Analysis of non-coding RNAs in <i>Methylorubrum extorquens</i> reveals a novel small RNA specific to Methylobacteriaceae
Bibliographic record
Abstract
Methylorubrum extorquens metabolizes methanol, a cheap raw material that can be derived from waste. It is a facultative methylotroph, making it a model organism to study the metabolism of one carbon compounds. Despite a considerable interest to exploit this bacteria as a biotechnological tool in a methanol-based bioeconomy, little is known about its non-coding sRNA. Small RNAs play well-documented essential roles in Escherichia coli for post-transcriptional regulation; and have important functions in many bacteria, including other Alphaproteobacteria like Agrobacterium tumefaciens. M. extorquens is expected to contain many sRNAs, especially since it also encodes for the protein Hfq, a chaperone protein important in the interaction between sRNAs and their target, but also critical for the stabilization of sRNAs themselves. Few sRNAs are annotated in the genome of this Alphaproteobacteria and they were never validated. In this study, formerly annotated sRNAs ffh, CC2171, BjrC1505 were confirmed by Northern blot, validating the expression of sRNAs in M. extorquens . Moreover, analysis of RNA-sequencing data established a considerable list of potential sRNAs. Interesting candidates selected after bioinformatic analysis were tested by Northern blot, revealing a novel sRNA specific to Methylobacteriaceae, sRNA Met2624. Its expression patterns and genomic context were analyzed. This research is the first experimental validation of sRNAs in M. extorquens and paves the way for other sRNA discoveries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".