 15 minutes of incubation in the stressful media. For RNAtag-Seq, samples were taken after 5, 10, 15 and 30 minutes.
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| Extracted molecule |
total RNA |
| Extraction protocol |
For CLASH, the cells were washed off the filters using 25mL of cold phosphate buffer saline and pelleted by centrifugation. Cells were subsequently resuspended in 2 volumes of TN150-lysostaphin buffer (50 mM Tris pH 7.8, 150 mM NaCl, 100 µg/mL lysostaphin, 0.1% NP-40, 0.5% Triton X-100) and transferred to 5 mL screw-cap tubes (Eppendorf). Sixty µL of DNase RQ1 and 10 µL of SUPERase-In was added and the mixture was incubated at 37°C for 30 min to lyse the cells. Afterwards, 3 volumes/cell weight of zirconia beads (Thistle Scientific; 0.1 µm) were added and the mixture was vortexed vigorously 5 times for one minute with incubation on ice between each samples. Two volumes/cell weight of cold TN150 anti-peptidase (50 mM Tris pH 7.8, 150 mM NaCl, 1 Roche EDTA-free mini pellet, 0.1% NP-40, 0.5% Triton X-100 and 10 mM EDTA) was added before centrifugation for 30 minutes at 10000 g at 4°C. Subsequently, 75 µL of anti-FLAG® M2 Magnetic Beads (pre-washed with TN150 buffer) was added and incubated with the lysate for 2 hours at 4°C. The beads were then washed three times for five minutes with 2 mL of TN1000 buffer (50 mM Tris pH 7.8, 1M NaCl, 0.1% NP-40, 0.5% Triton X-100) and three times with 2 mL of TN150 buffer (50 mM Tris pH 7.8, 150 mM NaCl, 0.1% NP-40, 0.5% Triton X-100) for 5 minutes. Beads were subsequently resuspended in 250 µL of TN150 and 10 µL of homemade GST-TEV protease was added. The samples were rotated for 2 hours at room temperature. TN150 was added to a final volume of 600 µL and 550 µL of the TEV eluates were incubated with RNace-it™ (Agilent Technologies, 1 µL of 1:100 dilution) for exactly seven minutes at 20°C. Subsequently, 500 µL of the mixture was added to a tube with 0.4 g guanidium-HCl and vortexed vigorously to inactivate the RNases. Sodium chloride and imidazole (pH 8.0) were added to a final concentration of 300 mM and 10 mM respectively, and the mixture was transferred to 50 µL of Ni-NTA agarose beads (QIAGEN), pre-equilibrated with wash buffer I (300 mM NaCl, 10 mM imidazole, 6M GuHCl, 50 mM Tris-HCl pH 7.8, 0.1% NP40, 5 mM β- mercaptoethanol and 0.5% Triton X-100) and incubated at 4°C overnight on a rotator. The next day the beads were transferred to Pierce snap-cap columns (Thermo Scientific) and washed twice with 500 µL wash buffer I and three times with 500 µL 1x NP-PNK buffer (10 mM MgCl2, 50 mM Tris-HCl pH 7.8, 0.1% NP40, 5 mM βMe and 0.5% Triton X-100). For RNAtag-Seq, total RNA was extracted from the cells using standard guanidinium thiocyanate-phenol-chloroform extraction (Chomczynski et al, 1987).
