Chapter 5
Bacterial small regulatory RNAs ( sRNAs ) are key actors in the finetuning of gene expression , ensuring rapid adaptation of bacteria to their ever-changing environment . 
sRNAs typically act at the posttranscriptional level by base-pairing to their messenger RNA ( mRNA ) targets in the 5 ′ untranslated region ( UTR ) [ 1 ] . 
Remarkably , limited complementarity between the sRNA and its targets not only allows the regulation of multiple mRNAs by a single sRNA but also the regulation of one mRNA by multiple sRNAs . 
This added complexity creates an extensive regulatory network where sRNAs act as bridges between various cellular metabo-lisms [ 2 ] . 
In the last decades , such networks were studied and specific sRNA targetomes were , in part , characterized . 
Since then , this field of study witnessed an explosion of technological advances [ 3 ] that exposed the versatility of sRNAs in terms of possible pairing sites and mechanisms of action . 
Indeed , these short regulators not only pair in the 5 ′ UTR of mRNAs , they can also target the coding sequence ( CDS ) [ 4 , 5 ] or could even pair in the 3 ′ UTR of targets . 
Moreover , sRNAs can regulate the translation of mRNA targets without directly affecting the stability of the transcript [ 6 ] . 
This new knowledge exposed a significant lack in efficacy of the classical techniques used to identify targets of sRNAs , reviving the challenge of sRNA target identification in bacterial cells . 
To tackle this issue , we developed and optimized a technique that combines RNA affinity purification and RNA sequencing ( RNAseq ) allowing genome-wide identification of sRNA -- mRNA interaction in bacterial cells . 
The assay is called MAPS : MS2 affinity purification coupled to RNAseq . 
Here , we describe the MAPS protocol in detail . 
Briefly , a sRNA is tagged with an MS2 RNA aptamer and expressed in vivo . 
Following cell lysis , tagged sRNAs are purified through affinity chromatography . 
Eluted RNAs are analyzed by highthroughput RNAseq and the ratio of enriched mRNAs in the tagged vs. untagged sRNA experiments is representative of the interaction between the two RNAs . 
Moreover , we describe the bioinformatic pipeline used to analyze MAPS data exploiting the Galaxy Project Platform . 
Ultrapure water should be used for every solution . 
Make sure to work with RNase-free material to avoid degradation of RNAs throughout the experiment . 
3 Methods
2 . 
Disposable Bio-Spin chromatography columns . 
3 . 
Purified MS2-MBP fusion protein . 
MBP : maltose-binding protein [ 7 ] . 
4 . 
Buffer A : 20 mM Tris -- HCl ( pH 8 ) , 150 m MgCl2 , 1 mM DTT , and 1 mM PMSF . 
5 . 
Elution buffer : Buffer A supplemented with 15 mM maltose . 
6 . 
Phenol-water , pH 6 : Melt phenol crystals at 65 °C and preheat an equal volume of ultrapure water to the same temperature . 
Carefully mix equal volume of liquid phenol and ultrapure water . 
Add 0.1 % w / ( phenol volume ) 8-hydroxyquinoline and mix carefully . 
Incubate 5 min at 65 °C . 
Aliquot in 50 mL conical tubes . 
Keep at 4 °C , protect from light . 
7 . 
25:24:1 ( v/v/v ) phenol-chloroform-isoamyl alcohol . 
8 . 
Glycogen . 
9 . 
95 % v/v ethanol . 
10 . 
75 % v/v ethanol . 
All steps should be performed on ice . 
All buffers should be at 4 °C . 
1 . 
Let the cell pellets thaw on ice for 30 min . 
2 . 
Resuspend the pellet in 2 mL of Buffer A ( see Notes 6 and 7 ) . 
3 . 
Chill the French Press cell by burying it in ice before performing the lysis . 
4 . 
Break the bacterial cells using a French Press at 430 psi , 3 times per sample . 
Keep samples on ice at all times ( see Note 8 ) . 
5 . 
Clear the lysates by centrifugation at 16,000 × g at 4 °C , 30 min . 
6 . 
Transfer the soluble fraction ( lysate ) to clean tubes . 
Keep on ice . 
All steps should be performed on ice . 
All buffers should be at 4 °C . 
1 . 
Add 75 μL amylose resin to a Bio-Spin disposable chromatography column . 
2 . 
Equilibrate the column three times with 1 mL of Buffer A ( see Note 9 ) . 
3 . 
Use the provided stopper to seal the column . 
Dilute 100 pmol of MS2-MBP coat protein in 1 mL Buffer A. Apply the protein solution to the sealed column and let stand for 5 min . 
4 . 
Remove the stopper and let the column drain . 
5 . 
Wash the column twice with 1 mL of Buffer A. 6 . 
