Unique Shine–Dalgarno Sequences in Cyanobacteria and Chloroplasts Reveal Evolutionary Differences in Their Translation Initiation
Bibliographic record
Abstract
Microorganisms require efficient translation to grow and replicate rapidly, and translation is often rate-limited by initiation. A prominent feature that facilitates translation initiation in bacteria is the Shine-Dalgarno (SD) sequence. However, there is much debate over its conservation in Cyanobacteria and in chloroplasts which presumably originated from endosymbiosis of ancient Cyanobacteria. Elucidating the utilization of SD sequences in Cyanobacteria and in chloroplasts is therefore important to understand whether 1) SD role in Cyanobacterial translation has been reduced prior to chloroplast endosymbiosis or 2) translation in Cyanobacteria and in plastid has been subjected to different evolutionary pressures. To test these alternatives, we employed genomic, proteomic, and transcriptomic data to trace differences in SD usage among Synechocystis species, Microcystis aeruginosa, cyanophages, Nicotiana tabacum chloroplast, and Arabidopsis thaliana chloroplast. We corrected their mis-annotated 16S rRNA 3' terminus using an RNA-Seq-based approach to determine their SD/anti-SD locational constraints using an improved measurement DtoStart. We found that cyanophages well-mimic Cyanobacteria in SD usage because both have been under the same selection pressure for SD-mediated initiation. Whereas chloroplasts lost this similarity because the need for SD-facilitated initiation has been reduced in plastids having much reduced genome size and different ribosomal proteins as a result of host-symbiont coevolution. Consequently, SD sequence significantly increases protein expression in Cyanobacteria but not in chloroplasts, and only Cyanobacterial genes compensate for a lack of SD sequence by having weaker secondary structures at the 5' UTR. Our results suggest different evolutionary pressures operate on translation initiation in Cyanobacteria and in chloroplast.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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".