For CLASH, the isolated RNA-RNA complexes had 3' and 5' Illumina adaptors ligated on their respective ends as described previously (McKellar et al, 2020; Granneman et al, 2009). For RNAtag-Seq, the total RNA samples were processed as described previously (Shishkin et al, 2015).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
Ribosome-depleted total RNA extracted before shifting cells to LPM media, second replicate
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| Data processing |
CLASH was performed on JKD6009 rnc::HTF in biological triplicates, with a single biological replicate for the wild type control. The cDNA libraries were sequenced on Illumina HiSeq 2500 machine. For USA300, four biological replicates of rnc::HTF were performed and two replicates of the untagged wild type. These were sequenced on a NovaSeq 6000. Raw sequencing reads in fastq files were processed using the CLASH data analysis pipeline developed by Sander Granneman, which uses tools from the pyCRAC package (version 1.5.0) (Webb et al., 2014). This PreProcessForHyb.py script first uses flexbar to trim the 3' adaptor sequence from the data and remove low quality nucleotides (Phred score <23). Subsequently, pyBarcodeFilter.py demultiplexes the data from the in-read barcode sequences found in the 5’ adapter. Using the random nucleotide information present in the 5’ adaptor sequences, the reads are then collapsed to remove potential PCR duplicates using pyFastqDuplicateRemover.py. For RNAseq, the CRAC_pipeline_PE.py script was used from the pyCRAC package. This firstly demultiplexes the data using pyBarcodeFilter.py and the in-read barcode sequences found in the L5 5’ adapters. Flexbar then trims the reads to remove 3’-adapter sequences and poor-quality nucleotides (Phred score <23). Using the random nucleotide information present in the L5 5’ adaptor sequences, the reads are then collapsed to remove potential PCR duplicates using pyFastqDuplicateRemover.py. The reads were then mapped to the JKD6008 and USA300 genomes using Novoalign (www.novocraft.com).
For mapping to the USA300 genome, we generated an annotation file in the Gene Transfer Format (GTF). This file contained the start and end positions of each gene on the chromosome as well as its encoded product (e.g. sRNA, protein- coding, tRNA). To generate this file, we used the Rockhopper software (Tjaden, 2015) on USA300 rRNA-depleted total RNA-seq data (Stuart McKellar and Ivayla Ivanova, unpublished) and a minimal GTF file obtained from ENSEMBL (without UTR information). The resulting GTF file contained information not only on the coding sequences, but also more complete 5’ and 3’ UTR coordinates. Overlapping features, such as sRNAs that overlap with UTRs of protein-coding genes, were removed from the GTF file. For USA300, we utilised already published genome annotations (Howden et al, 2010).
The hyb pipeline (Travis et al, 2014) was then used to detect RNA-RNA interactions. This uses bowtie2 and BLAST to map each half of the RNA-RNA interaction to the genome. Afterwards, a statistical analysis pipeline (Waters et al, 2017) was used to filter for statistical signifiance and intermolecular interactions were extracted, annotated and filtered for minimum folding energy cut-off using custom scripts. Finally, interactions involving only 'bona fide' sRNAs (Lui et al, 2018) were filtered from the data. To calculate enriched RNA structures, the MFE of structures were calculated using UNAFold and compared to a randomly shuffled control. The structures were converted into Vienna dot-bracket notation and double-stranded structures between 5 and 10 base-pairs were extracted.
Normalization and differential gene expression analysis on the RNAtag-Seq data were done with DESeq2 package (Love et al., 2014).
Genome_build: NCBI:txid451515 (USA300), NCBI:txid546343 (JKD6009)
Supplementary_files_format_and_content: CLASH data: Supplementary Tables 3 and 4 are Excel tables describing the RNA-RNA complexes. The .hyb files give the unique, annotated output from hyb. For RNAseq, bedgraph files are provided which detail peaks that can be visualised by a genome browser, and the pyReadCounters.gtf output detailing the hit tables.
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| Submission date |
Feb 04, 2021 |
| Last update date |
May 04, 2022 |
| Contact name |
Sander Granneman |
| E-mail(s) |
[email protected]
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| Organization name |
University of Edinburgh
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| Department |
Centre for Synthetic and Systems Biology
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| Lab |
Granneman lab
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| Street address |
Mayfield Road, Kings Buildings, Waddington building, room 3.06
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| City |
Edinburgh |
| ZIP/Postal code |
EH9 3JD |
| Country |
United Kingdom |
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| Platform ID |
GPL27158 |
| Series (1) |
| GSE166151 |
RNase III CLASH in MRSA uncovers sRNA regulatory networks coupling metabolism to toxin expression |
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| Relations |
| BioSample |
SAMN17777996 |
| SRA |
SRX10021219 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5064660_RNAseq_TSB_r2_minusReads.bedgraph.gz |
1.6 Mb |
(ftp)(http) |
BEDGRAPH |
| GSM5064660_RNAseq_TSB_r2_plusReads.bedgraph.gz |
1.7 Mb |
(ftp)(http) |
BEDGRAPH |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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