Load the bacterial lysate onto the column , 1 mL at a time and let the column drain . 
7 . 
Wash the column 5 times with 1 mL of Buffer A ( see Note 10 ) . 
8 . 
Insert the column in a clean RNase-free collecting tube . 
Elute with 1 mL of Elution Buffer . 
9 . 
Split the column output into two 1.5 mL microtubes . 
10 . 
Add 1 volume of phenol-chloroform-isoamyl alcohol to each tube and mix . 
Centrifuge at 16,000 × g at room temperature , 10 min . 
11 . 
Transfer the aqueous phase in clean microtubes containing 20 mg of glycogen ( see Note 11 ) . 
12 . 
Add two volumes of 95 % EtOH . 
Mix thoroughly and precipitate overnight at − 80 °C . 
13 . 
Centrifuge the samples at 16,000 × g at 4 °C , 30 min . 
14 . 
Remove the supernatant very carefully and add 500 μL of ice-cold 75 % EtOH to the pellets . 
Centrifuge the samples at 16,000 × g at 4 °C , 5 min ( see Note 12 ) 
15 . 
Remove the supernatant . 
Let the RNA pellets dry completely . 
16 . 
Resuspend the pellets in 86 μL of ultrapure H2O . 
Proceed to the next step ( see Note 13 ) . 
1 . 
Add 10 μL of 10 × TURBO ™ DNase Buffer and 4 TURBO ™ DNase to each sample . 
2 . 
Incubate at 37 °C , 30 min . 
3 . 
Add 100 μL of phenol-chloroform-isoamyl alcohol to each tube and mix . 
Centrifuge at 16,000 × g at room temperature , 10 min . 
4 . 
Add 2.5 volumes of 95 % EtOH . 
Mix thoroughly and precipitate overnight at − 80 °C . 
5 . 
Centrifuge the samples at 16,000 × g at 4 °C , 30 min . 
6 . 
Remove the supernatant carefully . 
Let the RNA pellets dry completely . 
7 . 
Resuspend the dried pellets in 6 μL of ultrapure H2O . 
8 . 
Quantify and verify the quality of the RNA using Agilent Nano Chip in a Bioanalyzer 2100 . 
9 . 
Prepare the cDNA libraries with the ScriptSeq ™ v2 RNA-Seq Library Preparation Kit from Illumina . 
10 . 
Sequence the libraries in both directions using Illumina MiSeq . 
Bioinformatics tools used are freely available on the Galaxy Platform [ 8 ] ( https://usegalaxy.org/ ) . 
The following procedure allows the alignment and visualization of the RNA sequencing reads on the genome of interest . 
Note that the procedure has to be performed independently for the experimental data set ( MS2-sRNA ) and the control data set ( sRNA ) . 
Here , the procedure will be detailed for one experimental data set with paired-end sequencing . 
Refer to Fig. 1a for visual workflow and to Table 1 for Galaxy Project tool details . 
name = `` NAME_EXPERIMENT '' description = `` BedGraph format '' visibility = full ( see Note 15 ) . 
8 . 
Navigate to the UCSC Microbial Genome Browser [ 13 ] ( http://microbes.ucsc.edu/ ) . 
9 . 
Enter the name of the reference genome that you need . 
Here , we used Escherichia coli K12 . 
10 . 
Click on the Manage custom tracks button and add your custom tracks ( see Note 16 ) . 
11 . 
Click on View in Genome Browser . 
You can now search for a gene name or genomic position to visually compare read alignment on the genome ( Fig. 2 ) ( see Note 17 ) 
The following procedure is performed to assign the RNAseq reads to gene names and to compare the experimental data set ( MS2-sRNA ) to the control ( sRNA ) data set . 
To perform this step , three files are required and need to be processed . 
Steps 1 and 2 are performed on the SAM files from step 4 in Subheading 3.5.1 for the MS2-sRNA experimental condition and for the sRNA control condition . 
Steps 4 and 5 are performed on the file acquired in step 3 of this section . 
Refer to Fig. 1b for visual workflow and to Table 1 for Galaxy Project tool details . 
1 . 
Run the Convert SAM to interval tool with default parameters on each SAM files . 
2 . 
On the converted files , run the Remove beginning of a file tool . 
These data sets are ready to use . 
3 . 
Through the NCBI database , download the Gene Data Bank of the bacterial strain corresponding to your experimental strain . 
This is a . 
txt file . 
4 . 
Upload files : Gene Data Bank file . 
Set the file type as Interval 
5 . 
Run Compute an expression on every row with the Add Interval parameter c3 -- c2 . 
6 . 
On the output from step 5 , go to edit attributes and set the Database/Build as the reference genome used in step 4 of Subheading 3.5.1 . 
