MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria
Bibliographic record
Abstract
Although small regulatory RNAs (sRNAs) are widespread among the bacterial domain of life, the functions of many of them remain poorly characterized notably due to the difficulty of identifying their mRNA targets. Here, we described a modified protocol of the MS2-Affinity Purification coupled with RNA Sequencing (MAPS) technology, aiming to reveal all RNA partners of a specific sRNA in vivo. Broadly, the MS2 aptamer is fused to the 5' extremity of the sRNA of interest. This construct is then expressed in vivo, allowing the MS2-sRNA to interact with its cellular partners. After bacterial harvesting, cells are mechanically lysed. The crude extract is loaded into an amylose-based chromatography column previously coated with the MS2 protein fused to the maltose binding protein. This enables the specific capture of MS2-sRNA and interacting RNAs. After elution, co-purified RNAs are identified by high-throughput RNA sequencing and subsequent bioinformatic analysis. The following protocol has been implemented in the Gram-positive human pathogen Staphylococcus aureus and is, in principle, transposable to any Gram-positive bacteria. To sum up, MAPS technology constitutes an efficient method to deeply explore the regulatory network of a particular sRNA, offering a snapshot of its whole targetome. However, it is important to keep in mind that putative targets identified by MAPS still need to be validated by complementary experimental approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".