Small regulatory RNAs in Archaea
Bibliographic record
Abstract
Small regulatory RNAs (sRNAs) are universally distributed in all three domains of life, Archaea, Bacteria, and Eukaryotes. In bacteria, sRNAs typically function by binding near the translation start site of their target mRNAs and thereby inhibit or activate translation. In eukaryotes, miRNAs and siRNAs typically bind to the 3'-untranslated region (3'-UTR) of their target mRNAs and influence translation efficiency and/or mRNA stability. In archaea, sRNAs have been identified in all species investigated using bioinformatic approaches, RNomics, and RNA-Seq. Their size can vary significantly between less than 50 to more than 500 nucleotides. Differential expression of sRNA genes has been studied using northern blot analysis, microarrays, and RNA-Seq. In addition, biological functions have been unraveled by genetic approaches, i.e., by characterization of designed mutants. As in bacteria, it was revealed that archaeal sRNAs are involved in many biological processes, including metabolic regulation, adaptation to extreme conditions, stress responses, and even in regulation of morphology and cellular behavior. Recently, the first target mRNAs were identified in archaea, including one sRNA that binds to the 5'-region of two mRNAs in Methanosarcina mazei Gö1 and a few sRNAs that bind to 3'-UTRs in Sulfolobus solfataricus, three Pyrobaculum species, and Haloferax volcanii, indicating that archaeal sRNAs appear to be able to target both the 5'-UTR or the 3'-UTRs of their respective target mRNAs. In addition, archaea contain tRNA-derived fragments (tRFs), and one tRF has been identified as a major ribosome-binding sRNA in H. volcanii, which downregulates translation in response to stress. Besides regulatory sRNAs, archaea contain further classes of sRNAs, e.g., CRISPR RNAs (crRNAs) and snoRNAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".