This data file is ready to use . 
7 . 
Run Join tool . 
In the parameters , join your sample data set ( MS2-sRNA or sRNA ; step 2 in Subheading 3.5.2 ) with the Gene Data Bank data set ( see step 6 ) . 
Use default parameters . 
8 . 
Run the Group tool for each output files obtained at step 7 . 
Select the following parameters : ( a ) Group by column : column 23 . 
( b ) Ignore case by grouping : no . 
( c ) Ignore lines beginning with these characters : select all characters except for the dot ( . ) . 
( d ) Operation : insert Operation 1 , Count Distinct on column 5 . 
( e ) Operation : insert Operation 2 , Mean on column 24 . 
9 . 
Run Join two Datasets side by side on a specified field tool with outputs from step 8 . 
Join the MS2-sRNA sample using column 1 with the sRNA control sample using column 1 . 
Set the following parameters : ( a ) Keep lines of first input that do not join with second input : Yes . 
( b ) Keep lines of first input that are incomplete : No . 
( c ) Fill Empty columns : yes . 
( d ) Fill column by : Single fill value . 
( e ) Fill value : 1 . 
10 . 
Download the output file . 
Open the file with Microsoft Excel ( or any similar software ) . 
The first 3 columns represent the gene name , the number of reads and the gene length for the MS2-sRNA experiment . 
The last three columns represent the same for the sRNA control . 
11 . 
Relativize the number of reads ( see Note 18 ) : ( a ) From the Illumina Dashboard , note the total number of reads and the total number of reads mapped for each sample . 
( b ) For the MS2-sRNA , calculate the relativized number of reads : Reads / ( ( total number of reads ) X ( the total number of reads mapped for `` MS2-sRNA '' ) ) ( c ) For the MS2-sRNA , calculate the relativized number of reads : Reads / ( ( total number of reads ) X ( the total number of reads mapped for `` sRNA '' ) ) 12 . 
Calculate the enrichment ratio for each gene between the MS2-sRNA experiment and the sRNA control ( Fig. 3 ) 
4 Notes
It is strongly recommended to perform in vivo validation of putative targets identified by MAPS . 
Various methods are useful to perform such validation . 
Northern blotting will be effective in assessing the sRNA-dependent modulation of the target at the RNA level [ 14 ] . 
Translational regulation can be determined using translational reporter-gene fusions . 
These procedures will not be detailed here as they are beyond the scope of this protocol . 
input sample will be useful at a later step ( refer to Note 11 ) . 
Various methods can be used for RNA extraction . 
We suggest the hot-phenol RNA extraction [ 16 ] . 
6 . 
Do not resuspend all the pellets at once . 
Follow steps 2 -- 4 for each sample individually . 
Keep all samples on ice at all times . 
7 . 
Depending on your experimental conditions ( for example if the cells were harvested at high OD600nm ) , the pellets can be resuspended in 3 mL . 
8 . 
The number of passages on the French Press can vary according to your experimental conditions . 
For example , if cells were harvested at high OD600nm , break the cells 4 times per sample . 
9 . 
Use a clean 10 mL-syringe to push the first few drops out of the column . 
Then , let the elution carry on by gravity only . 
10 . 
Number of washes is an important parameter and should be optimized for each experiment . 
11 . 
Addition of glycogen is very important , if not essential , to be able to recover the small RNA pellets and avoid their loss after precipitation . 
Glycogen must be in contact with the RNAs ( the aqueous phase ) before the addition of ethanol . 
12 . 
Be very careful as the RNA pellets do n't always stick to the bottom of the tube . 
Remove ethanol using a micropipette . 
Avoid using a vacuum system . 
13 . 
At this step , it is possible and highly recommended to test your samples by Northern blot . 
Compare the input samples ( see Note 5 ) with the output samples . 
14 . 
This parameter can be adjusted . 
If using the FastQ Quality Trimmer with the threshold at a score of 20 causes the loss of too many sequences , the threshold can be lowered . 
If the overall quality of sequences is above 20 , we do not perform the FastQ Quality Trimmer . 
15 . 
This header is essential for the next step . 
It informs the UCSC Microbial Genome Browser on the type of data contained in the file and allows you to name your experiment . 
16 . 
Ideally , add both the control track ( sRNA MAPS ) and the experimental track ( MS2-sRNA MAPS ) to compare the reads aligned in each condition . 
17 . 
Enrichment at a specific location on a gene of interest does n't always represent the exact pairing site of the sRNA . 
Additional experimental data is required to validate the pairing site localization . 
18 . 
Normally , reads must be relativized taking into consideration the size of the genes they mapped to . 
However , since we calculate the enrichment ratio of reads between the two conditions , gene size is irrelevant in our analysis